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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!
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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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Role of Statistics in Data Science

Takeaways from this article In this article, we understand why data is important, and talk about the importance of statistics in data analysis and data science. We also understand some basic statistics concepts and terminologies. We see how statistics and machine learning work in sync to give deep insights into data.  We understand the fundamentals behind Bayesian thinking and how Bayesian theorem works. Introduction Data plays a huge role in today’s tech world. All technologies are data-driven, and humongous amounts of data are produced on a daily basis. A data scientist is a professional who is able to analyse data sources, clean and process the data, understand why and how such data has been generated, take insights from it, and make changes such that they profit the organization. These days, everything revolves around data.  Data Cleaning: It deals with gathering the data and structuring it so that it becomes easy to pass this data as input to any machine learning algorithm. This way, redundant, irrelevant data and noise can also be eliminated.  Data Analysis: This deals with understanding more about the data, why the data has yielded certain results, and what can be done to improve it. It also helps calculate certain numerical values like mean, variance, the distributions, and the probability of a certain prediction.  How the basics of statistics will serve as a foundation to manipulate data in data scienceThe basics of statistics include terminologies, and methods of applying statistics in data science. In order to analyze the data, the important tool is statistics. The concepts involved in statistics help provide insights into the data to perform quantitative analysis on it. In addition to this, as a foundation, the basics and working of linear regression and classification algorithms must also be known to a data science aspirant.  Terminologies associated with statistics Population: It is an entire pool of data from where a statistical sample is extracted. It can be visualized as a complete data set of items that are similar in nature.  Sample: It is a subset of the population, i.e. it is an integral part of the population that has been collected for analysis.  Variable: A value whose characteristics such as quantity can be measured, it can also be addressed as a data point, or a data item.  Distribution: The sample data that is spread over a specific range of values.  Parameter: It is a value that is used to describe the attributes of a complete data set (also known as ‘population’). Example: Average, Percentage  Quantitative analysis: It deals with specific characteristics of data- summarizing some part of data, such as its mean, variance, and so on.  Qualitative analysis: This deals with generic information about the type of data, and how clean or structured it is.  How does analyzing data using statistics help gain deep insights into data? Statistics serve as a foundation while dealing with data and its analysis in data science. There are certain core concepts and basics which need to be thoroughly understood before jumping into advanced algorithms.  Not everyone understand the performance metrics of machine learning algorithms like f-score, recall, precision, accuracy, root mean squared error, and so on. Instead, visual representation of the data and the performance of the algorithm on the data serves as a good metric for the layperson to understand the same.  Also, visual representation helps identify outliers, specific trivial patterns, and certain metric summary such as mean, median, variance, that helps in understanding the middlemost value, and how the outlier affects the rest of the data.  Statistical Data Analysis Statistical data analysis deals with the usage of certain statistical tools that need knowledge of statistics. Software can also help with this, but without understanding why something is happening, it is impossible to get considerable work done in statistics and data science.  Statistics deals with data variables that are either univariate or multivariate. Univariate, as the name suggests deals with single data values, whereas multivariate data deals with the multiple number of values. Discriminant data analysis, factor data analysis can be performed on multivariate data. On the other hand, univariate data analysis, Z-test, F-test can be performed if we are dealing with univariate data.  Data associated with statistics is of many types. Some of them have been discussed below. Categorical data represents characteristics of people, such as marital status, gender, food they like, and so on. It is also known as ‘qualitative data’ or ‘yes/no data’. It takes numerical values like ‘1’, ‘2’, where these numbers indicate one or other type of characteristics. These numbers are not mathematically significant, which means it can’t be associated with each other. Continuous data deals with data that is immeasurable, and can’t be counted, which basically continual forms of values are. Predictions from a linear regression are continuous in nature. It is a continuous distribution that is also known as probability density function. On the other hand, discrete values can be measured, counted, and are discontinuous. Predictions from logistic regression are considered to be discrete in nature. Discrete data is non-continuous, and density concept doesn’t come into the picture here. The distribution is known as probability mass function. The Best way to Learn Statistics for Data Science The best way to learn anything is by implementing it, by working on it, by making mistakes and again learning from it.  It is important to understand the concepts, either by going through standard books or well-known websites, before implementing them.  Before jumping into data science, the core statistics concepts like such as regression, maximum likelihood, distributions, priors, posteriors, conditional probability, Bayesian theorem and basics of machine learning have to be understood clearly. Core statistics concepts Descriptive statistics: As the name suggests, it uses the data to give out more information about every aspect of the data with the help of graphs, plots, or numbers. It organizes the data into a structure, and helps think about the attributes that highlight the important parts of the data. Inferential statistics: It deals with drawing inferences/conclusions on the sample data set which is obtained from the population (entire data set) based on the relationship identified between data points in the data set. It helps in generalizing the relationship to the entire dataset. It is important to remember that the dataset drawn from the population is relevant and represents the population accurately. Regression: The term ‘regression’ which is a part of statistics and machine learning, talks about how data can be fit to a line, and how every point from the straight line gives some insights. In terms of machine learning, it can be understood as tasks that can be solved without explicitly being programmed. They discuss how a line can be fit to a given set of data points, and how it can be further extrapolated for the predictions to be done.  Maximum likelihood: It is a method that helps in finding values of parameters for a specific model. The values of the parameters have to be such that the likelihood of the predictions that occur have to be maximum in comparison to the data values that were actually observed. This means the difference between the actual and predicted value has to be less, thereby reducing the error and increasing the accuracy of the predictions.  Note: This concept is generally used with Logistic regression when we are trying to find the output as 0 or 1, yes or no, wherein the maximum likelihood tells about how likely a data point is near to 0 or 1.  Bayesian thinking Bayesian thinking deals with using probability to model the process of sampling, and being able to quantify the uncertainty associated with the data that would be collected.  This is known as prior probability- which means the level of uncertainty that is associated with the data before it is collected to be analysed.  Posterior probability deals with the uncertainty that occurs after the data has been collected.  Machine learning algorithms are usually focussed on giving the best predictions as output with minimal errors, exact probabilities of specific events occurring and so on. Bayes theorem is a way of calculating the probability of a hypothesis (a situation, which might not have occurred in reality) based on our previous experiences and the knowledge we have gained by it. This is considered as a basic concept that needs to be known.  Bayes theorem can be stated as follows: P(hypo | data) = (P(data | hypo) * P(hypo)) / P(data)In the above equation,   P(hypo | data) is the probability of a hypothesis ‘hypo’ when data ‘data’ is given, which is also known as posterior probability.   P(data | hypo) is the probability of data ‘data’ when the specific hypothesis ‘hypo’ is known to be true.   P(hypo) is the probability of a hypothesis ‘hypo’ being true (irrespective of the data in hand), which is also known as prior probability of ‘hypo’.   P(data) is the probability of the data (irrespective of the hypothesis). The idea here is to get the value of the posterior probability, given other data. The posterior probability for a variety of different hypotheses has to be found out, and the probability that has the highest value is selected. This is known as the maximum probable hypothesis, and is also known as the maximum a posteriori (MAP) hypothesis.MAP(hypo) = max(P(hypo | data))If the value of P(hypo | data) is replaced with the value we saw before, the equation would become:MAP(hypo) = max((P(data | hypo) * P(hypo)) / P(data))P(data) is considered as a normalizing term that helps in determining the probability. This value can be safely ignored when required, since it is a constant value. Naïve Bayes classifier   It is an algorithm that can be used with binary or multi-class classification problems. It is a simple algorithm wherein the probability for every hypothesis is simplified.   This is done in order to make the data more traceable. Instead of calculating value of every attribute like P(data1, data2,..,datan|hypo), we assume that every data point is independent of every other data point in the data set when the respective output is given.   This way, the equation becomes:P(data1 | hypo) * P(data2 |hypo) * … * P(data-n| hypo).This way, the attributes would be independent of each other. This classifier performs quite well even in the real world with real data when the assumption of data points being independent of each other doesn’t hold good.  Once a Naïve Bayes classifier has learnt from the data, it stores a list of probabilities in a data structure. Probabilities such as ‘class probability’ and ‘condition probability’ are stored. Training such a model is quick since the probability of every class and its associated value needs to be determined, and this doesn’t involve any optimization processes or changing of coefficient to give better predictions.   Class probability: It tells about the probability of every class that is present in the training dataset. It can be calculated by finding the frequency of values that belongs to each class divided by the total number of values.  Class probability = (number of classes/(number of classes of group 0 + number of classes of group 1)) Conditional probability: It talks about the conditional probability of every input that is associated with a class value. It can be calculated by finding the frequency of every data attribute in the data for a given class, and this can be determined by the number of data values that have that data label/class value.  Conditional probability P(condition | result ) = number of ((values with that condition and values with that result)/ (number of values with that result)) Not just the concept, once the user understands the way in which a data scientist needs to think, they will be able to focus on getting cleaner data, with better insights that would lead to performing better analysis, which in turn would give great results.  Introduction to Statistical Machine Learning The methods used in statistics are important to train and test the data that is used as input to the machine learning model. Some of these include outlier/anomaly detection, sampling of data, data scaling, variable encoding, dealing with missing values, and so on.  Statistics is also essential to evaluate the model that has been used, i.e. see how well the machine learning model performs on a test dataset, or on data that it has never seen before.  Statistics is essential in selecting the final and appropriate model to deal with that specific data in a predictive modelling situation.  It is also needed to show how well the model has performed, by taking various metrics and showing how the model has fared.  Metrics used in Statistics Most of the data can be fit to a common pattern that is known as Gaussian distribution or normal distribution. It is a bell-shaped curve that can be used to summarize the data with the below mentioned two parameters:  Mean: It is understood as the central most value when the data points are arranged in a descending or ascending order, or the most likely value.Mode: It can be understood as the data point that occurs the greatest number of times, i.e. The frequency of the value in the dataset would be very high.  Median: It is a measure of central tendency of the data set. It is the middle number, that can be found by sorting all the data points in a dataset and picking the middle-most element. If the number of data points in a dataset is odd, one single middle value is picked up, whereas two middle values are picked and their mean is calculated if the number of data points in a dataset is even. Range: It refers to the value that is calculated by finding the difference between the largest and the smallest value in a dataset. Quartile: As the name suggests, quartiles are values that divide the data points in a dataset into quarters. It is calculated by sorting the elements in order and then dividing the dataset into 4 equal parts. Three quartiles are identified: The first quartile that is the 25th percentile, the second quartile which is the 50th percentile and the third quartile that is the 75th percentile. Each of these quartiles tells about the percentage of data that is smaller or larger in comparison to other percentiles of data. Example: 25th percentile suggests that 25 percent of the data set is smaller than the remaining 75 percent of the data set. Quartile helps understand how the data is distributed around the median (which is the 50th percentile/second quartile). There are other distributions as well, and it depends on the type of data we have and the insights we need from that data, but Gaussian is considered as one of the basic distributions. Variance: The average of the difference between every value and the mean of that specific distribution.  Standard deviation: It can be understood as the measure that indicates the dispersion that occurs in the data points of the input data.  Conclusion In this post, we understood why and how statistics is important to understand and work with data science. We saw a few terminologies of statistics that are essential in understanding the insights which statistics would end up giving to data scientist. We also saw a few basic algorithms that every data scientist needs to know, in order to learn other advanced algorithms.  
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Role of Statistics in Data Science

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Activation Functions for Deep Neural Networks

The Universal Approximation Theorem Any predictive model is a mathematical function, y = f(x) that can map the features (x) to the target variable (y). The function, f(x) can be a linear function or it can be a fairly complex nonlinear function. The function, f(x) can help predict with high accuracy depending on the distribution of the data. In the case of neural networks, it would also depend on the type of network architecture that's employed. The Universal Approximation Theorem says that irrespective of what the f(x) is, a neural network model can be built that can approximately deliver the desired result. In order to build a proper neural network architecture, let us take a look at the activation functions. What are Activation Functions? Simply put, activation functions define the output of neurons given a certain set of inputs. Activation functions are mathematical functions that are added to neural network models to enable the models to learn complex patterns. An activation function takes in the output from the previous layer, passes it through the mathematical function to convert it into some form, that can be considered as an input for the next computation layer. Activation functions determine the final accuracy of a network model while also contributing to the computational efficiency of building the model. Why do we need Activation Functions? In a neural network, if we add the hidden layers as the weighted sum of the inputs, this would translate into a linear function which is equivalent to a linear regression model. Neural Network ArchitectureIn the above diagram, we see the hidden layer is simply the weighted sum of the inputs from the input layer. For example, b1 = bw1 + a1w1 + a2w3 which is nothing but a linear function.  Multi-layer neural network models can classify linearly inseparable classes. However, in order to do so, we need the network to be transformed to a nonlinear function. For this nonlinear transformation to happen, we would pass the weighted sum of the inputs through an activation function. These activation functions are nonlinear functions which are applied at the hidden layers. Each hidden layer can have different activation functions, though mostly all neurons in each layer will have the same activation function. Types of Activation Functions? In this section we discuss the following: Linear Function Threshold Activation Function Bipolar Activation Function Logistic Sigmoid Function Bipolar Sigmoid Function Hyperbolic Tangent Function Rectified Linear Unit Function Swish Function (proposed by Google Brain - a deep learning artificial intelligence research team at Google) Linear Function: A linear function is similar to a straight line, y=mx. Irrespective of the number of hidden layers, if all the layers are linear in nature, then the final output is also simply a linear function of the input values. Hence we take a look at the other activation functions which are non-linear in nature and can help learn complex patterns. Threshold Activation Function: In this case, if the input is above a certain value, the neuron is activated. However, it is to note that this function provides either a 1 or a 0 as the output. In other words, if we need to classify certain inputs into more than 2 categories, a Threshold-Activation function is not a suitable one. Because of its binary output nature, this function is also known as binary-step activation function.Threshold Activation FunctionBipolar Activation Function: This is similar to the threshold function that was explained above. However, this activation function will return an output of either -1 or +1 based on a threshold.Bipolar Activation FunctionLogistic Sigmoid Function: One of the most frequently used activation functions is the Logistic Sigmoid Function. Its output ranges between 0 and 1 and is plotted as an ‘S’ shaped graph.Logistic Sigmoid FunctionThis is a nonlinear function and is characterised by a small change in x that would lead to large change in y. This activation function is generally used for binary classification where the expected output is 0 or 1. This activation function provides an output between 0 and 1 and a default threshold of 0.5 is considered to convert the continuous output to 0 or 1 for classifying the observationsAnother variation of the Logistic Sigmoid function is the Bipolar Sigmoid Function. This activation function is a rescaled version of the Logistic Sigmoid Function which provides an output in the range of -1 to +1.Bipolar Logistic FunctionHyperbolic Tangent Function: This activation function is quite similar to the sigmoid function. Its output ranges between -1 to +1.Hyperbolic Tangent FunctionRectified Linear Activation Function: This activation function, also known as ReLU, outputs the input if it is positive, else will return zero. That is to say, if the input is zero or less, this function will return 0 or will return the input itself. This function mostly behaves like a linear function because of which the computational simplicity is achieved.This activation function has become quite popular and is often used because of its computational efficiency compared to sigmoid and the hyperbolic tangent function that helps the model converge faster.  Another critical point to note is that while the sigmoid & the hyperbolic tangent function tries to approximate a zero value, the Rectified Linear Activation Functions can return true zero.Rectified Linear Units Activation FunctionOne disadvantage of ReLU is that when the inputs are close to zero or negative, the gradient of the function becomes zero. This causes a problem for the algorithm while performing back-propagation and in turn the model cannot converge. This is commonly termed as the “Dying” ReLU problem. There are a few variations of the ReLU activation function, such as, Noisy ReLU, Leaky ReLU, Parametric ReLU and Exponential Linear Units (ELU) Leaky ReLU which is a modified version of ReLU, helps solve the “Dying” ReLU problem. It helps perform back-propagation even when the inputs are negative. Leaky ReLU, unlike ReLU, defines a small linear component of x when x is a negative value. With this change in leaky ReLU, the gradient can be of non-zero value instead of zero thus avoiding dead neurons. However, this might also bring in a challenge with Leaky ReLU when it comes to predicting negative values.  Exponential Linear Unit (ELU) is another variant of ReLU, which unlike ReLU and leaky ReLU, uses a log curve instead of a straight line to define the negative values. Swish Activation Function: Swish is a new activation function that has been proposed by Google Brain. While ReLU returns zero for negative values, Swish doesn’t return a zero for negative inputs. Swish is a self-gating technique which implies that while normal gates require multiple scalar inputs, self-gating technique requires a single input only. Swish has certain properties - Unlike ReLU, Swish is a smooth and non-monotonic function which makes it more acceptable compared to ReLU. Swish is unbounded above and bounded below.  Swish is represented as x · σ(βx), where σ(z) = (1 + exp(−z))−1 is the sigmoid function and β is a constant or a trainable parameter.  Activation functions in deep learning and the vanishing gradient descent problem Gradient based methods are used by various algorithms to train the models. Neural networks algorithm uses stochastic gradient descent method to train the model. A neural network algorithm randomly assigns weights to the layers and once the output is predicted, it calculates the prediction errors. It uses these errors to estimate a gradient that can be used to update the weights in the network. This is done in order to reduce the prediction errors. The error gradient is updated backward from the output layer to the input layer.  It is preferred to build a neural network model with a larger number of hidden layers. With more hidden layers, the neural network model can achieve enhanced capability to perform more accurately.  One problem with too many layers is that the gradient diminishes pretty fast as it moves from the output layer to the input layer, i.e. during the back propagation. By the time it reaches the other end backward, it is quite possible that the error might get too small to make any effect on the model performance improvement. Basically, this is a situation where some difficulty is faced while training a neural network model using gradient based methods.  This is known as the vanishing gradient descent problem. Gradient based methods might face this challenge when certain activation functions are used in the network.  In deep neural networks, various activations functions are used. However when training deep neural network models, the vanishing gradient descent problems can demonstrate unstable behavior.  Various workaround solutions have been proposed to solve this problem. The most commonly used activation function is the ReLU activation function that has proven to perform way better than any other previously existing activation functions like sigmoid or hyperbolic tangent. As mentioned above, Swish improves upon ReLU being a smooth and non-monotonic function. However, though the vanishing gradient descent problem is much less severe in Swish, it does not completely avoid the vanishing gradient descent problem. To tackle this problem, a new activation function has been proposed. “The activation function in the neural network is one of the important aspects which facilitates the deep training by introducing the nonlinearity into the learning process. However, because of zero-hard rectification, some of the existing activation functions such as ReLU and Swish miss to utilize the large negative input values and may suffer from the dying gradient problem. Thus, it is important to look for a better activation function which is free from such problems.... The proposed LiSHT activation function is an attempt to scale the non-linear Hyperbolic Tangent (Tanh) function by a linear function and tackle the dying gradient problem… A very promising performance improvement is observed on three different types of neural networks including Multi-layer Perceptron (MLP), Convolutional Neural Network (CNN) and Recurrent Neural Network like Long-short term memory (LSTM).“   - Swalpa Kumar Roy, Suvojit Manna, et al, Jan 2019 In a paper published here, Swalpa Kumar Roy, Suvojit Manna, et al proposes a new non-parametric activation function - the Linearly Scaled Hyperbolic Tangent (LiSHT) - for Neural Networks that attempts to tackle the vanishing gradient descent problem. 
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Activation Functions for Deep Neural Networks

The Universal Approximation Theorem Any predictiv... Read More

Top Job Roles With Their Salary Data in the World of Data Science for 2020–2021

Data Science requires the expertise of professionals who possess the skill of collecting, structuring, storing, handling and analyzing data, allowing individuals and organizations to make decisions based on insights generated from the data. Data science is woven into the fabric of our daily lives in myriad ways that we may not even be aware of; starting from the online purchases we make, our social media feeds, the music we listen to or even the movie recommendations that we are shown online.  For several years in a row, the job of a data scientist has been hailed as the “hottest job of the 21st century”. Data scientists are among the highest paid resources in the IT industry. According to Glassdoor, the average data scientist’s salary is $113,436. With the growth of data, the demand for data science job roles in companies has been rising at an accelerated pace.  How Data Science is a powerful career choice The landscape of a data science job is promising and full of opportunities spanning different industries. The nature of the job allows an individual to take on flexible remote jobs and also to be self-employed.  The field of data science has grown exponentially in a very short time, as companies have come to realize the importance of gathering huge volumes of data from websites, devices, social media platforms and other sources, and using them for business benefits. Once the data is made available, data scientists use their analytical skills, evaluate data and extract valuable information that allows organizations to enhance their innovations. A data scientist is responsible for collecting, cleansing, modifying and analyzing data into meaningful insights. In the first phase of their career, a data scientist generally works as a statistician or data analyst. Over many years of experience, they evolve to be data scientists.  The ambit of data has been increasing rapidly which has urged companies to actively recruit data scientists to harness and leverage insights from the huge quantities of valuable data available, enabling efficiency in processes and operations and driving sales and growth.  In the future, data may also emerge as the turning point of the world economy. So, pursuing a career in data science would be very useful for a computer enthusiast, not only because it pays well but also since it is the new trend in IT. According to the Bureau of Labor Statistics (BLS), jobs for computer and information research scientists, as well as data scientists are expected to grow by 14 percent by the year 2028. Who is a Data Scientist & What Do They Do? Data Scientists are people with integral analytical data expertise together with complex problem-solving skills, besides the curiosity to explore a wide range of emerging issues.  They are considered to be the best of both the sectors – IT and business, which makes them extremely skilled individuals whose job roles straddle the worlds of computer science, statistics, and trend analysis. Because of this surging demand for data identification and analysis in various tech fields like AI, Machine Learning, and Data Science, the salary of a data scientist is one of the highest in the world. Requisite skills for a data scientist Before we see the different types of jobs in the data analytics field, we must be aware of the prerequisite skills that make up the foundation of a data scientist: Understanding of data – As the name suggests, Data Science is all about data. You need to understand the language of data and the most important question you must ask yourself is whether you love working with data and crunching numbers. And if your answer is “yes”, then you’re on the right track. Understanding of algorithms or logic – Algorithms are a set of instructions that are given to a computer to perform a particular task. All Machine Learning models are based on algorithms, so it is quite an essential prerequisite for a would-be data scientist to understand the logic behind it.  Understanding of programming – To be an expert in data science, you do not need to be an expert coder. However, you should have the foundational programming knowledge which includes variables, constants, data types, conditional statements, IO functions, client/server, Database, API, hosting, etc. If you feel comfortable working with these and you have your coding skills sorted, then you’re good to go. Understanding of Statistics – Statistics is one of the most significant areas in the field of Data Science. You should be well aware of terminologies such as mean, median, mode, standard deviation, distribution, probability, Bayes’ theorem, and different Statistical tests like hypothesis testing, chi-square, ANOVA, etc. Understanding of Business domain:  If you do not have an in-depth working knowledge of the business domain, it will not really prove to be an obstacle in your journey of being a data scientist. However, if you have the primitive understanding of the specific business area you are working for, it will be an added advantage that can take you ahead. Apart from all the above factors, you need to have good communication skills which will help the entire team to get on the same page and work well together.Data Science Job Roles  Data science experts are in demand in almost every job sector, and are not confined to the IT industry alone.  Let us look at some major job roles, associated responsibilities , and the salary range: 1. Data ScientistsA Data Scientist’s job is as exciting as it is rewarding. With the help of Machine Learning, they handle raw data and analyze it with various algorithms such as regression, clustering, classification, and so on. They are able to arrive at insights that are essential for predicting and addressing complex business problems.  Responsibilities of Data Scientists The responsibilities of Data Scientists are outlined below: Collecting huge amounts of organized and unorganized data and converting them into useful insights. Using analytical skills like text analytics, machine learning, and deep learning to identify potential solutions which will help in the growth of organizations. Following a data-driven approach to solve complex problems.  Enhancing data accuracy and efficiency by cleansing and validating data. Using data visualization to communicate significant observations to the organization’s stakeholders. Data Scientists Salary Range According to Glassdoor, the average Data Scientist salary is $113,436 per annum. The median salary of an entry-level professional can be around $95,000 per annum. However, early level data scientists with 1 to 4 years' experience can get around $128,750 per annum while the median salary for those with more experience ranging around 5 to 9 years  can rise to an average of $165,000 per annum. 2. Data Engineers  A Data Engineer is the one who is responsible for building a specific software infrastructure for data scientists to work. They need to have in-depth knowledge of technologies like Hadoop and Big Data such as MapReduce, Hive, and SQL. Half of the work of Data Engineers is Data Wrangling and it is advantageous if they have a software engineering background. Responsibilities of Data Engineers  The responsibilities of Data Engineers are described below: Collecting data from different sources and then consolidating and cleansing it. Developing essential software for extracting, transforming, and loading data using SQL, AWS, and Big Data. Building data pipelines using machine learning algorithms and statistical techniques. Developing innovative ways to enhance data efficiency and quality. Developing, testing and maintaining data architecture. Required Skills for Data Engineers  There are certain skill sets that data engineers need to have: Strong skills in analytics to manage and work with massive unorganized datasets. Powerful programming skills in trending languages like Python, Java, C++, Ruby, etc. Strong knowledge of database software like SQL and experience in relational databases. Managerial and organizational skills along with fluency in various databases.  Data Engineers’ Salary Range According to Glassdoor, the average salary of a Data Engineer is $102,864 in the USA. Reputed companies like Amazon, Airbnb, Spotify, Netflix, IBM value and pay high salaries to data engineers. Entry-level data and mid-range data engineers get an average salary between $110,000 and $137,770 per annum. However, with experience, a data engineer can get up to $155,000 in a year. 3. Data Analyst As the name suggests, the job of a Data Analyst is to analyze data. A data analyst collects, processes, and executes statistical data analyses which help business users to develop meaningful insights. This process requires creating systems using programming languages like Python, R or SAS. Companies ranging from IT, healthcare, automobile, finance, insurance employ Data Analysts to run their businesses efficiently.  Responsibilities of Data Analysts  The responsibilities of Data Analysts are described below: Identifying correlations and gathering valuable patterns through data mining and analyzing data. Working with customer-centric algorithms and modifying them to suit individual customer demands. Solving certain business problems by mapping data from numerous sources and tracing them. Creating customized models for customer-centric market strategies, customer tastes, and preferences. Conducting consumer data research and analytics by deploying statistical analysis. Data Analyst Salary Range According to Glassdoor, the national average salary of a Data Analyst is $62,453 in the United States. The salaries of an entry-level data analyst start at  $34,5000 per year or $2875 per month.  Glassdoor states that a junior data analyst earns around $70,000 per year and experienced senior data analysts can expect to be paid around $107,000 per year which is roughly $8916 per month. Key Reasons to Become a Data Scientist Becoming a Data Scientist is a dream for many data enthusiasts. There are some basic reasons for this: 1. Highly in-demand field The job of Data Science is hailed as one of the most sought after jobs for 2020 and according to an estimate, it is predicted that this field would generate around 11.5 million jobs by the year 2026. The demand for expertise in data science is increasing while the supply is too low.  This shortage of qualified data scientists has escalated their demand in the market. A survey by the MIT Sloan Management Review indicates that 43 percent of companies report that a major challenge to their growth has been a lack of data analytic skills. 2. Highly Paid & Diverse Roles Since data analytics form the central part of decision-making, companies are willing to hire larger numbers of data scientists who can help them to make the right decisions that will boost business growth. Since it is a less saturated area with a mid-level supply of talents, various opportunities have emerged that require diverse skill sets. According to Glassdoor, in the year 2016,  data science was the highest-paid field across industries. 3. Evolving workplace environments With the arrival of technologies like Artificial Intelligence and Robotics which fall under the umbrella of data science, a vast majority of manual tasks have been replaced with automation.  Machine Learning has made it possible to train machines to perform repetitive tasks , freeing up humans to focus on critical problems that need their attention. Many new and exciting technologies have emerged within this field such as Blockchain, Edge Computing, Serverless Computing, and others.  4. Improving product standards The rigorous use of Machine Learning algorithms for regression, classification recommendation problems like decision trees, random forest, neural networks, naive Bayes etc has boosted the customer experiences that companies desire to have. One of the best examples of such development is the E-commerce sites that use intelligent Recommendation Systems to refer products and provide customer-centric insights depending upon their past purchases. Data Scientists serve as a trusted adviser to such companies by identifying the preferred target audience and handling marketing strategies. 5. Helping the world In today’s world, almost everything revolves around data. Data Scientists extract hidden information from massive lumps of data which helps in decision making across industries ranging from finance and healthcare to manufacturing, pharma and engineering . Organizations are equipped with data driven insights that boost productivity and enhance growth, even as they optimize resources and mitigate potential risks. Data Science catalyzes innovation and research, bringing positive changes across the world we live in. Factors Affecting a Data Scientist’s Salary The salaries of Data Scientists can depend upon several factors. Let us study them one by one and understand their significance: Data Scientist Salary by Location The number of job opportunities and the national data scientist salary for data innovators is the highest in Switzerland in the year 2020, followed by the Netherlands and United Kingdom. However, since Silicon Valley in the United States is the hub of new technological innovations, it is considered to generate the most jobs for startups in the world, followed by Bangalore in India. A data scientist’s salary in Silicon Valley or Bangalore is likely to be higher than in other countries. Below are the highest paying countries for data scientist roles along with their average annual data science salary: Switzerland$115,475Netherlands$68,880Germany$64,024United Kingdom$59,781Spain$30,050Italy$37,785Data Scientist Salary by ExperienceA career in the field of data science is very appealing to young IT professionals. Starting salaries are very lucrative, and there is incremental growth in salary  with  experience. Salaries of a data scientist depend on the expertise, as well as the years of experience: Entry-level data scientist salary – The median entry-level salary for a data scientist is around $95,000 per year which is quite high. Mid-level data scientist salary –   The median salary for a mid-level data scientist having experience of around 1 - 4 years is $128,750 per year. If the data scientist is in a managerial position, the average salary rises upto $185,000 per year. Experienced data scientist salary –  The median salary for an experienced data scientist having experience of around 5 - 9 years is $128,750 per year whereas the median salary of an experienced manager is much higher; around $250,000 per year. Data Scientist Salary by Skills There are some core competencies that will help you to shine in your career as a Data Scientist, and if you want to get the edge over your peers you should consider polishing up these skills: Python is the most crucial and coveted skill which data scientists must be familiar with, followed by R. The average salary in the US for  Python programmers is $120,365 per annum. If you are well versed in both Data Science and Big Data, instead of just one among them, your salary is likely to increase by at least 25 percent . The users of innovative technology like the Statistical Analytical System get a salary of around $77,842. On the other hand, users of software analysis software like SPSS have a pay scale of  $61,452 per year. Machine Learning Engineers on the average earn around $111,855 per year. However, with more experience in Machine Learning along with knowledge in Python, you can earn around $146,085 per annum. A Data Scientist with domain knowledge of Artificial Intelligence can earn an annual salary between $100,000 to $150,000. Extra skills in programming and innovative technologies have always been a value-add that can enhance your employability. Pick skills that are in-demand to see your career graph soar. Data Scientist Salary by Companies Some of the highest paying companies in the field of Data Science are tech giants like Facebook, Amazon, Apple, and service companies like McGuireWoods, Netflix or Airbnb.  Below is a list of top companies with the highest paying salaries: McGuireWoods$165,114Amazon$164,114Airbnb$154,879Netflix$147,617 Apple$144,490Twitter$144,341Walmart$144,198Facebook$143,189eBay$143,005Salaries of Other Related Roles Various other job roles associated with Data Science are also equally exciting and rewarding. Let us look at some of them and their salaries: Machine Learning Engineer$114,826Machine Learning Scientist$114,121Applications Architect$113,757Enterprise Architect$110,663Data Architect$108,278Infrastructure Architect$107,309Business Intelligence Developer$81,514Statistician$76,884Conclusion Let us look at what we have learned in this article so far: What is Data Science? The job of a Data Scientist Pre-requisite skills for a Data Scientist Different job roles Key reasons for becoming a Data Scientist Salary depending upon different factors Salary of other related roles The field of Data Science is ripe in terms of opportunities for Data Scientists, Data Engineers, and Data Analysts. The figures mentioned in this article are not set in stone and may vary depending upon the skills you possess, experience you have and various other factors. With more experience and skills, your salary is bound to increase by a certain percentage every year. Data science is a field that will revolutionize the world in the coming years and you can have a share of this very lucrative pie with the right education qualifications, skills, experience and training.  
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