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Essential Skills to Become a Data Scientist

The demand for Data Science professionals is now at an all-time high. There are companies in virtually every industry looking to extract the most value from the heaps of information generated on a daily basis.With the trend for Data Science catching up like never before, organizations are making complete use of their internal data assets to further examine the integration of hundreds of third-party data sources. What is crucial here is the role of the data scientists.Not very long back, the teams playing the key role of working on the data always found their places in the back rooms of multifold IT organizations. The teams though sitting on the backseat would help in steering the various corporate systems with the required data that acted as the fuel to keep the activities running. The critical database tasks performed by the teams responsible allowed corporate executives to report on operations activities and deliver financial results.When you take up a career in Data Science, your previous experience or skills do not matter. As a matter of fact, you would need a whole new range of skills to pursue a career in Data Science. Below are the skills required to become a top dog in Data Science.What should Data Scientists knowData scientists are expected to have knowledge and expertise in the following domains:The areas arch over dozens of languages, frameworks, and technologies that data scientists need to learn. Data scientists should always have the curiosity to amass more knowledge in their domain so that they stay relevant in this dynamic field.The world of Data Science demands certain important attributes and skills, according to IT leaders, industry analysts, data scientists, and others.How to become a Data Scientist?A majority of Data scientists already have a Master’s degree. If Master’s degree does not quench their thirst for more degrees, some even go on to acquire PhD degrees. Mind you, there are exceptions too. It isn’t mandatory that you should be an expert in a particular subject to become a Data Scientist. You could become one even with a qualification in Computer Science, Physical Sciences, Natural Sciences, Statistics or even Social Sciences. However, a degree in Mathematics and Statistics is always an added benefit for enhanced understanding of the concepts.Qualifying with a degree is not the end of the requirements. Brush up your skills by taking online lessons in a special skill set of your choice — get certified on how to use Hadoop, Big Data or R. You can also choose to enroll yourself for a Postgraduate degree in the field of Data Science, Mathematics or any other related field.Remember, learning does not end with earning a degree or certification. You need to practice what you learned — blog and share your knowledge, build an app and explore other avenues and applications of data.The Data Scientists of the modern world have a major role to play in businesses across the globe. They have the ability to extract useful insights from vast amounts of raw data using sophisticated techniques. The business acumen of the Data Scientists help a big deal in predicting what lies ahead for enterprises. The models that the Data Scientists create also bring out measures to mitigate potential threats if any.Take up organizational challenges with ABCDE skillsetAs a Data Scientist, you may have to face challenges while working on projects and finding solutions to problems.A = AnalyticsIf you are a Data Scientist, you are expected not just to study the data and identify the right tools and techniques; you need to have your answers ready to all the questions that come across while you are strategizing on working on a solution with or without a business model.B = Business AcumenOrganizations vouch for candidates with strong business acumen. As a Data Scientist, you are expected to showcase your skills in a way that will make the organization stand one step ahead of the competition. Undertaking a project and working on it is not the end of the path scaled by you. You need to understand and be able to make others understand how your business models influence business outcomes and how the outcomes will prove beneficial to the organization.C = CodingAnd a Data Scientist is expected to be adept at coding too. You may encounter technical issues where you need to sit and work on codes. If you know how to code, it will make you further versatile in confidently assisting your team.D = DomainThe world does not expect Data Scientists to be perfect with knowledge of all domains. However, it is always assumed that a Data Scientist has know-how of various industrial operations. Reading helps as a plus point. You can gain knowledge in various domains by reading the resources online.E = ExplainTo be a successful Data Scientist, you should be able to explain the problem you are faced with to figure out a solution to the problem and share it with the relevant stakeholders. You need to create a difference in the way you explain without leaving any communication gaps.The Important Skills for a Data ScientistLet us now understand the important skills to become an expert Data Scientist – all the skills that go in, to become one. The skills are as follows:Critical thinkingCodingMathML, DL, AICommunication1. Critical thinkingData scientists need to keep their brains racing with critical thinking. They should be able to apply the objective analysis of facts when faced with a complex problem. Upon reaching a logical analysis, a data scientist should formulate opinions or render judgments.Data scientists are counted upon for their understanding of complex business problems and the risks involved with decision-making. Before they plunge into the process of analysis and decision-making, data scientists are required to come up with a 'model' or 'abstract' on what is critical to coming up with the solution to a problem. Data scientists should be able to determine the factors that are extraneous and can be ignored while churning out a solution to a complex business problem.According to Jeffry Nimeroff, CIO at Zeta Global, which provides a cloud-based marketing platform – A data scientist needs to have experience but also have the ability to suspend belief...Before arriving at a solution, it is very important for a Data Scientist to be very clear on what is being expected and if the expected solution can be arrived at. It is only with experience that your intuition works stronger. Experience brings in benefits.If you are a novice and a problem is posed in front of you; all that the one who put the problem in front of you would get is a wide-eyed expression, perhaps. Instead, if you have hands-on experience of working with complex problems no matter what, you will step back, look behind at your experience, draw some inference from multiple points of view and try assessing the problem that is put forth.In simple steps, critical thinking involves the following steps:a. Describe the problem posed in front of you.b. Analyse the arguments involved – The IFs and BUTs.c. Evaluate the significance of the decisions being made and the successes or failures thereafter.2. CodingHandling a complex task might at times call for the execution of a chain of programming tasks. So, if you are a data scientist, you should know how to go about writing code. It does not stop at just writing the code; the code should be executable and should be crucial in helping you find a solution to a complex business problem.In the present scenario, Data Scientists are more inclined towards learning and becoming an expert with Python as the language of choice. There is a substantial crowd following R as well. Scala, Clojure, Java and Octave are a few other languages that find prominence too.Consider the following aspects to be a successful Data Scientist that can dab with programming skills –a) You need to deal with humongous volumes of data.b) Working with real-time data should be like a cakewalk for you.c) You need to hop around cloud computing and work your way with statistical models like the ones shown below:Different Statistical ModelsRegressionOptimizationClusteringDecision treesRandom forestsData scientists are expected to understand and have the ability to code in a bundle of languages – Python, C++ or Java.Gaining the knack to code helps Data Scientists; however, this is not the end requirement. A Data Scientist can always be surrounded by people who code.3. MathIf you have never liked Mathematics as a subject or are not proficient in Mathematics, Data Science is probably not the right career choice for you.You might own an organization or you might even be representing it; the fact is while you engage with your clients, you might have to look into many disparate issues. To deal with the issues that lay in front of you, you will be required to develop complex financial or operational models. To finally be able to build a worthy model, you will end up pulling chunks from large volumes of data. This is where Mathematics helps you.If you have the expertise in Mathematics, building statistical models is easier. Statistical models further help in developing or switching over to key business strategies. With skills in both Mathematics and Statistics, you can get moving in the world of Data Science. Spell the mantra of Mathematics and Statistics onto your lamp of Data Science, lo and behold you can be the genie giving way to the best solutions to the most complex problems.4. Machine learning, Deep Learning, AIData Science overlaps with the fields of Machine Learning, Deep Learning and AI.There is an increase in the way we work with computers, we now have enhanced connectivity; a large amount of data is being collected and industries make use of this data and are moving extremely fast.AI and deep learning may not show up in the requirements of job postings; yet, if you have AI and deep learning skills, you end up eating the big pie.A data scientist needs to be hawk-eyed and alert to the changes in the curve while research is in progress to come up with the best methodology to a problem. Coming up with a model might not be the end. A Data Scientist must be clear as to when to apply which practice to solve a problem without making it more complex.Data scientists need to understand the depth of problems before finding solutions. A data scientist need not go elsewhere to study the problems; all that is there in the data fetched is what is needed to bring out the best solution.A data scientist should be aware of the computational costs involved in building an environment and the following system boundary conditions:a. Interpretabilityb. Latencyc. BandwidthStudying a customer can act as a major plus point for both a data scientist and an organization… This helps in understanding what technology to apply.No matter how generations advance with the use of automated tools and open source is readily available, statistical skills are considered the much-needed add-ons for a data scientist.Understanding statistics is not an easy job; a data scientist needs to be competent to comprehend the assumptions made by the various tools and software.Experts have put forth a few important requisites for data scientists to make the best use of their models:Data scientists need to be handy with proper data interpretation techniques and ought to understand –a. the various functional interfaces to the machine learning algorithmsb. the statistics within the methodsIf you are a data scientist, try dabbing your profile with colours of computer science skills. You must be proficient in working with the keyboard and have a sound knowledge of fundamentals in software engineering.5. CommunicationCommunication and technology show a cycle of operations wherein, there is an integration between people, applications, systems, and data. Data science does not stand separate in this. Working with Data Science is no different. As a Data Scientist, you should be able to communicate with various stakeholders. Data plays a key attribute in the wheel of communication.Communication in Data Science ropes in the ‘storytelling’ ability. This helps you translate a solution you have arrived at into action or intervention that you have put in the pipeline. As a Data Scientist, you should be adept at knitting with the data you have extracted and communicated it clearly to your stakeholders.What does a data scientist communicate to the stakeholders?The benefits of dataThe technology and the computational costs involved in the process of extracting and making use of the dataThe challenges posed in the form of data quality, privacy, and confidentialityA Data Scientist also needs to keep an eye on the wide horizons for better prospects. The organization can be shown a map highlighting other areas of interest that can prove beneficial.If you are a Data Scientist with different feathers in your cap, one being that of a good communicator, you should be able to change a complex form of technical information to a simple and compact form before you present it to the various stakeholders. The information should highlight the challenges, the details of the data, the criteria for success and the anticipated results.If you want to excel in the field of Data Science, you must have an inquisitive bent of mind. The more you ask questions, the more information you gather, the easier it is to come up with paramount business models.6. Data architectureLet us draw some inference from the construction of a building and the role of an architect. Architects have the most knowledge of how the different blocks of buildings can go together and how the different pillars for a block make a strong support system. Like how architects manage and coordinate the entire construction process, so do the Data Scientists while building business models.A Data Scientist needs to understand all that happens to the data from the inception level to when it becomes a model and further until a decision is made based on the model.Not understanding the data architecture can have a tremendous impact on the assumptions made in the process and the decisions arrived at. If a Data Scientist is not familiar with the data architecture, it may lead to the organization taking wrong decisions leading to unexpected and unfavourable results.A slight change within the architecture might lead to situations getting worse for all the involved stakeholders.7. Risk analysis, process improvement, systems engineeringA Data Scientist with sharp business acumen should have the ability to analyse business risks, suggest improvements if any and facilitate further changes in various business processes. As a Data Scientist, you should understand how systems engineering works.If you want to be a Data Scientist and have sharp risk analysis, process improvement and systems engineering skills, you can set yourself for a smooth sail in this vast sea of Data Science.And, rememberYou will no more be a Data Scientist if you stop following scientific theories… After all, Data Science in itself is a major breakthrough in the field of Science.It is always recommended to analyse all the risks that may confront a business before embarking on a journey of model development. This helps in mitigating risks that an organization may have to encounter later. For a smooth business flow, a Data Scientist should also have the nature to probe into the strategies of the various stakeholders and the problems encountered by customers.A Data Scientist should be able to get the picture of the prevailing risks or the various systems that can have a whopping impact on the data or if a model can lead to positive fruition in the form of customer satisfaction.8. Problem-solving and strong business acumenData scientists are not very different when compared to the commoners. We can say this on the lines of problem-solving. The problem solving traits are inherent in every human being. What makes a data scientist stand apart is very good problem-solving skills. We come across complex problems even in everyday situations. How we differ in solving problems is in the perspectives that we apply. Understanding and analyzing before moving on to actually solving the problems by pulling out all the tools in practice is what Data Scientists are good at.The approach that a Data Scientist takes to solve a problem reaps more success than failure. With their approach, they bring critical thinking to the forefront.  Finding a Data Scientist with skill sets at variance is a problem faced by most of the employers.Technical Skills for a Data ScientistWhen the employers are on a hunt to trap the best, they look out for specialization in languages, libraries, and expertise in tech tools. If a candidate comes in with experience, it helps in boosting the profile.Let us see some very important technical skills:PythonRSQLHadoop/Apache SparkJava/SASTableauLet us briefly understand how these languages are in demand.PythonPython is one of the most in-demand languages. This has gained immense popularity as an open-source language. It is widely used both by beginners and experts. Data Scientists need to have Python as one of the primary languages in their kit.RR is altogether a new programming language for statisticians. Anyone with a mathematical bent of mind can learn it. Nevertheless, if you do not appreciate the nuances of Mathematics then it’s difficult to understand R. This never means that you cannot learn it, but without having that mathematical creativity, you cannot harness the power of it.SQLStructured Query Language or SQL is also highly in demand. The language helps in interacting with relational databases. Though it is not of much prominence yet, with a know-how in SQL you can gain a stand in the job market.Hadoop & SparkBoth Hadoop and Spark are open source tools from Apache for big data.Apache Hadoop is an open source software platform. Apache Hadoop helps when you have large data sets on computer clusters built from commodity hardware and you find it difficult to store and process the data sets.Apache Spark is a lightning-fast cluster computing and data processing engine designed for fast computation. It comes with a bunch of development APIs. It supports data workers with efficient execution of streaming, machine learning or SQL workloads.Java & SASWe also have Java and SAS joining the league of languages. These are in-demand languages by large players. Employers offer whopping packages to candidates with expertise in Java and SAS.TableauTableau joins the list as an analytics platform and visualization tool. The tool is powerful and user-friendly. The public version of the tool is available for free. If you wish to keep your data private, you have to consider the costs involved too.Easy tips for a Data ScientistLet us see the in-demand skill set for a Data Scientist in brief.a. A Data Scientist should have the acumen to handle data processing and go about setting models that will help various business processes.b. A Data Scientist should understand the depth of a business problem and the structure of the data that will be used in the process of solving it.c. A Data Scientist should always be ready with an explanation on how the created business models work; even the minute details count.A majority of the crowd out there is good at Maths, Statistics, Engineering or other related subjects. However, when interviewed, they may not show the required traits and when recruited may fail to shine in their performance levels. Sometimes the recruitment process to hire a Data Scientist gets so tedious that employers end up searching with lanterns even in broad daylight. Further, the graphical representation below shows some smart tips for smart Data Scientists.Smart tips for a Data ScientistWhat employers seek the most from Data Scientists?Let us now throw some light into what employers seek the most from Data Scientists:a. A strong sense of analysisb. Machine learning is at the core of what is sought from Data Scientists.c. A Data Scientist should infer and refer to data that has been in practice and will be in practice.d. Data Scientists are expected to be adept at Machine Learning and create models predicting the performance on the basis of demand.e. And, a big NOD to a combo skill set of statistics, Computer Science and Mathematics.Following screenshot shows the requirements of a topnotch employer from a Data Scientist. The requirements were posted on a jobs’ listing website.Let us do a sneak peek into the same job-listing website and see the skills in demand for a Data Scientist.ExampleRecommendations for a Data ScientistWhat are some general recommendations for Data Scientists in the present scenario? Let us walk you through a few.Exhibit your demonstration skills with data analysis and aim to become learned at Machine Learning.Focus on your communication skills. You would have a tough time in your career if you cannot show what you have and cannot communicate what you know. Experts have recommended reading Made to Stick for far-reaching impact of the ideas that you generate.Gain proficiency in deep learning. You must be familiar with the usage, interest, and popularity of deep learning framework.If you are wearing the hat of a Python expert, you must also have the know-how of common python data science libraries – numpy, pandas, matplotlib, and scikit-learn.ConclusionData Science is all about contributing more data to the technologically advanced world. Make your online presence a worthy one; learn while you earn.Start by browsing through online portals. If you are a professional, make your mark on LinkedIn. Securing a job through LinkedIn is now easier than scouring through job sites.Demonstrate all the skills that you are good at on the social portals you are associated with. Suppose you write an article on LinkedIn, do not refrain from sharing the link to the article on your Facebook account.Most important of all – when faced with a complex situation, understand why and what led to the problem. A deeper understanding of a problem will help you come up with the best model. The more you empathize with a situation, the more will be your success count. And in no time, you can become that extraordinary whiz in Data Science.Wishing you immense success if you happen to choose or have already chosen Data Science as the path for your career.All the best for your career endeavour!

Essential Skills to Become a Data Scientist

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Essential Skills to Become a Data Scientist

The demand for Data Science professionals is now at an all-time high. There are companies in virtually every industry looking to extract the most value from the heaps of information generated on a daily basis.

With the trend for Data Science catching up like never before, organizations are making complete use of their internal data assets to further examine the integration of hundreds of third-party data sources. What is crucial here is the role of the data scientists.

Not very long back, the teams playing the key role of working on the data always found their places in the back rooms of multifold IT organizations. The teams though sitting on the backseat would help in steering the various corporate systems with the required data that acted as the fuel to keep the activities running. The critical database tasks performed by the teams responsible allowed corporate executives to report on operations activities and deliver financial results.

When you take up a career in Data Science, your previous experience or skills do not matter. As a matter of fact, you would need a whole new range of skills to pursue a career in Data Science. Below are the skills required to become a top dog in Data Science.

What should Data Scientists know

what should data scientists know

Data scientists are expected to have knowledge and expertise in the following domains:

The areas arch over dozens of languages, frameworks, and technologies that data scientists need to learn. Data scientists should always have the curiosity to amass more knowledge in their domain so that they stay relevant in this dynamic field.

The world of Data Science demands certain important attributes and skills, according to IT leaders, industry analysts, data scientists, and others.

How to become a Data Scientist?

A majority of Data scientists already have a Master’s degree. If Master’s degree does not quench their thirst for more degrees, some even go on to acquire PhD degrees. Mind you, there are exceptions too. It isn’t mandatory that you should be an expert in a particular subject to become a Data Scientist. You could become one even with a qualification in Computer Science, Physical Sciences, Natural Sciences, Statistics or even Social Sciences. However, a degree in Mathematics and Statistics is always an added benefit for enhanced understanding of the concepts.

Qualifying with a degree is not the end of the requirements. Brush up your skills by taking online lessons in a special skill set of your choice — get certified on how to use Hadoop, Big Data or R. You can also choose to enroll yourself for a Postgraduate degree in the field of Data Science, Mathematics or any other related field.

Remember, learning does not end with earning a degree or certification. You need to practice what you learned — blog and share your knowledge, build an app and explore other avenues and applications of data.

The Data Scientists of the modern world have a major role to play in businesses across the globe. They have the ability to extract useful insights from vast amounts of raw data using sophisticated techniques. The business acumen of the Data Scientists help a big deal in predicting what lies ahead for enterprises. The models that the Data Scientists create also bring out measures to mitigate potential threats if any.

Take up organizational challenges with ABCDE skillset

As a Data Scientist, you may have to face challenges while working on projects and finding solutions to problems.

ABCDE skillset to overcome Organizational Challenges:- Analytics, Business Acumen, Coding, Domain, Explain

A = Analytics

If you are a Data Scientist, you are expected not just to study the data and identify the right tools and techniques; you need to have your answers ready to all the questions that come across while you are strategizing on working on a solution with or without a business model.

B = Business Acumen

Organizations vouch for candidates with strong business acumen. As a Data Scientist, you are expected to showcase your skills in a way that will make the organization stand one step ahead of the competition. Undertaking a project and working on it is not the end of the path scaled by you. You need to understand and be able to make others understand how your business models influence business outcomes and how the outcomes will prove beneficial to the organization.

C = Coding

And a Data Scientist is expected to be adept at coding too. You may encounter technical issues where you need to sit and work on codes. If you know how to code, it will make you further versatile in confidently assisting your team.

D = Domain

The world does not expect Data Scientists to be perfect with knowledge of all domains. However, it is always assumed that a Data Scientist has know-how of various industrial operations. Reading helps as a plus point. You can gain knowledge in various domains by reading the resources online.

E = Explain

To be a successful Data Scientist, you should be able to explain the problem you are faced with to figure out a solution to the problem and share it with the relevant stakeholders. You need to create a difference in the way you explain without leaving any communication gaps.

The Important Skills for a Data Scientist

Let us now understand the important skills to become an expert Data Scientist – all the skills that go in, to become one. The skills are as follows:

The Important Skills for a Data Scientist

  1. Critical thinking
  2. Coding
  3. Math
  4. ML, DL, AI
  5. Communication

Essential Skills to Become a Data Scientist

1. Critical thinking

Data scientists need to keep their brains racing with critical thinking. They should be able to apply the objective analysis of facts when faced with a complex problem. Upon reaching a logical analysis, a data scientist should formulate opinions or render judgments.

Data scientists are counted upon for their understanding of complex business problems and the risks involved with decision-making. Before they plunge into the process of analysis and decision-making, data scientists are required to come up with a 'model' or 'abstract' on what is critical to coming up with the solution to a problem. Data scientists should be able to determine the factors that are extraneous and can be ignored while churning out a solution to a complex business problem.

According to Jeffry Nimeroff, CIO at Zeta Global, which provides a cloud-based marketing platform – A data scientist needs to have experience but also have the ability to suspend belief...

Before arriving at a solution, it is very important for a Data Scientist to be very clear on what is being expected and if the expected solution can be arrived at. It is only with experience that your intuition works stronger. Experience brings in benefits.

If you are a novice and a problem is posed in front of you; all that the one who put the problem in front of you would get is a wide-eyed expression, perhaps. Instead, if you have hands-on experience of working with complex problems no matter what, you will step back, look behind at your experience, draw some inference from multiple points of view and try assessing the problem that is put forth.

In simple steps, critical thinking involves the following steps:

Steps in critical thinking

a. Describe the problem posed in front of you.

b. Analyse the arguments involved – The IFs and BUTs.

c. Evaluate the significance of the decisions being made and the successes or failures thereafter.

2. Coding

Handling a complex task might at times call for the execution of a chain of programming tasks. So, if you are a data scientist, you should know how to go about writing code. It does not stop at just writing the code; the code should be executable and should be crucial in helping you find a solution to a complex business problem.

In the present scenario, Data Scientists are more inclined towards learning and becoming an expert with Python as the language of choice. There is a substantial crowd following R as well. Scala, Clojure, Java and Octave are a few other languages that find prominence too.

Consider the following aspects to be a successful Data Scientist that can dab with programming skills –

a) You need to deal with humongous volumes of data.

b) Working with real-time data should be like a cakewalk for you.

c) You need to hop around cloud computing and work your way with statistical models like the ones shown below:

Different Statistical Models

  • Regression
  • Optimization
  • Clustering
  • Decision trees
  • Random forests

Data scientists are expected to understand and have the ability to code in a bundle of languages – Python, C++ or Java.

Gaining the knack to code helps Data Scientists; however, this is not the end requirement. A Data Scientist can always be surrounded by people who code.

3. Math

If you have never liked Mathematics as a subject or are not proficient in Mathematics, Data Science is probably not the right career choice for you.

You might own an organization or you might even be representing it; the fact is while you engage with your clients, you might have to look into many disparate issues. To deal with the issues that lay in front of you, you will be required to develop complex financial or operational models. To finally be able to build a worthy model, you will end up pulling chunks from large volumes of data. This is where Mathematics helps you.

If you have the expertise in Mathematics, building statistical models is easier. Statistical models further help in developing or switching over to key business strategies. With skills in both Mathematics and Statistics, you can get moving in the world of Data Science. Spell the mantra of Mathematics and Statistics onto your lamp of Data Science, lo and behold you can be the genie giving way to the best solutions to the most complex problems.

4. Machine learning, Deep Learning, AI

Data Science overlaps with the fields of Machine Learning, Deep Learning and AI.

There is an increase in the way we work with computers, we now have enhanced connectivity; a large amount of data is being collected and industries make use of this data and are moving extremely fast.

AI and deep learning may not show up in the requirements of job postings; yet, if you have AI and deep learning skills, you end up eating the big pie.

A data scientist needs to be hawk-eyed and alert to the changes in the curve while research is in progress to come up with the best methodology to a problem. Coming up with a model might not be the end. A Data Scientist must be clear as to when to apply which practice to solve a problem without making it more complex.

Data scientists need to understand the depth of problems before finding solutions. A data scientist need not go elsewhere to study the problems; all that is there in the data fetched is what is needed to bring out the best solution.

A data scientist should be aware of the computational costs involved in building an environment and the following system boundary conditions:

a. Interpretability

b. Latency

c. Bandwidth

Studying a customer can act as a major plus point for both a data scientist and an organization… This helps in understanding what technology to apply.

No matter how generations advance with the use of automated tools and open source is readily available, statistical skills are considered the much-needed add-ons for a data scientist.

Understanding statistics is not an easy job; a data scientist needs to be competent to comprehend the assumptions made by the various tools and software.

Experts have put forth a few important requisites for data scientists to make the best use of their models:

Data scientists need to be handy with proper data interpretation techniques and ought to understand –

a. the various functional interfaces to the machine learning algorithms

b. the statistics within the methods

If you are a data scientist, try dabbing your profile with colours of computer science skills. You must be proficient in working with the keyboard and have a sound knowledge of fundamentals in software engineering.

5. Communication

Communication and technology show a cycle of operations wherein, there is an integration between people, applications, systems, and data. Data science does not stand separate in this. Working with Data Science is no different. As a Data Scientist, you should be able to communicate with various stakeholders. Data plays a key attribute in the wheel of communication.

Communication in Data Science ropes in the ‘storytelling’ ability. This helps you translate a solution you have arrived at into action or intervention that you have put in the pipeline. As a Data Scientist, you should be adept at knitting with the data you have extracted and communicated it clearly to your stakeholders.

What does a data scientist communicate to the stakeholders?

What does a data scientist communicate to the stakeholders

The benefits of data

The technology and the computational costs involved in the process of extracting and making use of the data

The challenges posed in the form of data quality, privacy, and confidentiality

A Data Scientist also needs to keep an eye on the wide horizons for better prospects. The organization can be shown a map highlighting other areas of interest that can prove beneficial.

If you are a Data Scientist with different feathers in your cap, one being that of a good communicator, you should be able to change a complex form of technical information to a simple and compact form before you present it to the various stakeholders. The information should highlight the challenges, the details of the data, the criteria for success and the anticipated results.

If you want to excel in the field of Data Science, you must have an inquisitive bent of mind. The more you ask questions, the more information you gather, the easier it is to come up with paramount business models.

6. Data architecture

Let us draw some inference from the construction of a building and the role of an architect. Architects have the most knowledge of how the different blocks of buildings can go together and how the different pillars for a block make a strong support system. Like how architects manage and coordinate the entire construction process, so do the Data Scientists while building business models.

A Data Scientist needs to understand all that happens to the data from the inception level to when it becomes a model and further until a decision is made based on the model.

Not understanding the data architecture can have a tremendous impact on the assumptions made in the process and the decisions arrived at. If a Data Scientist is not familiar with the data architecture, it may lead to the organization taking wrong decisions leading to unexpected and unfavourable results.

A slight change within the architecture might lead to situations getting worse for all the involved stakeholders.

7. Risk analysis, process improvement, systems engineering

A Data Scientist with sharp business acumen should have the ability to analyse business risks, suggest improvements if any and facilitate further changes in various business processes. As a Data Scientist, you should understand how systems engineering works.

If you want to be a Data Scientist and have sharp risk analysis, process improvement and systems engineering skills, you can set yourself for a smooth sail in this vast sea of Data Science.

And, remember

You will no more be a Data Scientist if you stop following scientific theories… After all, Data Science in itself is a major breakthrough in the field of Science.

It is always recommended to analyse all the risks that may confront a business before embarking on a journey of model development. This helps in mitigating risks that an organization may have to encounter later. For a smooth business flow, a Data Scientist should also have the nature to probe into the strategies of the various stakeholders and the problems encountered by customers.

A Data Scientist should be able to get the picture of the prevailing risks or the various systems that can have a whopping impact on the data or if a model can lead to positive fruition in the form of customer satisfaction.

8. Problem-solving and strong business acumen

Data scientists are not very different when compared to the commoners. We can say this on the lines of problem-solving. The problem solving traits are inherent in every human being. What makes a data scientist stand apart is very good problem-solving skills. We come across complex problems even in everyday situations. How we differ in solving problems is in the perspectives that we apply. Understanding and analyzing before moving on to actually solving the problems by pulling out all the tools in practice is what Data Scientists are good at.

The approach that a Data Scientist takes to solve a problem reaps more success than failure. With their approach, they bring critical thinking to the forefront.  

Finding a Data Scientist with skill sets at variance is a problem faced by most of the employers.

Technical Skills for a Data ScientistTechnical or Programming Skills required to become a Data Scientist

When the employers are on a hunt to trap the best, they look out for specialization in languages, libraries, and expertise in tech tools. If a candidate comes in with experience, it helps in boosting the profile.

Let us see some very important technical skills:

  • Python
  • R
  • SQL
  • Hadoop/Apache Spark
  • Java/SAS
  • Tableau

Let us briefly understand how these languages are in demand.

  • Python

Python is one of the most in-demand languages. This has gained immense popularity as an open-source language. It is widely used both by beginners and experts. Data Scientists need to have Python as one of the primary languages in their kit.

  • R

R is altogether a new programming language for statisticians. Anyone with a mathematical bent of mind can learn it. Nevertheless, if you do not appreciate the nuances of Mathematics then it’s difficult to understand R. This never means that you cannot learn it, but without having that mathematical creativity, you cannot harness the power of it.

  • SQL

Structured Query Language or SQL is also highly in demand. The language helps in interacting with relational databases. Though it is not of much prominence yet, with a know-how in SQL you can gain a stand in the job market.

  • Hadoop & Spark

Both Hadoop and Spark are open source tools from Apache for big data.

Apache Hadoop is an open source software platform. Apache Hadoop helps when you have large data sets on computer clusters built from commodity hardware and you find it difficult to store and process the data sets.

Apache Spark is a lightning-fast cluster computing and data processing engine designed for fast computation. It comes with a bunch of development APIs. It supports data workers with efficient execution of streaming, machine learning or SQL workloads.

  • Java & SAS

We also have Java and SAS joining the league of languages. These are in-demand languages by large players. Employers offer whopping packages to candidates with expertise in Java and SAS.

  • Tableau

Tableau joins the list as an analytics platform and visualization tool. The tool is powerful and user-friendly. The public version of the tool is available for free. If you wish to keep your data private, you have to consider the costs involved too.

Easy tips for a Data Scientist

Let us see the in-demand skill set for a Data Scientist in brief.

a. A Data Scientist should have the acumen to handle data processing and go about setting models that will help various business processes.

b. A Data Scientist should understand the depth of a business problem and the structure of the data that will be used in the process of solving it.

c. A Data Scientist should always be ready with an explanation on how the created business models work; even the minute details count.

A majority of the crowd out there is good at Maths, Statistics, Engineering or other related subjects. However, when interviewed, they may not show the required traits and when recruited may fail to shine in their performance levels. Sometimes the recruitment process to hire a Data Scientist gets so tedious that employers end up searching with lanterns even in broad daylight. Further, the graphical representation below shows some smart tips for smart Data Scientists.

Smart tips for a Data Scientist

Smart tips for a Data Scientist

What employers seek the most from Data Scientists?

Let us now throw some light into what employers seek the most from Data Scientists:

a. A strong sense of analysis

b. Machine learning is at the core of what is sought from Data Scientists.

c. A Data Scientist should infer and refer to data that has been in practice and will be in practice.

d. Data Scientists are expected to be adept at Machine Learning and create models predicting the performance on the basis of demand.

e. And, a big NOD to a combo skill set of statistics, Computer Science and Mathematics.

Following screenshot shows the requirements of a topnotch employer from a Data Scientist. The requirements were posted on a jobs’ listing website.

Let us do a sneak peek into the same job-listing website and see the skills in demand for a Data Scientist.

  1. Example

Essential Skills to become a Data Scientist

Recommendations for a Data Scientist

What are some general recommendations for Data Scientists in the present scenario? Let us walk you through a few.

  • Exhibit your demonstration skills with data analysis and aim to become learned at Machine Learning.
  • Focus on your communication skills. You would have a tough time in your career if you cannot show what you have and cannot communicate what you know. Experts have recommended reading Made to Stick for far-reaching impact of the ideas that you generate.
  • Gain proficiency in deep learning. You must be familiar with the usage, interest, and popularity of deep learning framework.

If you are wearing the hat of a Python expert, you must also have the know-how of common python data science libraries – numpy, pandas, matplotlib, and scikit-learn.

Conclusion

Data Science is all about contributing more data to the technologically advanced world. Make your online presence a worthy one; learn while you earn.

Start by browsing through online portals. If you are a professional, make your mark on LinkedIn. Securing a job through LinkedIn is now easier than scouring through job sites.

Demonstrate all the skills that you are good at on the social portals you are associated with. Suppose you write an article on LinkedIn, do not refrain from sharing the link to the article on your Facebook account.

Most important of all – when faced with a complex situation, understand why and what led to the problem. A deeper understanding of a problem will help you come up with the best model. The more you empathize with a situation, the more will be your success count. And in no time, you can become that extraordinary whiz in Data Science.

Wishing you immense success if you happen to choose or have already chosen Data Science as the path for your career.

All the best for your career endeavour!

Priyankur

Priyankur Sarkar

Data Science Enthusiast

Priyankur Sarkar loves to play with data and get insightful results out of it, then turn those data insights and results in business growth. He is an electronics engineer with a versatile experience as an individual contributor and leading teams, and has actively worked towards building Machine Learning capabilities for organizations.

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2 comments

Juhi 18 Jun 2019

It’s really great information. Keep sharing, Thanks

nikhil 31 Aug 2020

Great article,very informative.Thanks for sharing your thought process! looking forward to subscribe to your news letter. Thanks for such an interesting and wonderful blog.It is really a nice and informative blog and the content is really precise. looking forward to subscribe to your news letter. ExcelR- Data Science, Data Analytics, Business Analytics Course Trai

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What Is Data Science(with Examples), It's Lifecycle and Who exactly is a Data Scientist

Oh yes, Science is everywhere. A while ago, when children embarked on the journey of learning everyday science in school, the statement that always had a mention was “Science is everywhere”. The situation is more or less the same even in present times. Science has now added a few feathers to its cap. Yes, the general masses sing the mantra “Data Science” is everywhere. What does it mean when I say Data Science is everywhere? Let us take a look at the Science of Data. What are those aspects that make this Science unique from everyday Science?The Big Data Age as you may call it has in it Data as the object of study.Data Science for a person who has set up a firm could be a money spinnerData Science for an architect working at an IT consulting company could be a bread earnerData Science could be the knack behind the answers that come out from the juggler’s hatData Science could be a machine imported from the future, which deals with the Math and Statistics involved in your lifeData science is a platter full of data inference, algorithm development, and technology. This helps the users find recipes to solve analytically complex problems.With data as the core, we have raw information that streams in and is stored in enterprise data warehouses acting as the condiments to your complex problems. To extract the best from the data generated, Data Science calls upon Data Mining. At the end of the tunnel, Data Science is about unleashing different ways to use data and generate value for various organizations.Let us dig deeper into the tunnel and see how various domains make use of Data Science.Example 1Think of a day without Data Science, Google would not have generated results the way it does today.Example 2Suppose you manage an eatery that churns out the best for different taste buds. To model a product in the pipeline, you are keen on knowing what the requirements of your customers are. Now, you know they like more cheese on the pizza than jalapeno toppings. That is the existing data that you have along with their browsing history, purchase history, age and income. Now, add more variety to this existing data. With the vast amount of data that is generated, your strategies to bank upon the customers’ requirements can be more effective. One customer will recommend your product to another outside the circle; this will further bring more business to the organization.Consider this image to understand how an analysis of the customers’ requirements helps:Example 3Data Science plays its role in predictive analytics too.I have an organization that is into building devices that will send a trigger if a natural calamity is soon to occur. Data from ships, aircraft, and satellites can be accumulated and analyzed to build models that will not only help with weather forecasting but also predict the occurrence of natural calamities. The model device that I build will send triggers and save lives too.Consider the image shown below to understand how predictive analytics works:Example 4A lot many of us who are active on social media would have come across this situation while posting images that show you indulging in all fun and frolic with your friends. You might miss tagging your friends in the images you post but the tag suggestion feature available on most platforms will remind you of the tagging that is pending.The automatic tag suggestion feature uses the face recognition algorithm.Lifecycle of Data ScienceCapsulizing the main phases of the Data Science Lifecycle will help us understand how the Data Science process works. The various phases in the Data Science Lifecycle are:DiscoveryData PreparationModel PlanningModel BuildingOperationalizingCommunicating ResultsPhase 1Discovery marks the first phase of the lifecycle. When you set sail with your new endeavor,it is important to catch hold of the various requirements and priorities. The ideation involved in this phase needs to have all the specifications along with an outline of the required budget. You need to have an inquisitive mind to make the assessments – in terms of resources, if you have the required manpower, technology, infrastructure and above all time to support your project. In this phase, you need to have a business problem laid out and build an initial hypotheses (IH) to test your plan. Phase 2Data preparation is done in this phase. An analytical sandbox is used in this to perform analytics for the entire duration of the project. While you explore, preprocess and condition data, modeling follows suit. To get the data into the sandbox, you will perform ETLT (extract, transform, load and transform).We make use of R for data cleaning, transformation, and visualization and further spot the outliers and establish a relationship between the variables. Once the data is prepared after cleaning, you can play your cards with exploratory analytics.Phase 3In this phase of Model planning, you determine the methods and techniques to pick on the relationships between variables. These relationships set the base for the algorithms that will be implemented in the next phase.  Exploratory Data Analytics (EDA) is applied in this phase using various statistical formulas and visualization tools.Subsequently, we will look into the various models that are required to work out with the Data Science process.RR is the most commonly used tool. The tool comes with a complete set of modeling capabilities. This proves a good environment for building interpretive models.SQL Analysis Services SQL Analysis services has the ability to perform in-database analytics using basic predictive models and common data mining functions.SAS/ACCESS  SAS/ACCESS helps you access data from Hadoop. This can be used for creating repeatable and reusable model flow diagrams.You have now got an overview of the nature of your data and have zeroed in on the algorithms to be used. In the next stage, the algorithm is applied to further build up a model.Phase 4This is the Model building phase as you may call it. Here, you will develop datasets for training and testing purposes. You need to understand whether your existing tools will suffice for running the models that you build or if a more robust environment (like fast and parallel processing) is required. The various tools for model building are SAS Enterprise Miner, WEKA, SPCS Modeler, Matlab, Alpine Miner and Statistica.Phase 5In the Operationalize phase, you deliver final reports, briefings, code and technical documents. Moreover, a pilot project may also be implemented in a real-time production environment on a small scale. This helps users get a clear picture of the performance and other related constraints before full deployment.Phase 6The Communicate results phase is the conclusion. Here, we evaluate if you have been able to meet your goal the way you had planned in the initial phase. It is in this phase that the key findings pop their heads out. You communicate to the stakeholders in this phase. This phase brings you the result of your project whether it is a success or a failure.Why Do We Need Data Science?Data Science to be precise is an amalgamation of Infrastructure, Software, Statistics and the various data sources.To really understand big data, it would help us if we bridge back to the historical background. Gartner’s definition circa 2001, which is still the go-to definition says,Big data is data that contains greater variety arriving in increasing volumes and with ever-higher velocity. This is known as the three Vs.When we break the definition into simple terms, all that it means is, big data is humongous. This involves the multiplication of complex data sets with the addition of new data sources. When the data sets are in such high volumes, our traditional data processing software fails to manage them. It is just like how you cannot expect your humble typewriter to do the job of a computer. You cannot expect a typewriter to even do the ctrl c + ctrl v job for you. The amount of data that comes with the solutions to all your business problems is massive. To help you with the processing of this data, you have Data Science playing the key role.The concept of big data itself may sound relatively new; however, the origins of large data sets can be traced back to the 1960s and the '70s. This is when the world of data was just getting started. The world witnessed the set up of the first data centers and the development of the relational database.Around 2005, Facebook, YouTube, and other online services started gaining immense popularity. The more people indulged in the use of these platforms, the more data they generated. The processing of this data involved a lot of Data Science. The masses had to store the amassed data and analyse it at a later point. As a platform that answers to the storage and analysis of the amassed data, Hadoop was developed. Hadoop is an open-source framework that helps in the storage and analysis of big data sets. And as we say, the rest will follow suit; we had NoSQL gaining popularity during this time.With the advent of big data, the need for its storage also grew. The storage of data became a major issue for enterprise industries until 2010. We have had Hadoop, Spark and other frameworks mitigating the challenge to a very large extent. Though the volume of big data is skyrocketing, the focus remains on the processing of the data, all thanks to these efficient frameworks. And, Data Science once again hogs the limelight.Can we say it is only the users leading to huge amounts of data? No, we cannot. It is not only humans generating the data but also the work they indulge in.Delving into the iota of the Internet of Things (IoT) will get us some clarity on the question that we just raised. As we have more objects and devices connected to the Internet, data gathers not just by use but also by the pattern of your usage and the performance of the various products.The Three Vs of Big DataData Science helps in the extraction of knowledge from the accumulated data. While big data has come far with the accumulation of users’ data, its usefulness is only just beginning.Following are the Three Properties that define Big Data:VolumeVelocityVarietyVolumeThe amount of data is a crucial factor here. Big data stands as a pillar when you have to process a multitude of low-density, unstructured data. The data may contain unknown value – such as clickstreams on a webpage or a mobile app and Twitter data feeds. The values of the data may differ from user to user. For some, the value might be in tens of terabytes of data. For others, the value might be in hundreds of petabytes.Consider the different social media platforms – Facebook records 2 billion users, YouTube has 1 billion users, 350 million users for Twitter and a whopping 700 million users on Instagram. There is exchange of billions of images, posts and tweets on these platforms. Imagine the amuck storage of data the users contribute too. Mind Boggling, is it not? This insanely large amount of data is generated every minute and every hour.VelocityThe fast rate at which the data is received and acted upon is the Velocity. Usually, the data is written to the disk. When there is data with highest velocity, it streams directly into the memory. With the advancement in technology, we now have more numbers of Internet-connected devices across industries. The velocity of the data generated through these devices that act real time or near real time may call for real-time evaluation and action.Sticking to our social media example, Facebook accounts for 900 million photo uploads, Twitter handles 500 million tweets, Google is to go to solution for 3.5 billion searches, YouTube calls for 0.4 millions hours of video uploads; all this on a daily basis. The bundled amount of data is stifling.VarietyThe data generated by the users comes in different types. The different types form different varieties of data. Dating back, we had traditional data types that were structured and organized in a relational database.Texts, tweets, videos, photos uploaded form the different varieties of structured data uploaded on the Internet.Voicemails, emails, ECG reading, audio recordings and a lot more form the different varieties of unstructured data that we find on the Internet.Who is a Data Scientist? A curious brain and an impressive training is all that you need to become a Data Scientist. Not as easy as it may sound.Deep thinking, deep learning with intense intellectual curiosity is a common trait found in data scientists. The more you ask questions, the more discoveries you come up with, the more augmented your learning experience is, the more it gets easier for you to tread on the path of Data Science.A factor that differentiates a data scientist from a normal bread earner is that they are more obsessed with creativity and ingenuity. A normal bread earner will go seeking money whereas, the motivator for a data scientist is the ability to solve analytical problems with a pinch of curiosity and creativity. Data scientists are always on a treasure hunt – hunting for the best from the trove.If you think, you need a degree in Sciences or you need to be a PhD in Math to become a legitimate data scientist, mind you, you are carrying a misconception. A natural propensity in these areas will definitely add to your profile but you can be an expert data scientist without a degree in these areas too. Data Science becomes a cinch with heaps of knowledge in programming and business acumen.Data Science is a discipline gaining colossal prominence of late. Educational institutions are yet to come up with comprehensive Data Science degree programs. A data scientist can never claim to have undergone all the required schooling. Learning the rights skills, guided by self-determination is a never-ending process for a data scientist.As Data Science is multidisciplinary, many people find it confusing to differentiate between Data Scientist and Data Analyst.Data Analytics is one of the components of Data Science. Analytics help in understanding the data structure of an organization. The achieved output is further used to solve problems and ring in business insights.The Basic Differences between a Data Scientist and a Data AnalystScientists and Analysts are not exactly synonymous. The roles are not mutually exclusive either. The roles of Data Scientists and Data Analysts differ a lot. Let us take a look at some of the basic differences:CriteriaData ScientistData AnalystGoalInquisitive nature and a strong business acumen helps Data Scientists to arrive at solutionsThey perform data analysis and sourcingTasksData Scientists need to be adept at data insight mining, preparation, and analysis to extract informationData Analysts gather, arrange, process and model both structured and unstructured dataSubstantive expertiseRequiredNot RequiredNon-technical skillsRequiredNot RequiredWhat Skills Are Required To Become a Data Scientist?Data scientists blend with the best skills. The fundamental skills required to become a Data Scientist are as follows:Proficiency in MathematicsTechnology knowhow and the knack to hackBusiness AcumenProficiency in MathematicsA Data Scientist needs to be equipped with a quantitative lens. You can be a Data Scientist if you have the ability to view the data quantitatively.Before a data product is finally built, it calls for a tremendous amount of data insight mining. There are portions of data that include textures, dimensions and correlations. To be able to find solutions to come with an end product, a mathematical perspective always helps.If you have that knack for Math, finding solutions utilizing data becomes a cakewalk laden with heuristics and quantitative techniques. The path to finding solutions to major business problems is a tedious one. It involves the building of analytical models. Data Scientists need to identify the underlying nuts and bolts to successfully build models.Data Science carries with it a misconception that it is all about statistics. Statistics is crucial; however, only the Math type is more accountable. Statistics has two offshoots – the classical and the Bayesian. When people talk about stats, they are usually referring to classical stats. Data Scientists need to refer both types to arrive at solutions. Moreover, there is a mix of inferential techniques and machine learning algorithms; this mix leans on the knowledge of linear algebra. There are popular methods in Data Science; finding a solution using these methods calls upon matrix math which has got very less to do with classical stats.Technology knowhow and the knack to hackOn a lighter note, let us put a disclaimer… you are not being asked to learn hacking to come crashing on computers. As a hacker, you need to be gelled with the amalgam of creativity and ingenuity. You are expected to use the right technical skills to build models and thereby find solutions to complex analytical problems.Why does the world of Data Science vouch on your hacking ability? The answer finds its element in the use of technology by Data Scientists. Mindset, training and the right technology when put together can squeeze out the best from mammoth data sets. Solving complex algorithms requires more sophisticated tools than just Excel. Data scientists need to have the nitty-gritty ability to code. They should be able to prototype quick solutions, as well as integrate with complex data systems. SQL, Python, R, and SAS are the core languages associated with Data Science. A knowhow of Java, Scala, Julia, and other languages also helps. However, the knowledge of language fundamentals does not suffice the quest to extract the best from enormous data sets. A hacker needs to be creative to sail through technical waters and make the codes reach the shore.Business AcumenA strong business acumen is a must-have in the portfolio of any Data Scientist. You need to make tactical moves and fetch that from the data, which no one else can. To be able to translate your observation and make it a shared knowledge calls for a lot of responsibility that can face no fallacy.With the right business acumen, a Data Scientist finds it easy to present a story or the narration of a problem or a solution.To be able to put your ideas and the solutions you arrive at across the table, you need to have business acumen along with the prowess for tech and algorithms.Data, Math, and tech will not help always. You need to have a strong business influence that can further be influenced by a strong business acumen.Companies Using Data ScienceTo address the issues associated with the management of complex and expanding work environments, IT organizations make use of data to identify new value sources. The identification helps them exploit future opportunities and to further expand their operations. What makes the difference here is the knowledge you extract from the repository of data. The biggest and the best companies use analytics to efficiently come up with the best business models.Following are a few top companies that use Data Science to expand their services and increase their productivity.GoogleAmazonProcter & GambleNetflixGoogle Google has always topped the list on a hiring spree for top-notch data scientists. A force of data scientists, artificial intelligence and machine learning by far drives Google. Moreover, when you are here, you get the best when you give the best of your data expertise.AmazonAmazon, the global e-commerce and cloud computing giant hire data scientists on a big scale. To bank upon the customers’ mindsets, enhance the geographical outreach of both the cloud domain and e-commerce domain among other business-driven goals, they make use of Data Science. Data Scientists play a crucial role in steering Data Science.Procter & Gamble and NetflixBig Data is a major component of Data Science.It has answers to a range of business problems – from customer experience to analytics.Netflix and Procter & Gamble join the race of product development by using big data to anticipate customer demand. They make use of predictive analytics, an offshoot of Data Science to build models for services in their pipeline. This modelling is an attribute that contributes to their commercial success. The significant addition to the commercial success of P&G is that it uses data and analytics from test markets, social media, and early store rollouts. Following this strategy, it further plans, produces, and launches the final products. And, the finale often garners an overwhelming response for them.The Final Component of the Big Data StoryWhen speed multiplied with storage capabilities, thus evolved the final component of the Big Data story – the generation and collection of the data. If we still had massive room-sized calculators working as computers, we may not have come across the humongous amount of data that we see today. With the advancement in technology, we called upon ubiquitous devices. With the increase in the number of devices, we have more data being generated. We are generating data at our own pace from our own space owing to the devices that we make use of from our comfort zones. Here I tweet, there you post, while a video is being uploaded on some platform by someone from some corner of the room you are seated in.The more you inform people about what you are doing in your life, the more data you end up writing. I am happy and I share a quote on Facebook expressing my feelings; I am contributing to more data. This is how enormous amount of data is generated. The Internet-connected devices that we use support in writing data. Anything that you engage with in this digital world, the websites you browse, the apps you open on your cell phone, all the data pertaining to these can be logged in a database miles away from you.Writing data and storing it is not an arduous task anymore. At times, companies just push the value of the data to the backburner. At some point of time, this data will be fetched and cooked when they see the need for it.There are different ways to cash upon the billions of data points. Data Science puts the data into categories to get a clear picture. On a Final NoteIf you are an organization looking out to expand your horizons, being data-driven will take you miles. The application of an amalgam of Infrastructure, Software and Statistics, and the various data sources is the secret formula to successfully arrive at key business solutions. The future belongs to Data Science. Today, it is data that we see all around us. This new age sounds the bugle for more opportunities in the field of Data Science. Very soon, the world will need around one million Data Scientists.If you are keen on donning the hat of a Data Scientist, be your own architect when it comes to solving analytical problems. You need to be a highly motivated problem solver to overcome the toughest analytical challenges.Master Data Science with our in-depth online courses. Explore them now!
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What Is Data Science(with Examples), It's Lif...

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Bagging and Random Forest in Machine Learning

In today’s world, innovations happen on a daily basis, rendering all the previous versions of that product, service or skill-set outdated and obsolete. In such a dynamic and chaotic space, how can we make an informed decision without getting carried away by plain hype? To make the right decisions, we must follow a set of processes; investigate the current scenario, chart down your expectations, collect reviews from others, explore your options, select the best solution after weighing the pros and cons, make a decision and take the requisite action. For example, if you are looking to purchase a computer, will you simply walk up to the store and pick any laptop or notebook? It’s highly unlikely that you would do so. You would probably search on Amazon, browse a few web portals where people have posted their reviews and compare different models, checking for their features, specifications and prices. You will also probably ask your friends and colleagues for their opinion. In short, you would not directly jump to a conclusion, but will instead make a decision considering the opinions and reviews of other people as well. Ensemble models in machine learning also operate on a similar manner. They combine the decisions from multiple models to improve the overall performance. The objective of this article is to introduce the concept of ensemble learning and understand algorithms like bagging and random forest which use a similar technique. What is Ensemble Learning? Ensemble methods aim at improving the predictive performance of a given statistical learning or model fitting technique. The general principle of ensemble methods is to construct a linear combination of some model fitting method, instead of using a single fit of the method. An ensemble is itself a supervised learning algorithm, because it can be trained and then used to make predictions. Ensemble methods combine several decision trees classifiers to produce better predictive performance than a single decision tree classifier. The main principle behind the ensemble model is that a group of weak learners come together to form a strong learner, thus increasing the accuracy of the model.When we try to predict the target variable using any machine learning technique, the main causes of difference in actual and predicted values are noise, variance, and bias. Ensemble helps to reduce these factors (except noise, which is irreducible error). The noise-related error is mainly due to noise in the training data and can't be removed. However, the errors due to bias and variance can be reduced.The total error can be expressed as follows: Total Error = Bias + Variance + Irreducible Error A measure such as mean square error (MSE) captures all of these errors for a continuous target variable and can be represented as follows: Where, E stands for the expected mean, Y represents the actual target values and fˆ(x) is the predicted values for the target variable. It can be broken down into its components such as bias, variance and noise as shown in the following formula: Using techniques like Bagging and Boosting helps to decrease the variance and increase the robustness of the model. Combinations of multiple classifiers decrease variance, especially in the case of unstable classifiers, and may produce a more reliable classification than a single classifier. Ensemble Algorithm The goal of ensemble algorithms is to combine the predictions of several base estimators built with a given learning algorithm in order to improve generalizability / robustness over a single estimator. There are two families of ensemble methods which are usually distinguished: Averaging methods. The driving principle is to build several estimators independently and then to average their predictions. On average, the combined estimator is usually better than any of the single base estimator because its variance is reduced.|Examples: Bagging methods, Forests of randomized trees. Boosting methods. Base estimators are built sequentially and one tries to reduce the bias of the combined estimator. The motivation is to combine several weak models to produce a powerful ensemble.Examples: AdaBoost, Gradient Tree Boosting.Advantages of Ensemble Algorithm Ensemble is a proven method for improving the accuracy of the model and works in most of the cases. Ensemble makes the model more robust and stable thus ensuring decent performance on the test cases in most scenarios. You can use ensemble to capture linear and simple as well nonlinear complex relationships in the data. This can be done by using two different models and forming an ensemble of two. Disadvantages of Ensemble Algorithm Ensemble reduces the model interpret-ability and makes it very difficult to draw any crucial business insights at the end It is time-consuming and thus might not be the best idea for real-time applications The selection of models for creating an ensemble is an art which is really hard to master Basic Ensemble Techniques Max Voting: Max-voting is one of the simplest ways of combining predictions from multiple machine learning algorithms. Each base model makes a prediction and votes for each sample. The sample class with the highest votes is considered in the final predictive class. It is mainly used for classification problems.  Averaging: Averaging can be used while estimating the probabilities in classification tasks. But it is usually used for regression problems. Predictions are extracted from multiple models and an average of the predictions are used to make the final prediction. Weighted Average: Like averaging, weighted averaging is also used for regression tasks. Alternatively, it can be used while estimating probabilities in classification problems. Base learners are assigned different weights, which represent the importance of each model in the prediction. Ensemble Methods Ensemble methods became popular as a relatively simple device to improve the predictive performance of a base procedure. There are different reasons for this: the bagging procedure turns out to be a variance reduction scheme, at least for some base procedures. On the other hand, boosting methods are primarily reducing the (model) bias of the base procedure. This already indicates that bagging and boosting are very different ensemble methods. From the perspective of prediction, random forests is about as good as boosting, and often better than bagging.  Bootstrap Aggregation or Bagging tries to implement similar learners on small sample populations and then takes a mean of all the predictions. It combines Bootstrapping and Aggregation to form one ensemble model Reduces the variance error and helps to avoid overfitting Bagging algorithms include: Bagging meta-estimator Random forest Boosting refers to a family of algorithms which converts weak learner to strong learners. Boosting is a sequential process, where each subsequent model attempts to correct the errors of the previous model. Boosting is focused on reducing the bias. It makes the boosting algorithms prone to overfitting. To avoid overfitting, parameter tuning plays an important role in boosting algorithms. Some examples of boosting are mentioned below: AdaBoost GBM XGBM Light GBM CatBoost Why use ensemble models? Ensemble models help in improving algorithm accuracy as well as the robustness of a model. Both Bagging and Boosting should be known by data scientists and machine learning engineers and especially people who are planning to attend data science/machine learning interviews. Ensemble learning uses hundreds to thousands of models of the same algorithm and then work hand in hand to find the correct classification. You may also consider the fable of the blind men and the elephant to understand ensemble learning, where each blind man found a feature of the elephant and they all thought it was something different. However, if they would work together and discussed among themselves, they might have figured out what it is. Using techniques like bagging and boosting leads to increased robustness of statistical models and decreased variance. Now the question becomes, between these different “B” words. Which is better? Which is better, Bagging or Boosting? There is no perfectly correct answer to that. It depends on the data, the simulation and the circumstances. Bagging and Boosting decrease the variance of your single estimate as they combine several estimates from different models. So the result may be a model with higher stability. If the problem is that the single model gets a very low performance, Bagging will rarely get a better bias. However, Boosting could generate a combined model with lower errors as it optimizes the advantages and reduces pitfalls of the single model. By contrast, if the difficulty of the single model is overfitting, then Bagging is the best option. Boosting for its part doesn’t help to avoid over-fitting; in fact, this technique is faced with this problem itself. For this reason, Bagging is effective more often than Boosting. In this article we will discuss about Bagging, we will cover Boosting in the next post. But first, let us look into the very important concept of bootstrapping. Bootstrap Sampling Sampling is the process of selecting a subset of observations from the population with the purpose of estimating some parameters about the whole population. Resampling methods, on the other hand, are used to improve the estimates of the population parameters. In machine learning, the bootstrap method refers to random sampling with replacement. This sample is referred to as a resample. This allows the model or algorithm to get a better understanding of the various biases, variances and features that exist in the resample. Taking a sample of the data allows the resample to contain different characteristics then it might have contained as a whole. This is demonstrated in figure 1 where each sample population has different pieces, and none are identical. This would then affect the overall mean, standard deviation and other descriptive metrics of a data set. In turn, it can develop more robust models. Bootstrapping is also great for small size data sets that can have a tendency to overfit. In fact, we recommended this to one company who was concerned because their data sets were far from “Big Data”. Bootstrapping can be a solution in this case because algorithms that utilize bootstrapping can be more robust and handle new data sets depending on the methodology chosen(boosting or bagging). The reason behind using the bootstrap method is because it can test the stability of a solution. By using multiple sample data sets and then testing multiple models, it can increase robustness. Perhaps one sample data set has a larger mean than another, or a different standard deviation. This might break a model that was overfit, and not tested using data sets with different variations. One of the many reasons bootstrapping has become very common is because of the increase in computing power. This allows for many times more permutations to be done with different resamples than previously. Bootstrapping is used in both Bagging and Boosting Let us assume we have a sample of ‘n’ values (x) and we’d like to get an estimate of the mean of the sample. mean(x) = 1/n * sum(x) Consider a sample of 100 values (x) and we’d like to get an estimate of the mean of the sample. We can calculate the mean directly from the sample as: We know that our sample is small and that the mean has an error in it. We can improve the estimate of our mean using the bootstrap procedure: Create many (e.g. 1000) random sub-samples of the data set with replacement (meaning we can select the same value multiple times). Calculate the mean of each sub-sample Calculate the average of all of our collected means and use that as our estimated mean for the data Example: Suppose we used 3 re-samples and got the mean values 2.3, 4.5 and 3.3. Taking the average of these we could take the estimated mean of the data to be 3.367. This process can be used to estimate other quantities like the standard deviation and even quantities used in machine learning algorithms, like learned coefficients. While using Python, we do not have to implement the bootstrap method manually. The scikit-learn library provides an implementation that creates a single bootstrap sample of a dataset. The resample() scikit-learn function can be used for sampling. It takes as arguments the data array, whether or not to sample with replacement, the size of the sample, and the seed for the pseudorandom number generator used prior to the sampling. For example, let us create a bootstrap that creates a sample with replacement with 4 observations and uses a value of 1 for the pseudorandom number generator. boot = resample(data, replace=True, n_samples=4, random_state=1)As the bootstrap API does not allow to easily gather the out-of-bag observations that could be used as a test set to evaluate a fit model, in the univariate case we can gather the out-of-bag observations using a simple Python list comprehension. # out of bag observations  oob = [x for x in data if x not in boot]Let us look at a small example and execute it.# scikit-learn bootstrap  from sklearn.utils import resample  # data sample  data = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6]  # prepare bootstrap sample  boot = resample(data, replace=True, n_samples=4, random_state=1)  print('Bootstrap Sample: %s' % boot)  # out of bag observations  oob = [x for x in data if x not in boot]  print('OOB Sample: %s' % oob) The output will include the observations in the bootstrap sample and those observations in the out-of-bag sample.Bootstrap Sample: [0.6, 0.4, 0.5, 0.1]  OOB Sample: [0.2, 0.3]Bagging Bootstrap Aggregation, also known as Bagging, is a powerful ensemble method that was proposed by Leo Breiman in 1994 to prevent overfitting. The concept behind bagging is to combine the predictions of several base learners to create a more accurate output. Bagging is the application of the Bootstrap procedure to a high-variance machine learning algorithm, typically decision trees. Suppose there are N observations and M features. A sample from observation is selected randomly with replacement (Bootstrapping). A subset of features are selected to create a model with sample of observations and subset of features. Feature from the subset is selected which gives the best split on the training data. This is repeated to create many models and every model is trained in parallel Prediction is given based on the aggregation of predictions from all the models. This approach can be used with machine learning algorithms that have a high variance, such as decision trees. A separate model is trained on each bootstrap sample of data and the average output of those models used to make predictions. This technique is called bootstrap aggregation or bagging for short. Variance means that an algorithm’s performance is sensitive to the training data, with high variance suggesting that the more the training data is changed, the more the performance of the algorithm will vary. The performance of high variance machine learning algorithms like unpruned decision trees can be improved by training many trees and taking the average of their predictions. Results are often better than a single decision tree. What Bagging does is help reduce variance from models that are might be very accurate, but only on the data they were trained on. This is also known as overfitting. Overfitting is when a function fits the data too well. Typically this is because the actual equation is much too complicated to take into account each data point and outlier. Bagging gets around this by creating its own variance amongst the data by sampling and replacing data while it tests multiple hypothesis(models). In turn, this reduces the noise by utilizing multiple samples that would most likely be made up of data with various attributes(median, average, etc). Once each model has developed a hypothesis. The models use voting for classification or averaging for regression. This is where the “Aggregating” in “Bootstrap Aggregating” comes into play. Each hypothesis has the same weight as all the others. When we later discuss boosting, this is one of the places the two methodologies differ. Essentially, all these models run at the same time, and vote on which hypothesis is the most accurate. This helps to decrease variance i.e. reduce the overfit. Advantages Bagging takes advantage of ensemble learning wherein multiple weak learners outperform a single strong learner.  It helps reduce variance and thus helps us avoid overfitting. Disadvantages There is loss of interpretability of the model. There can possibly be a problem of high bias if not modeled properly. While bagging gives us more accuracy, it is computationally expensive and may not be desirable depending on the use case. There are many bagging algorithms of which perhaps the most prominent would be Random Forest.  Decision Trees Decision trees are simple but intuitive models. Using a top-down approach, a root node creates binary splits unless a particular criteria is fulfilled. This binary splitting of nodes results in a predicted value on the basis of the interior nodes which lead to the terminal or the final nodes. For a classification problem, a decision tree will output a predicted target class for each terminal node produced. We have covered decision tree algorithm  in detail for both classification and regression in another article. Limitations to Decision Trees Decision trees tend to have high variance when they utilize different training and test sets of the same data, since they tend to overfit on training data. This leads to poor performance when new and unseen data is added. This limits the usage of decision trees in predictive modeling. However, using ensemble methods, models that utilize decision trees can be created as a foundation for producing powerful results. Bootstrap Aggregating Trees We have already discussed about bootstrap aggregating (or bagging), we can create an ensemble (forest) of trees where multiple training sets are generated with replacement, meaning data instances. Once the training sets are created, a CART model can be trained on each subsample. Features of Bagged Trees Reduces variance by averaging the ensemble's results. The resulting model uses the entire feature space when considering node splits. Bagging trees allow the trees to grow without pruning, reducing the tree-depth sizes and resulting in high variance but lower bias, which can help improve predictive power. Limitations to Bagging Trees The main limitation of bagging trees is that it uses the entire feature space when creating splits in the trees. Suppose some variables within the feature space are indicating certain predictions, there is a risk of having a forest of correlated trees, which actually  increases bias and reduces variance. Why a Forest is better than One Tree?The main objective of a machine learning model is to generalize properly to new and unseen data. When we have a flexible model, overfitting takes place. A flexible model is said to have high variance because the learned parameters (such as the structure of the decision tree) will vary with the training data. On the other hand, an inflexible model is said to have high bias as it makes assumptions about the training data. An inflexible model may not have the capacity to fit even the training data and in both cases — high variance and high bias — the model is not able to generalize new and unseen data properly. You can through the article on one of the foundational concepts in machine learning, bias-variance tradeoff which will help you understand that the balance between creating a model that is so flexible memorizes the training data and an inflexible model cannot learn the training data.  The main reason why decision tree is prone to overfitting when we do not limit the maximum depth is because it has unlimited flexibility, which means it keeps growing unless there is one leaf node for every single observation. Instead of limiting the depth of the tree which results in reduced variance and increase in bias, we can combine many decision trees into a single ensemble model known as the random forest. What is Random Forest algorithm? Random forest is like bootstrapping algorithm with Decision tree (CART) model. Suppose we have 1000 observations in the complete population with 10 variables. Random forest will try to build multiple CART along with different samples and different initial variables. It will take a random sample of 100 observations and then chose 5 initial variables randomly to build a CART model. It will go on repeating the process say about 10 times and then make a final prediction on each of the observations. Final prediction is a function of each prediction. This final prediction can simply be the mean of each prediction. The random forest is a model made up of many decision trees. Rather than just simply averaging the prediction of trees (which we could call a “forest”), this model uses two key concepts that gives it the name random: Random sampling of training data points when building trees Random subsets of features considered when splitting nodes How the Random Forest Algorithm Works The basic steps involved in performing the random forest algorithm are mentioned below: Pick N random records from the dataset. Build a decision tree based on these N records. Choose the number of trees you want in your algorithm and repeat steps 1 and 2. In case of a regression problem, for a new record, each tree in the forest predicts a value for Y (output). The final value can be calculated by taking the average of all the values predicted by all the trees in the forest. Or, in the case of a classification problem, each tree in the forest predicts the category to which the new record belongs. Finally, the new record is assigned to the category that wins the majority vote. Using Random Forest for Regression Here we have a problem where we have to predict the gas consumption (in millions of gallons) in 48 US states based on petrol tax (in cents), per capita income (dollars), paved highways (in miles) and the proportion of population with the driving license. We will use the random forest algorithm via the Scikit-Learn Python library to solve this regression problem. First we import the necessary libraries and our dataset. import pandas as pd  import numpy as np  dataset = pd.read_csv('/content/petrol_consumption.csv')  dataset.head() Petrol_taxAverage_incomepaved_HighwaysPopulation_Driver_licence(%)Petrol_Consumption09.0357119760.52554119.0409212500.57252429.0386515860.58056137.5487023510.52941448.043994310.544410You will notice that the values in our dataset are not very well scaled. Let us scale them down before training the algorithm. Preparing Data For Training We will perform two tasks in order to prepare the data. Firstly we will divide the data into ‘attributes’ and ‘label’ sets. The resultant will then be divided into training and test sets. X = dataset.iloc[:, 0:4].values  y = dataset.iloc[:, 4].valuesNow let us divide the data into training and testing sets:from sklearn.model_selection import train_test_split  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)Feature Scaling The dataset is not yet a scaled value as you will see that the Average_Income field has values in the range of thousands while Petrol_tax has values in the range of tens. It will be better if we scale our data. We will use Scikit-Learn's StandardScaler class to do the same. # Feature Scaling  from sklearn.preprocessing import StandardScaler  sc = StandardScaler()  X_train = sc.fit_transform(X_train)  X_test = sc.transform(X_test)Training the Algorithm Now that we have scaled our dataset, let us train the random forest algorithm to solve this regression problem. from sklearn.ensemble import Random Forest Regressor  regressor = Random Forest Regressor(n_estimators=20,random_state=0)  regressor.fit(X_train, y_train)  y_pred = regressor.predict(X_test)The RandomForestRegressor is used to solve regression problems via random forest. The most important parameter of the RandomForestRegressor class is the n_estimators parameter. This parameter defines the number of trees in the random forest. Here we started with n_estimator=20 and check the performance of the algorithm. You can find details for all of the parameters of RandomForestRegressor here. Evaluating the Algorithm Let us evaluate the performance of the algorithm. For regression problems the metrics used to evaluate an algorithm are mean absolute error, mean squared error, and root mean squared error.  from sklearn import metrics  print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))  print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))  print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred))) Mean Absolute Error: 51.76500000000001 Mean Squared Error: 4216.166749999999 Root Mean Squared Error: 64.93201637097064 With 20 trees, the root mean squared error is 64.93 which is greater than 10 percent of the average petrol consumption i.e. 576.77. This may indicate, among other things, that we have not used enough estimators (trees). Let us now change the number of estimators to 200, the results are as follows: Mean Absolute Error: 48.33899999999999 Mean Squared Error: 3494.2330150000003  Root Mean Squared Error: 59.112037818028234 The graph below shows the decrease in the value of the root mean squared error (RMSE) with respect to number of estimators.  You will notice that the error values decrease with the increase in the number of estimators. You may consider 200 a good number for n_estimators as the rate of decrease in error diminishes. You may try playing around with other parameters to figure out a better result. Using Random Forest for ClassificationNow let us consider a classification problem to predict whether a bank currency note is authentic or not based on four attributes i.e. variance of the image wavelet transformed image, skewness, entropy, andkurtosis of the image. We will use Random Forest Classifier to solve this binary classification problem. Let’s get started. import pandas as pd  import numpy as np  dataset = pd.read_csv('/content/bill_authentication.csv')  dataset.head()VarianceSkewnessKurtosisEntropyClass03.621608.6661-2.8073-0.44699014.545908.1674-2.4586-1.46210023.86600-2.63831.92420.10645033.456609.5228-4.0112-3.59440040.32924-4.45524.5718-0.988800Similar to the data we used previously for the regression problem, this data is not scaled. Let us prepare the data for training. Preparing Data For Training The following code divides data into attributes and labels: X = dataset.iloc[:, 0:4].values  y = dataset.iloc[:, 4].values The following code divides data into training and testing sets:from sklearn.model_selection import train_test_split  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) Feature Scaling We will do the same thing as we did for the previous problem. # Feature Scaling  from sklearn.preprocessing import StandardScaler  sc = StandardScaler()  X_train = sc.fit_transform(X_train)  X_test = sc.transform(X_test)Training the Algorithm Now that we have scaled our dataset, let us train the random forest algorithm to solve this classification problem. from sklearn.ensemble import Random Forest Classifier  classifier = RandomForestClassifier(n_estimators=20, random_state=0)  classifier.fit(X_train, y_train)  y_pred = classifier.predict(X_test)For classification, we have used RandomForestClassifier class of the sklearn.ensemble library. It takes n_estimators as a parameter. This parameter defines the number of trees in out random forest. Similar to the regression problem, we have started with 20 trees here. You can find details for all of the parameters of Random Forest Classifier here. Evaluating the Algorithm For evaluating classification problems,  the metrics used are accuracy, confusion matrix, precision recall, and F1 valuesfrom sklearn.metrics import classification_report, confusion_matrix, accuracy_score  print(confusion_matrix(y_test,y_pred))  print(classification_report(y_test,y_pred))  print(accuracy_score(y_test, y_pred)) The output will look something like this: Output:[ [ 155   2] [     1  117] ]Precisionrecallf1-scoresupport00.990.990.9915710.980.990.99118accuracy0.99275macro avg0.990.990.992750.98909090909090910.990.990.99275The accuracy achieved by our random forest classifier with 20 trees is 98.90%. Let us change the number of trees to 200.from sklearn.ensemble import Random Forest Classifier  classifier = Random Forest Classifier(n_estimators=200, random_state=0)  classifier.fit(X_train, y_train)  y_pred = classifier.predict(X_test) Output:[ [ 155   2] [     1  117] ]Precisionrecallf1-scoresupport00.990.990.9915710.980.990.99118accuracy0.99275macro avg0.990.990.992750.98909090909090910.990.990.99275Unlike the regression problem, changing the number of estimators for this problem did not make any difference in the results.An accuracy of 98.9% is pretty good. In this case, we have seen that there is not much improvement if the number of trees are increased. You may try playing around with other parameters of the RandomForestClassifier class and see if you can improve on our results. Advantages and Disadvantages of using Random Forest As with any algorithm, there are advantages and disadvantages to using it. Let us look into the pros and cons of using Random Forest for classification and regression. Advantages Random forest algorithm is unbiased as there are multiple trees and each tree is trained on a subset of data.  Random Forest algorithm is very stable. Introducing a new data in the dataset does not affect much as the new data impacts one tree and is pretty hard to impact all the trees. The random forest algorithm works well when you have both categorical and numerical features. With missing values in the dataset, the random forest algorithm performs very well. Disadvantages A major disadvantage of random forests lies in their complexity. More computational resources are required and also results in the large number of decision trees joined together. Due to their complexity, training time is more compared to other algorithms. Summary In this article we have covered what is ensemble learning and discussed about basic ensemble techniques. We also looked into bootstrap sampling involves iteratively resampling of a dataset with replacement which allows the model or algorithm to get a better understanding various features. Then we moved on to bagging followed by random forest. We also implemented random forest in Python for both regression and classification and came to a conclusion that increasing number of trees or estimators does not always make a difference in a classification problem. However, in regression there is an impact.  We have covered most of the topics related to algorithms in our series of machine learning blogs,click here. If you are inspired by the opportunities provided by machine learning, enrol in our  Data Science and Machine Learning Courses for more lucrative career options in this landscape.Build your own projects using Machine Learning with Python. Practice with our industry experts on our live workshops now.
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Bagging and Random Forest in Machine Learning

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Boosting and AdaBoost in Machine Learning

Ensemble learning is a strategy in which a group of models are used to find a solution to a challenging problem, by using a strategy and combining diverse machine learning models into one single predictive model.In general, ensemble methods are mainly used for improving the overall performance accuracy of a model and combine several different models, also known as the base learners, to predict the results, instead of using a single model.In one of the articles related to ensemble learning, we have already discussed about the popular ensemble method, Bootstrap Aggregation. Bagging tries to implement similar learners on small sample populations and then takes a mean of all the predictions. It combines Bootstrapping and Aggregation to form one ensemble model. It basically reduces the variance error and helps to avoid overfitting. In this article we will look into the limitations of bagging and how a boosting algorithm can be used to overcome those limitations. We will also learn about various types of boosting algorithms and implement one of them in Python. Let’s get started.What are the limitations of Bagging?Let us recall the concept of bagging and consider a binary classification problem. We are either classifying an observation as 0 or as 1.In bagging, T bootstrap samples are selected, a classifier is fitted on each of these samples, and the models are trained in parallel. In a Random Forest, decision trees are trained in parallel. Then the results of all classifiers are averaged into a bagging classifier:Formula for a Bagging ClassifierLet us consider 3 classifiers and the result for the classification can either be right or wrong. If we plot the results of the 3 classifiers, there are regions in which the classifiers will be wrong. These regions are represented in red in the figure below.Example case in which Bagging works wellThe above example works pretty well as when one classifier is wrong, the two others are correct. By voting classifier, you can achieve a better accuracy. However, there are cases where Bagging does not work properly, when all classifiers are mistaken to be in the same region.Due to this reason, the intuition behind the discovery of Boosting was the following :instead of training parallel models, one should train models sequentiallyeach model should focus on where the performance of the previous classifier was poorWith this intuition, Boosting algorithm was introduced. Let us understand what Boosting is all about.What is Boosting?Boosting is an ensemble modeling technique which attempts to build a strong classifier from the number of weak classifiers. It is done by building a model using weak models in series. First, a model is built from the training data. Then the second model is built which tries to correct the errors present in the first model. This procedure is continued and models are added until either the complete training data set is predicted correctly or the maximum number of models are added.Boosting being a sequential process, each subsequent model attempts to correct the errors of the previous model. It is focused on reducing the bias unlike bagging. It makes the boosting algorithms prone to overfitting. To avoid overfitting, parameter tuning plays an important role in boosting algorithms, which will be discussed in the later part of this article. Some examples of boosting are XGBoost, GBM, ADABOOST etc..How can boosting identify weak learners?To find weak learners, we apply base learning (ML) algorithms with a different distribution. As each time base learning algorithm is applied, it generates a new weak prediction rule. This is an iterative process. After many iterations, the boosting algorithm combines these weak rules into a single strong prediction rule.How do we choose a different distribution for each round?Step 1: The base learner takes all the distributions and assigns equal weight or attention to each observation.Step 2: If there is any prediction error caused by first base learning algorithm, then we pay higher attention to observations having prediction error. Then, we apply the next base learning algorithm.Step 3: Iterate Step 2 till the limit of base learning algorithm is reached or higher accuracy is achieved.Finally, it combines the outputs from weak learner and creates a strong learner which eventually improves the prediction power of the model. Boosting gives higher focus to examples which are mis-classified or have higher errors by preceding weak rules.How would you classify an email as SPAM or not?Our initial approach would be to identify ‘SPAM’ and ‘NOT SPAM’ emails using the following criteria. If: Email has only one image file (promotional image), It’s a SPAM.Email has only link(s), It’s a SPAM.Email body consists of sentences like “You won a prize money of $ xxxxxx”, It’s a SPAM.Email from our official domain “www.knowledgehut.com” , Not a SPAM.Email from known source, Not a SPAM.Individually, these rules are not powerful enough to classify an email into ‘SPAM’ or ‘NOT SPAM’. Therefore, these rules are called as weak learner.To convert weak learner to strong learner, we’ll combine the prediction of each weak learner using methods like:Using average/ weighted averageConsidering prediction has higher voteExample: Above, we have defined 5 weak learners. Out of these 5, 3 are voted as ‘SPAM’ and 2 are voted as ‘Not a SPAM’. In this case, by default, we’ll consider an email as SPAM because we have higher(3) vote for ‘SPAM’Boosting helps in training a series of low performing algorithms, called weak learners, simply by adjusting the error metric over time. Weak learners are considered to be those algorithms whose error rate is slightly under 50% as illustrated below:Weighted errorsLet us consider data points on a 2D plot. Some of the data points will be well classified, others won’t. The weight attributed to each error when computing the error rate is 1/n where n is the number of data points to classify.Now if we apply some weight to the errors :You might now notice that we give more weight to the data points that are not well classified. An illustration of the weighting process is mentioned below:Example of weighting processIn the end, we want to build a strong classifier that may look like the figure mentioned below:Strong ClassifierTree stumpsThere might be a question in your mind about how many classifiers should one implement in order to ensure it works well. And how is each classifier chosen at each step?Well, Tree stumps defines a 1-level decision tree. At each step, we need to find the best stump, i.e the best data split, which will minimize the overall error. You can see a stump as a test, in which the assumption is that everything that lies on one side belongs to class 1, and everything that lies on the other side belongs to class 0.Many such combinations are possible for a tree stump. Let us look into an example to understand how many combinations we face.3 data points to splitWell there are 12 possible combinations. Let us check how.12 StumpsThere are 12 possible “tests” we could make. The “2” on the side of each separating line simply represents the fact that all points on one side could be points that belong to class 0, or to class 1. Therefore, there are 2 tests embedded in it.At each iteration t, we will choose ht the weak classifier that splits best the data, by reducing the overall error rate the most. Recall that the error rate is a modified error rate version that takes into account what has been introduced before.Finding the best splitThe best split is found by identifying at each iteration t, the best weak classifier ht, generally a decision tree with 1 node and 2 leaves (a stump). Let us consider an example of credit defaulter, i.e whether a person who borrowed money will return or not.Identifying the best splitIn this case, the best split at time t is to stump on the Payment history, since the weighted error resulting from this split is minimum.Simply note that decision tree classifiers like these ones can in practice be deeper than a simple stump. This will be considered as a hyper-parameter.Combining classifiersIn the next step we combine the classifiers into a Sign classifier, and depending on which side of the frontier a point will stand, it is classified as 0 or 1. It can be achieved by:Combining classifiersYou can improve the classifier by adding weights on each classifier, to avoid giving the same importance to the different classifiers.AdaBoostPseudo-codePseudo-codeThe key elements to keep in mind are:Z is a constant whose role is to normalize the weights so that they add up to 1αt is a weight that we apply to each classifierThis algorithm is called AdaBoost or Adaptive Boosting. This is one of the most important algorithms among all boosting methods.ComputationBoosting algorithms are generally fast to train, although we consider every stump possible and compute exponentials recursively.Well, if we choose αt and Z properly, the weights that are supposed to change at each step simplify to:Weights after choice of α and ZTypes of Boosting AlgorithmsUnderlying engine used for boosting algorithms can be anything.  It can be decision stamp, margin-maximizing classification algorithm etc. There are many boosting algorithms which use other types of engines such as: AdaBoost (Adaptive Boosting)Gradient Tree BoostingXGBoostIn this article, we will focus on AdaBoost and Gradient Boosting followed by their respective Python codes and a little bit about XGBoost.Where are Boosted algorithms required?Boosted algorithms are mainly used when there is plenty of data to make a prediction and high predictive power is expected. It is used to reduce bias and variance in supervised learning. It combines multiple weak predictors to build strong predictor.The underlying engine used for boosting algorithms can be anything. For instance, AdaBoost is a boosting done on Decision stump. There are many other boosting algorithms which use other types of engine such as:GentleBoostGradient BoostingLPBoostBrownBoostAdaptive BoostingAdaptive Boosting, or most commonly known AdaBoost, is a Boosting algorithm. This algorithm uses the method to correct its predecessor. It pays more attention to under fitted training instances by the previous model. Thus, at every new predictor the focus is more on the complicated cases more than the others.It fits a sequence of weak learners on different weighted training data. It starts by predicting the original data set and gives equal weight to each observation. If prediction is incorrect using the first learner, then it gives higher weight to observation which have been predicted incorrectly. Being an iterative process, it continues to add learner(s) until a limit is reached in the number of models or accuracy.Mostly, AdaBoost uses decision stamps. But, we can use any machine learning algorithm as base learner if it accepts weight on training data set. We can use AdaBoost algorithms for both classification and regression problems.Let us consider the example of the image mentioned above. In order to build an AdaBoost classifier, consider that as a first base classifier a Decision Tree algorithm is trained to make predictions on our training data. Applying the following methodology of AdaBoost, the weight of the misclassified training instances is increased. Then the second classifier is trained and the updated weights are acknowledged. It repeats the procedure over and over again.At the end of every model prediction we end up boosting the weights of the misclassified instances so that the next model does a better job on them, and so on.This sequential learning technique might sound similar to Gradient Descent, except that instead of tweaking a single predictor’s parameter to minimize the cost function, AdaBoost adds predictors to the ensemble, gradually making it better.One disadvantage of this algorithm is that the model cannot be parallelized since each predictor can only be trained after the previous one has been trained and evaluated.Below are the steps for performing the AdaBoost algorithm:Initially, all observations are given equal weights.A model is built on a subset of data.Using this model, predictions are made on the whole dataset.Errors are calculated by comparing the predictions and actual values.While creating the next model, higher weights are given to the data points which were predicted incorrectly.Weights can be determined using the error value. For instance,the higher the error the more is the weight assigned to the observation.This process is repeated until the error function does not change, or the maximum limit of the number of estimators is reached.Hyperparametersbase_estimators: specify the base type estimator, i.e. the algorithm to be used as base learner.n_estimators: It defines the number of base estimators, where the default is 10 but you can increase it in order to obtain a better performance.learning_rate: same impact as in gradient descent algorithmmax_depth: Maximum depth of the individual estimatorn_jobs: indicates to the system how many processors it is allowed to use. Value of ‘-1’ means there is no limit;random_state: makes the model’s output replicable. It will always produce the same results when you give it a fixed value as well as the same parameters and training data.Now, let us take a quick look at how to use AdaBoost in Python using a simple example on handwritten digit recognition.import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.ensemble import AdaBoostClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score from sklearn.model_selection import cross_val_score from sklearn.model_selection import cross_val_predict from sklearn.model_selection import train_test_split from sklearn.model_selection import learning_curve from sklearn.datasets import load_digitsLet us load the data :dataset = load_digits() X = dataset['data'] y = dataset['target']X contains arrays of length 64 which are simply flattened 8x8 images. The aim of this dataset is to recognize handwritten digits. Let’s take a look at a given handwritten digit:plt.imshow(X[4].reshape(8,8))If we stick to a Decision Tree Classifier of depth 1 (a stump), here’s how to implement AdaBoost classifier:reg_ada = AdaBoostClassifier(DecisionTreeClassifier(max_depth=1)) scores_ada = cross_val_score(reg_ada, X, y, cv=6) scores_ada.mean()0.2636257855582272And it should head a result of around 26%, which can largely be improved. One of the key parameters is the depth of the sequential decision tree classifiers. How does accuracy improve with depth of the decision trees?score = [] for depth in [1,2,10] : reg_ada = AdaBoostClassifier(DecisionTreeClassifier(max_depth=depth)) scores_ada = cross_val_score(reg_ada, X, y, cv=6) score.append(scores_ada.mean()) score[0.2636257855582272, 0.5902852679072207, 0.9527524912410157]And the maximal score is reached for a depth of 10 in this simple example, with an accuracy of 95.3%.Gradient BoostingThis is another very popular Boosting algorithm which works pretty similar to what we’ve seen for AdaBoost. Gradient Boosting works by sequentially adding the previous predictors underfitted predictions to the ensemble, ensuring the errors made previously are corrected.The difference lies in what it does with the underfitted values of its predecessor. Contrary to AdaBoost, which tweaks the instance weights at every interaction, this method tries to fit the new predictor to the residual errors made by the previous predictor.So that you can understand Gradient Boosting it is important to understand Gradient Descent first.Below are the steps for performing the Gradient Boosting algorithm:A model is built on a subset of data.Using this model, predictions are made on the whole dataset.Errors are calculated by comparing the predictions and actual values.A new model is created using the errors calculated as target variable. Our objective is to find the best split to minimize the error.The predictions made by this new model are combined with the predictions of the previous.New errors are calculated using this predicted value and actual value.This process is repeated until the error function does not change, or the maximum limit of the number of estimators is reached.Hyperparametersn_estimators: It controls the number of weak learners.Learning_rate: Controls the contribution of weak learners in the final combination. There is a trade-off between learning_rate and n_estimators.min_samples_split: Minimum number of observation which is required in a node to be considered for splitting. It is used to control overfitting.min_samples_leaf: Minimum samples required in a terminal or leaf node. Lower values should be chosen for imbalanced class problems since the regions in which the minority class will be in the majority will be very small.min_weight_fraction_leaf: similar to the previous but defines a fraction of the total number of observations instead of an integer.max_depth : maximum depth of a tree. Used to control overfitting.max_lead_nodes : maximum number of terminal leaves in a tree. If this is defined max_depth is ignored.max_features : number of features it should consider while searching for the best split.You can tune loss function for better performance.Implementation in PythonYou can find Gradient Boosting function in Scikit-Learn’s library.# for regression from sklearn.ensemble import GradientBoostingRegressor model = GradientBoostingRegressor(n_estimators=3,learning_rate=1) model.fit(X,Y) # for classification from sklearn.ensemble import GradientBoostingClassifier model = GradientBoostingClassifier() model.fit(X,Y)XGBoostXG Boost or Extreme Gradient Boosting is an advanced implementation of the Gradient Boosting. This algorithm has high predictive power and is ten times faster than any other gradient boosting techniques. Moreover, it includes a variety of regularization which reduces overfitting and improves overall performance.AdvantagesIt implements regularization which helps in reducing overfit (Gradient Boosting does not have);It implements parallel processing which is much faster than Gradient Boosting;Allows users to define custom optimization objectives and evaluation criteria adding a whole new dimension to the model;XGBoost has an in-built routine to handle missing values;XGBoost makes splits up to the max_depth specified and then starts pruning the tree backwards and removes splits beyond which there is no positive gain;XGBoost allows a user to run a cross-validation at each iteration of the boosting process and thus it is easy to get the exact optimum number of boosting iterations in a single run.Boosting algorithms represent a different machine learning perspective which is turning a weak model to a stronger one to fix its weaknesses. I hope this article helped you understand how boosting works.We have covered most of the topics related to algorithms in our series of machine learning blogs, click here. If you are inspired by the opportunities provided by machine learning, enroll in our  Data Science and Machine Learning Courses for more lucrative career options in this landscape.Your one-stop-shop for Machine Learning is just a click away. Access our live online training and find easy solutions to all your queries here.
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Boosting and AdaBoost in Machine Learning

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