  ## 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.

# Data Science: Correlation vs Regression in Statistics

10K 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 regularizationIt 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 coefficientsSome 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:

CorrelationRegression
There 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.

### Dipayan Ghatak

Project Manager

Leading Projects across geographies in Microsoft Consultant Services.

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## A Peek Into the World of Data Science

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 15 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.