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A Peek Into the World of Data Science

Touted as the sexiest job in the 21st century, back in 2012 by Harvard Business Review, the data science world has since received a lot of attention across the entire world, cutting across industries and fields. Many people wonder what the fuss is all about. At the same time, others have been venturing into this field and have found their calling.  Eight years later, the chatter about data science and data scientists continues to garner headlines and conversations. Especially with the current pandemic, suddenly data science is on everyone’s mind. But what does data science encompass? With the current advent of technology, there are terabytes upon terabytes of data that organizations collect daily. From tracking the websites we visit - how long, how often - to what we purchase and where we go - our digital footprint is an immense source of data for a lot of businesses. Between our laptops, smartphones and our tablets - almost everything we do translates into some form of data.  On its own, this raw data will be of no use to anyone. Data science is the process that repackages the data to generate insights and answer business questions for the organization. Using domain understanding, programming and analytical skills coupled together with business sense and know-how, existing data is converted to provide actionable insights for an organization to drive business growth. The processed data is what is worth its weight in gold. By using data science, we can uncover existing insights and behavioural patterns or even predict future trends.  Here is where our highly-sought-after data scientists come in.  A data scientist is a multifaceted role in an organization. They have a wide range of knowledge as they need to marry a plethora of methods, processes and algorithms with computer science, statistics and mathematics to process the data in a format that answers the critical business questions meaningfully and with actionable insights for the organization. With these actionable data, the company can make plans that will be the most profitable to drive their business goals.  To churn out the insights and knowledge that everyone needs these days, data science has become more of a craft than a science despite its name. The data scientists need to be trained in mathematics yet have some creative and business sense to find the answers they are looking in the giant haystack of raw data. They are the ones responsible for helping to shape future business plans and goals.  It sounds like a mighty hefty job, doesn’t it? It is also why it is one of the most sought after jobs these days. The field is rapidly evolving, and keeping up with the latest developments takes a lot of dedication and time, in order to produce actionable data that the organizations can use.  The only constant through this realm of change is the data science project lifecycle. We will discuss briefly below on the critical areas of the project lifecycle. The natural tendency is to envision that it is a circular process immediately - but there will be a lot of working back and forth within some phases to ensure that the project runs smoothly.  Stage One: Business Understanding  As a child, were you one of those children that always asked why? Even when the adults would give you an answer, you followed up with a “why”? Those children will have probably grown up to be data scientists as it seems, their favourite question is: Why? By asking the why - they will get to know the problem that needs to be solved and the critical question will emerge. Once there is a clear understanding of the business problem and question, then the work can begin. Data scientists want to ensure that the insights that come from this question are supported by data and will allow the business to achieve the desired results. Therefore, the foundation stone to any data science project is in understanding the business.  Stage Two: Data Understanding  Once the problem and question have been confirmed, you need to start laying out the objectives of this project by determining the required variables to be predicted. You must know what you need from the data and what the data should address. You must collate all the information and data, which can be reasonably difficult. An agreement over the sources and the requirements of the data characteristics needs to be reached before moving forward.  Through this process, an efficient and insightful understanding is required of how the data can and will be used for the project. This operational management of the data is vital, as the data that is sourced at this stage will define the project and how effective the solutions will be in the end.  Stage Three: Data Preparation  It has been said quite often that a bulk of a data scientist’s time is spent in preparing the data for use. In this report from CrowdFlower in 2016, the percentage of time spent on cleaning and organizing data is pegged at 60%. That is more than half their day!  Since data comes in various forms, and from a multitude of sources, there will be no standardization or consistency throughout the data. Raw data needs to be managed and prepared - with all the incomplete values and attributes fixed, and all deconflicting values in the data eliminated. This process requires human intervention as you must be able to discern which data values are required to reach your end goal. If the data is not prepared according to the business understanding, the final result might not be suitable to address the issue.  Stage Four: Modeling Once the tedious process of preparation is over, it is time to get the results that will be required for this project lifecycle. There are various types of techniques that can be used, ranging from decision-tree building to neural network generation. You must decide which would be the best technique based on the question that needs to be answered. If required, multiple modeling techniques can be used; where each task must be performed individually. Generally, modeling techniques are applied more than once (per process), and there will be more than one technique used per project.  With each technique, parameters must be set based on specific criteria. You, as the data scientist, must apply your knowledge to judge the success of the modeling and rank the models used based on the results; according to pre-set criteria. Stage Five: Evaluation Once the results are churned out and extracted, we then need to refer back to the business query that we talked about in Stage One and decide if it answers the question raised; and if the model and data meet the objectives that the data science project has set out to address. The evaluation also can unveil other results that are not related to the business question but are good points for future direction or challenges that the organization might face. These results should be tabled for discussion and used for new data science projects. Final Stage: Deployment  This is almost the finishing line!  Now with the evaluated results, the team would need to sit down and have an in-depth discussion on what the data shows and what the business needs to do based on the data. The project team should come up with a suitable plan for deployment to address the issue. The deployment will still need to be monitored and assessed along the way to ensure that the project will be a successful one; backed by data.  The assessment would normally restart the project lifecycle; bringing you full circle.  Data is everywhere  In this day and age, we are surrounded by a multitude of data science applications as it crosses all industries. We will focus on these five industries, where data science is making waves. Banking & Finance  Financial institutions were the earliest adopters of data analytics, and they are all about data! From using data for fraud or anomaly detection in their banking transactions to risk analytics and algorithmic trading - one will find data plays a key role in all levels of a financial institution.  Risk analytics is one of the key areas where data science is used; as financial institutions depend on it to make strategic decisions for the financial health of the business. They need to assess each risk to manage and optimize their cost.  Logistics & Transportation  The world of logistics is a complex one. In a production line, raw materials sometimes come from all over the world to create a single product. A delay of any of the parts will affect the production line, and the output of stock will be affected drastically. If logistical delays can be predicted, the company can adjust quickly to another alternative to ensure that there will be no gap in the supply chain, ensuring that the production line will function at optimum efficiency.  Healthcare  2020 has been an interesting one. It has been a battle of a lifetime for many of us. Months have passed, and yet the virus still rages on to wreak havoc on lives and economies. Many countries have turned to data science applications to help with their fight against COVID-19. With so much data generated daily, people and governments need to know various things such as:  Epidemiological clusters so people can be quarantined to stop the spread of the virus tracking of symptoms over thousands of patients to understand how the virus transmits and mutates to find vaccines and  solutions to mitigate transmission. Manufacturing  In this field, millions can be on the line each day as there are so many moving parts that can cause delays, production issues, etc. Data science is primarily used to boost production rates, reduce cost (workforce or energy), predict maintenance and reduce risks on the production floor.  This allows the manufacturer to make plans to ensure that the production line is always operating at the optimum level, providing the best output at any given time.  Retail (Brick & Mortar, Online)  Have you ever wondered why some products in a shop are placed next to each other or how discounts on items work? All those are based on data science.  The retailers track people’s shopping routes, purchases and basket matching to work out details like where products should be placed; or what should go on sale and when to drive up the sales for an item. And that is just for the instore purchases.  Online data tracks what you are buying and suggests what you might want to buy next based on past purchase histories; or even tells you what you might want to add to your cart. That’s how your online supermarket suggests you buy bread if you have a jar of peanut butter already in your cart.  As a data scientist, you must always remember that the power in the data. You need to understand how the data can be used to find the desired results for your organization. The right questions must be asked, and it has become more of an art than a science. Image Source: Data science Life Cycle

A Peek Into the World of Data Science

10K
A Peek Into the World of Data Science

Touted as the sexiest job in the 21st century, back in 2012 by Harvard Business Review, the data science world has since received a lot of attention across the entire world, cutting across industries and fields. Many people wonder what the fuss is all about. At the same time, others have been venturing into this field and have found their calling.  

Eight years later, the chatter about data science and data scientists continues to garner headlines and conversations. Especially with the current pandemic, suddenly data science is on everyone’s mind. But what does data science encompass? 

With the current advent of technology, there are terabytes upon terabytes of data that organizations collect daily. From tracking the websites we visit - how long, how often - to what we purchase and where we go - our digital footprint is an immense source of data for a lot of businesses. Between our laptops, smartphones and our tablets - almost everything we do translates into some form of data.  

On its own, this raw data will be of no use to anyone. Data science is the process that repackages the data to generate insights and answer business questions for the organization. Using domain understanding, programming and analytical skills coupled together with business sense and know-how, existing data is converted to provide actionable insights for an organization to drive business growth. The processed data is what is worth its weight in gold. By using data science, we can uncover existing insights and behavioural patterns or even predict future trends.  

Here is where our highly-sought-after data scientists come in.  

A data scientist is a multifaceted role in an organization. They have a wide range of knowledge as they need to marry a plethora of methods, processes and algorithms with computer science, statistics and mathematics to process the data in a format that answers the critical business questions meaningfully and with actionable insights for the organization. With these actionable data, the company can make plans that will be the most profitable to drive their business goals.  

To churn out the insights and knowledge that everyone needs these days, data science has become more of a craft than science despite its name. The data scientists need to be trained in mathematics yet have some creative and business sense to find the answers they are looking in the giant haystack of raw data. They are the ones responsible for helping to shape future business plans and goals.  

It sounds like a mighty hefty job, doesn’t it? It is also why it is one of the most sought after jobs these days. The field is rapidly evolving, and keeping up with the latest developments takes a lot of dedication and time, in order to produce actionable data that the organizations can use.  

The only constant through this realm of change is the data science project lifecycle. We will discuss briefly below on the critical areas of the project lifecycle. The natural tendency is to envision that it is a circular process immediately - but there will be a lot of working back and forth within some phases to ensure that the project runs smoothly.  

Stage One: Business Understanding  

As a child, were you one of those children that always asked why? Even when the adults would give you an answer, you followed up with a “why”? Those children will have probably grown up to be data scientists as it seems, their favourite question is: Why? 

By asking the why - they will get to know the problem that needs to be solved and the critical question will emerge. Once there is a clear understanding of the business problem and question, then the work can begin. Data scientists want to ensure that the insights that come from this question are supported by data and will allow the business to achieve the desired results. Therefore, the foundation stone to any data science project is in understanding the business.  

Stage Two: Data Understanding  

Once the problem and question have been confirmed, you need to start laying out the objectives of this project by determining the required variables to be predicted. You must know what you need from the data and what the data should address. You must collate all the information and data, which can be reasonably difficult. An agreement over the sources and the requirements of the data characteristics needs to be reached before moving forward.  

Through this process, an efficient and insightful understanding is required of how the data can and will be used for the project. This operational management of the data is vital, as the data that is sourced at this stage will define the project and how effective the solutions will be in the end.  

Stage Three: Data Preparation  

It has been said quite often that a bulk of a data scientist’s time is spent in preparing the data for use. In this report from CrowdFlower in 2016, the percentage of time spent on cleaning and organizing data is pegged at 60%. That is more than half their day!  

Since data comes in various forms, and from a multitude of sources, there will be no standardization or consistency throughout the data. Raw data needs to be managed and prepared - with all the incomplete values and attributes fixed, and all deconflicting values in the data eliminated. This process requires human intervention as you must be able to discern which data values are required to reach your end goal. If the data is not prepared according to the business understanding, the final result might not be suitable to address the issue.  

Stage Four: Modeling 

Once the tedious process of preparation is over, it is time to get the results that will be required for this project lifecycle. There are various types of techniques that can be used, ranging from decision-tree building to neural network generation. You must decide which would be the best technique based on the question that needs to be answered. If required, multiple modeling techniques can be used; where each task must be performed individually. Generally, modeling techniques are applied more than once (per process), and there will be more than one technique used per project.  

With each technique, parameters must be set based on specific criteria. You, as the data scientist, must apply your knowledge to judge the success of the modeling and rank the models used based on the results; according to pre-set criteria. 

Stage Five: Evaluation 

Once the results are churned out and extracted, we then need to refer back to the business query that we talked about in Stage One and decide if it answers the question raised; and if the model and data meet the objectives that the data science project has set out to address. 

The evaluation also can unveil other results that are not related to the business question but are good points for future direction or challenges that the organization might face. These results should be tabled for discussion and used for new data science projects. 

Final Stage: Deployment  

This is almost the finishing line!  

Now with the evaluated results, the team would need to sit down and have an in-depth discussion on what the data shows and what the business needs to do based on the data. The project team should come up with a suitable plan for deployment to address the issue. The deployment will still need to be monitored and assessed along the way to ensure that the project will be a successful one; backed by data.  

The assessment would normally restart the project lifecycle; bringing you full circle.  

Data is everywhere  

In this day and age, we are surrounded by a multitude of data science applications as it crosses all industries. We will focus on these five industries, where data science is making waves. 

Banking & Finance  

Financial institutions were the earliest adopters of data analytics, and they are all about data! From using data for fraud or anomaly detection in their banking transactions to risk analytics and algorithmic trading - one will find data plays a key role in all levels of a financial institution.  

Risk analytics is one of the key areas where data science is used; as financial institutions depend on it to make strategic decisions for the financial health of the business. They need to assess each risk to manage and optimize their cost.  

Logistics & Transportation  

The world of logistics is a complex one. In a production line, raw materials sometimes come from all over the world to create a single product. A delay of any of the parts will affect the production line, and the output of stock will be affected drastically. If logistical delays can be predicted, the company can adjust quickly to another alternative to ensure that there will be no gap in the supply chain, ensuring that the production line will function at optimum efficiency.  

Healthcare  

2020 has been an interesting one. It has been a battle of a lifetime for many of us. Months have passed, and yet the virus still rages on to wreak havoc on lives and economies. Many countries have turned to data science applications to help with their fight against COVID-19. 

With so much data generated daily, people and governments need to know various things such as:  

  • Epidemiological clusters so people can be quarantined to stop the spread of the virus 
  • tracking of symptoms over thousands of patients to understand how the virus transmits and mutates to find vaccines and  
  • solutions to mitigate transmission. 

Manufacturing  

In this field, millions can be on the line each day as there are so many moving parts that can cause delays, production issues, etc. Data science is primarily used to boost production rates, reduce cost (workforce or energy), predict maintenance and reduce risks on the production floor.  

This allows the manufacturer to make plans to ensure that the production line is always operating at the optimum level, providing the best output at any given time.  

Retail (Brick & Mortar, Online)  

Have you ever wondered why some products in a shop are placed next to each other or how discounts on items work? All those are based on data science.  

The retailers track people’s shopping routes, purchases and basket matching to work out details like where products should be placed; or what should go on sale and when to drive up the sales for an item. And that is just for the instore purchases.  

Online data tracks what you are buying and suggests what you might want to buy next based on past purchase histories; or even tells you what you might want to add to your cart. That’s how your online supermarket suggests you buy bread if you have a jar of peanut butter already in your cart.  

As a data scientist, you must always remember that the power in the data. You need to understand how the data can be used to find the desired results for your organization. The right questions must be asked, and it has become more of an art than a science. 

Data science Life Cycle

Image Source: Data science Life Cycle

Felicia

Felicia Norfor

Author

With over 15 years of experience in the marketing (digital and physical), communications and content creation fields, she has been dipping her toes into the data world lately. Working alongside analytics people is an eye-opening experience, making her realise how essential data teams are and wonders how she has lived without them for so long.

She currently provides consultancy services for marketing, communications and engagement events, while marrying data extensively into her projects. Otherwise, she can be found at knees deep, learning from these data peeps at Algoritmo Lab, a data science consultancy start-up with offices in Singapore and India.

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This term was coined in the year 1997. “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at the tasks improves with the experiences.”, as defined in the book on ML written by Mitchell in 1997. The difference between a traditional programming and programming using Machine Learning is depicted here, the first Approach (a) is a traditional approach, and second approach (b) is a Machine Learning based approach.Machine Learning encompasses the techniques in AI which allow the system to learn automatically looking at the data available. While learning, the system tries to improve the experience without making any explicit efforts in programming. Any machine learning application follows the following steps broadlySelecting the training datasetAs the definition indicates, machine learning algorithms require past experience, that is data, for learning. So, selection of appropriate data is the key for any machine learning application.Preparing the dataset by preprocessing the dataOnce the decision about the data is made, it needs to be prepared for use. Machine learning algorithms are very susceptible to the small changes in data. To get the right insights, data must be preprocessed which includes data cleaning and data transformation.  Exploring the basic statistics and properties of dataTo understand what the data wishes to convey, the data engineer or Machine Learning engineer needs to understand the properties of data in detail. These details are understood by studying the statistical properties of data. Visualization is an important process to understand the data in detail.Selecting the appropriate algorithm to apply on the datasetOnce the data is ready and understood in detail, then appropriate Machine Learning algorithms or models are selected. The choice of algorithm depends on characteristics of data as well as type of task to be performed on the data. The choice also depends on what kind of output is required from the data.Checking the performance and fine-tuning the parameters of the algorithmThe model or algorithm chosen is fine-tuned to get improved performance. If multiple models are applied, then they are weighed against the performance. The final algorithm is again fine-tuned to get appropriate output and performance.Why Pursue a Career in Machine Learning in 2021?A recent survey has estimated that the jobs in AI and ML have grown by more than 300%. Even before the pandemic struck, Machine Learning skills were in high demand and the demand is expected to increase two-fold in the near future.A career in machine learning gives you the opportunity to make significant contributions in AI, the future of technology. All the big and small businesses are adopting Machine Learning models to improve their bottom-line margins and return on investment.  The use of Machine Learning has gone beyond just technology and it is now used in diverse industries including healthcare, automobile, manufacturing, government and more. This has greatly enhanced the value of Machine Learning experts who can earn an average salary of $112,000.  Huge numbers of jobs are expected to be created in the coming years.  Here are a few reasons why one should pursue a career in Machine Learning:The global machine learning market is expected to touch $20.83B in 2024, according to Forbes.  We are living in a digital age and this explosion of data has made the use of machine learning models a necessity. Machine Learning is the only way to extract meaning out of data and businesses need Machine Learning engineers to analyze huge data and gain insights from them to improve their businesses.If you like numbers, if you like research, if you like to read and test and if you have a passion to analyse, then machine learning is the career for you. Learning the right tools and programming languages will help you use machine learning to provide appropriate solutions to complex problems, overcome challenges and grow the business.Machine Learning is a great career option for those interested in computer science and mathematics. They can come up with new Machine Learning algorithms and techniques to cater to the needs of various business domains.As explained above, a career in machine learning is both rewarding and lucrative. There are huge number of opportunities available if you have the right expertise and knowledge. On an average, Machine Learning engineers get higher salaries, than other software developers.Years of experience in the Machine Learning domain, helps you break into data scientist roles, which is not just among the hottest careers of our generation but also a highly respected and lucrative career. Right skills in the right business domain helps you progress and make a mark for yourself in your organization. For example, if you have expertise in pharmaceutical industries and experience working in Machine learning, then you may land job roles as a data scientist consultant in big pharmaceutical companies.Statistics on Machine learning growth and the industries that use MLAccording to a research paper in AI Multiple (https://research.aimultiple.com/ml-stats/), the Machine Learning market will grow to 9 Billion USD by the end of 2022. There are various areas where Machine Learning models and solutions are getting deployed, and businesses see an overall increase of 44% investments in this area. North America is one of the leading regions in the adoption of Machine Learning followed by Asia.The Global Machine Learning market will grow by 42% which is evident from the following graph. Image sourceThere is a huge demand for Machine Learning modelling because of the large use of Cloud Based Applications and Services. The pandemic has changed the face of businesses, making them heavily dependent on Cloud and AI based services. Google, IBM, and Amazon are just some of the companies that have invested heavily in AI and Machine Learning based application development, to provide robust solutions for problems faced by small to large scale businesses. Machine Learning and Cloud based solutions are scalable and secure for all types of business.ML analyses and interprets data patterns, computing and developing algorithms for various business purposes.Advantages of Machine Learning courseNow that we have established the advantages of perusing a career in Machine Learning, let’s understand from where to start our machine learning journey. The best option would be to start with a Machine Learning course. There are various platforms which offer popular Machine Learning courses. One can always start with an online course which is both effective and safe in these COVID times.These courses start with an introduction to Machine Learning and then slowly help you to build your skills in the domain. Many courses even start with the basics of programming languages such as Python, which are important for building Machine Learning models. Courses from reputed institutions will hand hold you through the basics. Once the basics are clear, you may switch to an offline course and get the required certification.Online certifications have the same value as offline classes. They are a great way to clear your doubts and get personalized help to grow your knowledge. These courses can be completed along with your normal job or education, as most are self-paced and can be taken at a time of your convenience. There are plenty of online blogs and articles to aid you in completion of your certification.Machine Learning courses include many real time case studies which help you in understanding the basics and application aspects. Learning and applying are both important and are covered in good Machine Learning Courses. So, do your research and pick an online tutorial that is from a reputable institute.What Does the Career Path in Machine Learning Look Like?One can start their career in Machine Learning domain as a developer or application programmer. But the acquisition of the right skills and experience can lead you to various career paths. Following are some of the career options in Machine Learning (not an exhaustive list):Data ScientistA data scientist is a person with rich experience in a particular business field. A person who has a knowledge of domain, as well as machine learning modelling, is a data scientist. Data Scientists’ job is to study the data carefully and suggest accurate models to improve the business.AI and Machine Learning EngineerAn AI engineer is responsible for choosing the proper Machine Learning Algorithm based on natural language processing and neural network. They are responsible for applying it in AI applications like personalized advertising.  A Machine Learning Engineer is responsible for creating the appropriate models for improvement of the businessData EngineerA Data Engineer, as the name suggests, is responsible to collect data and make it ready for the application of Machine Learning models. Identification of the right data and making it ready for extraction of further insights is the main work of a data engineer.Business AnalystA person who studies the business and analyzes the data to get insights from it is a Business Analyst. He or she is responsible for extracting the insights from the data at hand.Business Intelligence (BI) DeveloperA BI developer uses Machine Learning and Data Analytics techniques to work on a large amount of data. Proper representation of data to suit business decisions, using the latest tools for creation of intuitive dashboards is the role of a BI developer.  Human Machine Interface learning engineerCreating tools using machine learning techniques to ease the human machine interaction or automate decisions, is the role of a Human Machine Interface learning engineer. This person helps in generating choices for users to ease their work.Natural Language Processing (NLP) engineer or developerAs the name suggests, this person develops various techniques to process Natural Language constructs. Building applications or systems using machine learning techniques to build Natural Language based applications is their main task. They create multilingual Chatbots for use in websites and other applications.Why are Machine Learning Roles so popular?As mentioned above, the market growth of AI and ML has increased tremendously over the past years. The Machine Learning Techniques are applied in every domain including marketing, sales, product recommendations, brand retention, creating advertising, understanding the sentiments of customer, security, banking and more. Machine learning algorithms are also used in emails to ease the users work. This says a lot, and proves that a career in Machine Learning is in high demand as all businesses are incorporating various machine learning techniques and are improving their business.One can harness this popularity by skilling up with Machine Learning skills. Machine Learning models are now being used by every company, irrespective of their size--small or big, to get insights on their data and use these insights to improve the business. As every company wishes to grow faster, they are deploying more machine learning engineers to get their work done on time. Also, the migration of businesses to Cloud services for better security and scalability, has increased their requirement for more Machine Learning algorithms and models to cater to their needs.Introducing the Machine learning techniques and solutions has brought huge returns for businesses.  Machine Learning solution providers like Google, IBM, Microsoft etc. are investing in human resources for development of Machine Learning models and algorithms. The tools developed by them are popularly used by businesses to get early returns. It has been observed that there is significant increase in patents in Machine Learning domains since the past few years, indicating the quantum of work happening in this domain.Machine Learning SkillsLet’s visit a few important skills one must acquire to work in the domain of Machine Learning.Programming languagesKnowledge of programming is very important for a career in Machine Learning. Languages like Python and R are popularly used to develop applications using Machine Learning models and algorithms. Python, being the simplest and most flexible language, is very popular for AI and Machine Learning applications. These languages provide rich support of libraries for implementation of Machine Learning Algorithms. A person who is good in programming can work very efficiently in this domain.Mathematics and StatisticsThe base for Machine Learning is mathematics and statistics. Statistics applied to data help in understanding it in micro detail. Many machine learning models are based on the probability theory and require knowledge of linear algebra, transformations etc. A good understanding of statistics and probability increases the early adoption to Machine Learning domain.Analytical toolsA plethora of analytical tools are available where machine learning models are already implemented and made available for use. Also, these tools are very good for visualization purposes. Tools like IBM Cognos, PowerBI, Tableue etc are important to pursue a career as a  Machine Learning engineer.Machine Learning Algorithms and librariesTo become a master in this domain, one must master the libraries which are provided with various programming languages. The basic understanding of how machine learning algorithms work and are implemented is crucial.Data Modelling for Machine Learning based systemsData lies at the core of any Machine Learning application. So, modelling the data to suit the application of Machine Learning algorithms is an important task. Data modelling experts are the heart of development teams that develop machine learning based systems. SQL based solutions like Oracle, SQL Server, and NoSQL solutions are important for modelling data required for Machine Learning applications. MongoDB, DynamoDB, Riak are some important NOSQL based solutions available to process unstructured data for Machine Learning applications.Other than these skills, there are two other skills that may prove to be beneficial for those planning on a career in the Machine Learning domain:Natural Language processing techniquesFor E-commerce sites, customer feedback is very important and crucial in determining the roadmap of future products. Many customers give reviews for the products that they have used or give suggestions for improvement. These feedbacks and opinions are analyzed to gain more insights about the customers buying habits as well as about the products. This is part of natural language processing using Machine Learning. The likes of Google, Facebook, Twitter are developing machine learning algorithms for Natural Language Processing and are constantly working on improving their solutions. Knowledge of basics of Natural Language Processing techniques and libraries is must in the domain of Machine Learning.Image ProcessingKnowledge of Image and Video processing is very crucial when a solution is required to be developed in the area of security, weather forecasting, crop prediction etc. Machine Learning based solutions are very effective in these domains. Tools like Matlab, Octave, OpenCV are some important tools available to develop Machine Learning based solutions which require image or video processing.ConclusionMachine Learning is a technique to automate the tasks based on past experiences. This is among the most lucrative career choices right now and will continue to remain so in the future. Job opportunities are increasing day by day in this domain. Acquiring the right skills by opting for a proper Machine Learning course is important to grow in this domain. You can have an impressive career trajectory as a machine learning expert, provided you have the right skills and expertise.
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Why Should You Start a Career in Machine Learning?

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Types of Probability Distributions Every Data Science Expert Should know

Data Science has become one of the most popular interdisciplinary fields. It uses scientific approaches, methods, algorithms, and operations to obtain facts and insights from unstructured, semi-structured, and structured datasets. Organizations use these collected facts and insights for efficient production, business growth, and to predict user requirements. Probability distribution plays a significant role in performing data analysis equipping a dataset for training a model. In this article, you will learn about the types of Probability Distribution, random variables, types of discrete distributions, and continuous distribution.  What is Probability Distribution? A Probability Distribution is a statistical method that determines all the probable values and possibilities that a random variable can deliver from a particular range. This range of values will have a lower bound and an upper bound, which we call the minimum and the maximum possible values.  Various factors on which plotting of a value depends are standard deviation, mean (or average), skewness, and kurtosis. All of these play a significant role in Data science as well. We can use probability distribution in physics, engineering, finance, data analysis, machine learning, etc. Significance of Probability distributions in Data Science In a way, most of the data science and machine learning operations are dependent on several assumptions about the probability of your data. Probability distribution allows a skilled data analyst to recognize and comprehend patterns from large data sets; that is, otherwise, entirely random variables and values. Thus, it makes probability distribution a toolkit based on which we can summarize a large data set. The density function and distribution techniques can also help in plotting data, thus supporting data analysts to visualize data and extract meaning. General Properties of Probability Distributions Probability distribution determines the likelihood of any outcome. The mathematical expression takes a specific value of x and shows the possibility of a random variable with p(x). Some general properties of the probability distribution are – The total of all probabilities for any possible value becomes equal to 1. In a probability distribution, the possibility of finding any specific value or a range of values must lie between 0 and 1. Probability distributions tell us the dispersal of the values from the random variable. Consequently, the type of variable also helps determine the type of probability distribution.Common Data Types Before jumping directly into explaining the different probability distributions, let us first understand the different types of probability distributions or the main categories of the probability distribution. Data analysts and data engineers have to deal with a broad spectrum of data, such as text, numerical, image, audio, voice, and many more. Each of these have a specific means to be represented and analyzed. Data in a probability distribution can either be discrete or continuous. Numerical data especially takes one of the two forms. Discrete data: They take specific values where the outcome of the data remains fixed. Like, for example, the consequence of rolling two dice or the number of overs in a T-20 match. In the first case, the result lies between 2 and 12. In the second case, the event will be less than 20. Different types of discrete distributions that use discrete data are: Binomial Distribution Hypergeometric Distribution Geometric Distribution Poisson Distribution Negative Binomial Distribution Multinomial Distribution  Continuous data: It can obtain any value irrespective of bound or limit. Example: weight, height, any trigonometric value, age, etc. Different types of continuous distributions that use continuous data are: Beta distribution Cauchy distribution Exponential distribution Gamma distribution Logistic distribution Weibull distribution Types of Probability Distribution explained Here are some of the popular types of Probability distributions used by data science professionals. (Try all the code using Jupyter Notebook) Normal Distribution: It is also known as Gaussian distribution. It is one of the simplest types of continuous distribution. This probability distribution is symmetrical around its mean value. It also shows that data at close proximity of the mean is frequently occurring, compared to data that is away from it. Here, mean = 0, variance = finite valueHere, you can see 0 at the center is the Normal Distribution for different mean and variance values. Here is a code example showing the use of Normal Distribution: from scipy.stats import norm  import matplotlib.pyplot as mpl  import numpy as np  def normalDist() -> None:      fig, ax = mpl.subplots(1, 1)      mean, var, skew, kurt = norm.stats(moments = 'mvsk')      x = np.linspace(norm.ppf(0.01),  norm.ppf(0.99), 100)      ax.plot(x, norm.pdf(x),          'r-', lw = 5, alpha = 0.6, label = 'norm pdf')      ax.plot(x, norm.cdf(x),          'b-', lw = 5, alpha = 0.6, label = 'norm cdf')      vals = norm.ppf([0.001, 0.5, 0.999])      np.allclose([0.001, 0.5, 0.999], norm.cdf(vals))      r = norm.rvs(size = 1000)      ax.hist(r, normed = True, histtype = 'stepfilled', alpha = 0.2)      ax.legend(loc = 'best', frameon = False)      mpl.show()  normalDist() Output: Bernoulli Distribution: It is the simplest type of probability distribution. It is a particular case of Binomial distribution, where n=1. It means a binomial distribution takes 'n' number of trials, where n > 1 whereas, the Bernoulli distribution takes only a single trial.   Probability Mass Function of a Bernoulli’s Distribution is:  where p = probability of success and q = probability of failureHere is a code example showing the use of Bernoulli Distribution: from scipy.stats import bernoulli  import seaborn as sb    def bernoulliDist():      data_bern = bernoulli.rvs(size=1200, p = 0.7)      ax = sb.distplot(          data_bern,           kde = True,           color = 'g',           hist_kws = {'alpha' : 1},          kde_kws = {'color': 'y', 'lw': 3, 'label': 'KDE'})      ax.set(xlabel = 'Bernouli Values', ylabel = 'Frequency Distribution')  bernoulliDist() Output:Continuous Uniform Distribution: In this type of continuous distribution, all outcomes are equally possible; each variable gets the same probability of hit as a consequence. This symmetric probabilistic distribution has random variables at an equal interval, with the probability of 1/(b-a). Here is a code example showing the use of Uniform Distribution: from numpy import random  import matplotlib.pyplot as mpl  import seaborn as sb  def uniformDist():      sb.distplot(random.uniform(size = 1200), hist = True)      mpl.show()  uniformDist() Output: Log-Normal Distribution: A Log-Normal distribution is another type of continuous distribution of logarithmic values that form a normal distribution. We can transform a log-normal distribution into a normal distribution. Here is a code example showing the use of Log-Normal Distribution import matplotlib.pyplot as mpl  def lognormalDist():      muu, sig = 3, 1      s = np.random.lognormal(muu, sig, 1000)      cnt, bins, ignored = mpl.hist(s, 80, normed = True, align ='mid', color = 'y')      x = np.linspace(min(bins), max(bins), 10000)      calc = (np.exp( -(np.log(x) - muu) **2 / (2 * sig**2))             / (x * sig * np.sqrt(2 * np.pi)))      mpl.plot(x, calc, linewidth = 2.5, color = 'g')      mpl.axis('tight')      mpl.show()  lognormalDist() Output: Pareto Distribution: It is one of the most critical types of continuous distribution. The Pareto Distribution is a skewed statistical distribution that uses power-law to describe quality control, scientific, social, geophysical, actuarial, and many other types of observable phenomena. The distribution shows slow or heavy-decaying tails in the plot, where much of the data reside at its extreme end. Here is a code example showing the use of Pareto Distribution – import numpy as np  from matplotlib import pyplot as plt  from scipy.stats import pareto  def paretoDist():      xm = 1.5        alp = [2, 4, 6]       x = np.linspace(0, 4, 800)      output = np.array([pareto.pdf(x, scale = xm, b = a) for a in alp])      plt.plot(x, output.T)      plt.show()  paretoDist() Output:Exponential Distribution: It is a type of continuous distribution that determines the time elapsed between events (in a Poisson process). Let’s suppose, that you have the Poisson distribution model that holds the number of events happening in a given period. We can model the time between each birth using an exponential distribution.Here is a code example showing the use of Pareto Distribution – from numpy import random  import matplotlib.pyplot as mpl  import seaborn as sb  def expDist():      sb.distplot(random.exponential(size = 1200), hist = True)      mpl.show()   expDist()Output:Types of the Discrete probability distribution – There are various types of Discrete Probability Distribution a Data science aspirant should know about. Some of them are – Binomial Distribution: It is one of the popular discrete distributions that determine the probability of x success in the 'n' trial. We can use Binomial distribution in situations where we want to extract the probability of SUCCESS or FAILURE from an experiment or survey which went through multiple repetitions. A Binomial distribution holds a fixed number of trials. Also, a binomial event should be independent, and the probability of obtaining failure or success should remain the same. Here is a code example showing the use of Binomial Distribution – from numpy import random  import matplotlib.pyplot as mpl  import seaborn as sb    def binomialDist():      sb.distplot(random.normal(loc = 50, scale = 6, size = 1200), hist = False, label = 'normal')      sb.distplot(random.binomial(n = 100, p = 0.6, size = 1200), hist = False, label = 'binomial')      plt.show()    binomialDist() Output:Geometric Distribution: The geometric probability distribution is one of the crucial types of continuous distributions that determine the probability of any event having likelihood ‘p’ and will happen (occur) after 'n' number of Bernoulli trials. Here 'n' is a discrete random variable. In this distribution, the experiment goes on until we encounter either a success or a failure. The experiment does not depend on the number of trials. Here is a code example showing the use of Geometric Distribution – import matplotlib.pyplot as mpl  def probability_to_occur_at(attempt, probability):      return (1-p)**(attempt - 1) * probability  p = 0.3  attempt = 4  attempts_to_show = range(21)[1:]  print('Possibility that this event will occur on the 7th try: ', probability_to_occur_at(attempt, p))  mpl.xlabel('Number of Trials')  mpl.ylabel('Probability of the Event')  barlist = mpl.bar(attempts_to_show, height=[probability_to_occur_at(x, p) for x in attempts_to_show], tick_label=attempts_to_show)  barlist[attempt].set_color('g')  mpl.show() Output:Poisson Distribution: Poisson distribution is one of the popular types of discrete distribution that shows how many times an event has the possibility of occurrence in a specific set of time. We can obtain this by limiting the Bernoulli distribution from 0 to infinity. Data analysts often use the Poisson distributions to comprehend independent events occurring at a steady rate in a given time interval. Here is a code example showing the use of Poisson Distribution from scipy.stats import poisson  import seaborn as sb  import numpy as np  import matplotlib.pyplot as mpl  def poissonDist():       mpl.figure(figsize = (10, 10))      data_binom = poisson.rvs(mu = 3, size = 5000)      ax = sb.distplot(data_binom, kde=True, color = 'g',                       bins=np.arange(data_binom.min(), data_binom.max() + 1),                       kde_kws={'color': 'y', 'lw': 4, 'label': 'KDE'})      ax.set(xlabel = 'Poisson Distribution', ylabel='Data Frequency')      mpl.show()      poissonDist() Output:Multinomial Distribution: A multinomial distribution is another popular type of discrete probability distribution that calculates the outcome of an event having two or more variables. The term multi means more than one. The Binomial distribution is a particular type of multinomial distribution with two possible outcomes - true/false or heads/tails. Here is a code example showing the use of Multinomial Distribution – import numpy as np  import matplotlib.pyplot as mpl  np.random.seed(99)   n = 12                      pvalue = [0.3, 0.46, 0.22]     s = []  p = []     for size in np.logspace(2, 3):      outcomes = np.random.multinomial(n, pvalue, size=int(size))        prob = sum((outcomes[:,0] == 7) & (outcomes[:,1] == 2) & (outcomes[:,2] == 3))/len(outcomes)      p.append(prob)      s.append(int(size))  fig1 = mpl.figure()  mpl.plot(s, p, 'o-')  mpl.plot(s, [0.0248]*len(s), '--r')  mpl.grid()  mpl.xlim(xmin = 0)  mpl.xlabel('Number of Events')  mpl.ylabel('Function p(X = K)') Output:Negative Binomial Distribution: It is also a type of discrete probability distribution for random variables having negative binomial events. It is also known as the Pascal distribution, where the random variable tells us the number of repeated trials produced during a specific number of experiments.  Here is a code example showing the use of Negative Binomial Distribution – import matplotlib.pyplot as mpl   import numpy as np   from scipy.stats import nbinom    x = np.linspace(0, 6, 70)   gr, kr = 0.3, 0.7        g = nbinom.ppf(x, gr, kr)   s = nbinom.pmf(x, gr, kr)   mpl.plot(x, g, "*", x, s, "r--") Output: Apart from these mentioned distribution types, various other types of probability distributions exist that data science professionals can use to extract reliable datasets. In the next topic, we will understand some interconnections & relationships between various types of probability distributions. Relationship between various Probability distributions – It is surprising to see that different types of probability distributions are interconnected. In the chart shown below, the dashed line is for limited connections between two families of distribution, whereas the solid lines show the exact relationship between them in terms of transformation, variable, type, etc. Conclusion  Probability distributions are prevalent among data analysts and data science professionals because of their wide usage. Today, companies and enterprises hire data science professionals in many sectors, namely, computer science, health, insurance, engineering, and even social science, where probability distributions appear as fundamental tools for application. It is essential for Data analysts and data scientists. to know the core of statistics. Probability Distributions perform a requisite role in analyzing data and cooking a dataset to train the algorithms efficiently. If you want to learn more about data science - particularly probability distributions and their uses, check out KnowledgeHut's comprehensive Data science course. 
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Types of Probability Distributions Every Data Scie...

Data Science has become one of the most popular in... Read More