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

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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 makeour 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 sciencestatistics, 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 Scientists

A 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 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 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$115,475
Netherlands$68,880
Germany$64,024
United Kingdom$59,781
Spain$30,050
Italy$37,785

Data Scientist Salary by Experience

A 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$165,114
Amazon$164,114
Airbnb$154,879
Netflix$147,617 
Apple$144,490
Twitter$144,341
Walmart$144,198
Facebook$143,189
eBay$143,005

Salaries 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$114,826
Machine Learning Scientist$114,121
Applications Architect$113,757
Enterprise Architect$110,663
Data Architect$108,278
Infrastructure Architect$107,309
Business Intelligence Developer$81,514
Statistician$76,884

Conclusion 

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.  

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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A Peak 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
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A Peak Into the World of Data Science

Touted as the sexiest job in the 21st century, ba... Read More

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. Image source: 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

Data Science: Correlation vs Regression in Statistics

In this article, we will understand the key differences between correlation and regression, and their significance. Correlation and regression are two different types of analyses that are performed on multi-variate distributions of data. They are mathematical concepts that help in understanding the extent of the relation between two variables: and the nature of the relationship between the two variables respectively. Correlation Correlation, as the name suggests is a word formed by combining ‘co’ and ‘relation’. It refers to the analysis of the relationship that is established between two variables in a given dataset. It helps in understanding (or measuring) the linear relationship between two variables.  Two variables are said to be correlated when a change in the value of one variable results in a corresponding change in the value of the other variable. This could be a direct or an indirect change in the value of variables. This indicates a relationship between both the variables.  Correlation is a statistical measure that deals with the strength of the relation between the two variables in question.  Correlation can be a positive or negative value. Positive Correlation Two variables are considered to be positively correlated when the value of one variable increases or decreases following an increase or decrease in the value of the other variable respectively.  Let us understand this better with the help of an example: Suppose you start saving your money in a bank, and they offer some amount of interest on the amount you save in the bank. The more the amount you store in the bank, the more interest you get on your money. This way, the money stored in a bank and the interest obtained on it are positively correlated. Let us take another example: While investing in stocks, it is usually said that higher the risk while investing in a stock, higher is the rate of returns on such stocks.  This shows a direct inverse relationship between the two variables since both of them increase/decrease when the other variable increases/decreases respectively. Negative Correlation Two variables are considered to be negatively correlated when the value of one variable increases following a decrease in the value of the other variable. Let us understand this with an example: Suppose a person is looking to lose weight. The one basic idea behind weight loss is reducing the number of calorie intake. When fewer calories are consumed and a significant number of calories are burnt, the rate of weight loss is quicker. This means when the amount of junk food eaten is decreased, weight loss increases. Let us take another example: Suppose a popular non-essential product that is being sold faces an increase in the price. When this happens, the number of people who purchase it will reduce and the demand would also reduce. This means, when the popularity and price of the product increases, the demand for the product reduces. An inverse proportion relationship is observed between the two variables since one value increases and the other value decreases or one value decreases and the other value increases.  Zero Correlation This indicates that there is no relationship between two variables. It is also known as a zero correlation. This is when a change in one variable doesn't affect the other variable in any way. Let us understand this with the help of an example: When the increase in height of our friend/neighbour doesn’t affect our height, since our height is independent of our friend’s height.  Correlation is used when there is a requirement to see if the two variables that are being worked upon are related to each other, and if they are, what the extent of this relationship is, and whether the values are positively or negatively correlated.  Pearson’s correlation coefficient is a popular measure to understand the correlation between two values.  Regression Regression is the type of analysis that helps in the prediction of a dependant value when the value of the independent variable is given. For example, given a dataset that contains two variables (or columns, if visualized as a table), a few rows of values for both the variables would be given. One or more of one of the variables (or column) would be missing, that needs to be found out. One of the variables would depend on the other, thereby forming an equation that relevantly represents the relationship between the two variables. Regression helps in predicting the missing value. Note: The idea behind any regression technique is to ensure that the difference between the predicted and the actual value is minimal, thereby reducing the error that occurs during the prediction of the dependent variable with the help of the independent variable. There are different types of regression and some of them have been listed below: Linear Regression This is one of the basic kinds of regression, which usually involves two variables, where one variable is known as the ‘dependent’ variable and the other one is known as an ‘independent’ variable. Given a dataset, a pattern has to be formed (linear equation) with the help of these two variables and this equation has to be used to fit the given data to a straight line. This straight-line needs to be used to predict the value for a given variable. The predicted values are usually continuous. Logistic Regression There are different types of logistic regression:  Binary logistic regression is a regression technique wherein there are only two types or categories of input that are possible, i.e 0 or 1, yes or no, true or false and so on. Multinomial logistic regression helps predict output wherein the outcome would belong to one of the more than two classes or categories. In other words, this algorithm is used to predict a nominal dependent variable. Ordinal logistic regression deals with dependant variables that need to be ranked while predicting it with the help of independent variables.  Ridge Regression It is also known as L2 regularization. It is a regression technique that helps in finding the best coefficients for a linear regression model with the help of an estimator that is known as ridge estimator. It is used in contrast to the popular ordinary least square method since the former has low variance and hence it calculates better coefficients. It doesn’t eliminate coefficients thereby not producing sparse, simple models.  Lasso Regression LASSO is an acronym that stands for ‘Least Absolute Shrinkage and Selection Operator’. It is a type of linear regression that uses the concept of ‘shrinkage’. Shrinkage is a process with the help of which values in a data set are reduced/shrunk to a certain base point (this could be mean, median, etc). It helps in creating simple, easy to understand, sparse models, i.e the models that have fewer parameters to deal with, thereby being simple.  Lasso regression is highly suited for models that have high collinearity levels, i.e a model where certain processes (such as model selection or parameter selection or variable selection) is automated.  It is used to perform L1 and L2 regularization. L1 regularization is a technique that adds a penalty to the given values of coefficients in the equation. This also results in simple, easy to use, sparse models that would contain lesser coefficients. Some of these coefficients can also be estimated off to 0 and hence eliminated from the model altogether. This way, the model becomes simple.  It is said that Lasso regression is easier to work with and understand in comparison to ridge regression.  There are significant differences between both these statistical concepts.  Difference between Correlation and Regression Let us summarize the difference between correlation and regression with the help of a table: CorrelationRegressionThere are two variables, and their relationship is understood and measured.Two variables are represented as 'dependent' and 'independent' variables, and the dependent variable is predicted.The relationship between the two variables is analysed.This concept tells about how one variable affects the other and tries to predict the dependant variable.The relationship between two variables (say ‘x’ and ‘y’) is the same if it is expressed as ‘x is related to y’ or ‘y is related to x’.There is a significant difference when we say ‘x depends on y’ and ‘y depends on x’. This is because the independent and dependent variables change.Correlation between two variables can be expressed through a single point on a graph, visually.A line or a curve is fitted to the given data, and the line or the curve is extrapolated to predict the data and make sure the line or the curve fits the data on the graph.It is a numerical value that tells about the strength of the relation between two variables.It predicts one variable based on the independent variables. (this predicted value can be continuous or discrete, depending on the type of regression) by fitting a straight line to the data.Conclusion In this article, we understood the significant differences between two statistical techniques, namely- correlation and regression with the help of examples. Correlation establishes a relationship between two variables whereas regression deals with the prediction of values and curve fitting. 
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Data Science: Correlation vs Regression in Statist...

In this article, we will understand the key differ... Read More