What is the Difference Between Data Science, Big Data and Data Analytics?

Since all three terms deal with the word ‘data’, there is a lot of confusion surrounding them. Most people are not aware that they are not the same, and there are many differences between the three different terms.

  • Data Science is a science or study of data, and involves creating algorithms and models to extract knowledge from data.
  • Big Data is basically a term that describes large amounts of data. It is not a field in itself, but the analysis of big data is used in many different fields and to make better decisions by businesses.
  • Data Analytics refers to the analysis of data for drawing conclusions out of it. It is mainly used by businesses to make strategic decisions and solve problems.

Thus, in simpler terms, data scientists build the tools and algorithms that can be used to make sense of data, including big data. For this, they utilise technology, machine learning and mathematical principles.

On the other hand, data analysts apply these models to analyse business data of all kinds to help make smarter business decisions. Even the use of excel by businesses falls under the purview of data analysis. A big data analyst would just utilise large amounts of data, that cannot be processed by traditional tools like Excel.


What is the Demand for a Career in Data Science?

Data is everywhere. From the votes we give in political elections to the pictures we upload on Instagram, everything is data. Reports estimate that by the year 2020, as much as 1.7 MB of digital data will be created each second for every single person on the planet.

With so much data and information available, organizations are focusing more and more on using the insights from this data to evaluate progress, build solutions and make decisions.

And it is not just a global phenomenon. Even India is witnessing a surge of opportunities in Data Science and Data Analytics. A recent report by Edvancer and Analytics India magazine revealed that India has the most number of Data Analytics jobs after the US, with over 78,000 positions currently available.

It is not surprising then that Data Science is being called the ‘hottest job of the 21st century’. It is making its presence felt everywhere, and according to McKinsey & Company, Big data will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus.


How to Start a Career in Data Science in India?  

Due to its multi-disciplinary nature, Data Science requires you to have a broad set of skills, including knowledge of Mathematics, Statistics, Computer Science and Hacking/Coding, coupled with substantial expertise in business or a field of science. Knowledge about the concepts of Artificial Intelligence and Machine Learning are also beneficial.

Thus, to build a career as a Data Scientist, degrees in Mathematics, Statistics, Economics, Engineering, Computer Science, etc. can help form a good base. 

Some Leading Global Universities for Data Science/Data Analytics

1. Carnegie Mellon University, United States

Course: Master of Information Systems Management: Business Intelligence & Data Analytics; MS in Computational Data Science.

2. Texas A&M University, United States

Course: M.S in Analytics

3. Georgia Institute of Technology, United States

Course: M.S in Analytics

4. Imperial College, London

Course: M.Sc in Business Analytics. M.Sc in Computing (Machine Learning)

5. Massachusetts Institute of Technology, United States

Course: Master of Business Analytics

6. University College London, United Kingdom

Course: M.Sc Data Science (Specializations in statistics, Machine learning, etc); M.Sc in Business Analytics.


While degrees can help you enter the fields or form a base, there are many good online learning platforms that offer certifications in data science, as well as specific skills required for it. These can prove really useful for entering the field and being successful in it.


What Skills are Needed to Be a Data Scientist?

  1. Basic use of statistical tools and fast mathematical calculations
  2. Ability to work with large numbers and calculations
  3. Good grasp of programming languages like Java, Perl, C/C++, Python, etc.
  4. Extensive knowledge of data analytics software like SAS, R, Hadoop and Tableau.
  5. Familiarity with SQL database techniques.
  6. Critical reasoning skills and problem-solving ability
  7. Data visualization and communication ability


What are the Career Opportunities in Data Science?

There are different types of roles available within the domain of Data Science. The most prominent ones include:


Data Scientist

This is the core analytics part of big data. Data scientists are involved in understanding and exploring data patterns, in order to analyze the impact on businesses. They apply statistical and mathematical models to simplify data. Along with analyzing data, they also devise solutions for various data complexities.


Data Engineer

This role is majorly for all software engineers, who are involved in the non-analytical part of big data. Their work role is more focused on coding, cleaning up data sets, and implementing suggestions and data solutions that come from data scientists.


Business Intelligence Professional

A business intelligence specialist is involved in the market research of various structured and unstructured data and generates reports to analyze the business trends. They are trained to work on SQL and other statistical tools. They send these reports to the management and update the data models as and when required.


Data Manager

Also sometimes known as Database Administrators, are involved in the structuring of data and management of unstructured data. They are responsible for creating the infrastructure and database systems that meet the needs of research and data science teams for the information gathered. They also review data for inconsistencies and conduct maintenance of data.


Data Analyst

Data analysts, as I previously suggested help make sense of large amounts of data, specifically for use by businesses. They work with SQL databases, Excel, Tableau and other software to analyse various kinds of data (e.g. website traffic, sales figures, operational costs, etc.). They then create reports to be used to create solutions and make strategic decisions.

Apart from these, many other specialised roles in Machine Learning, Artificial Intelligence and Big Data are also coming up. All in all, a data scientist can be a programmer, product developer, analyst and statistician, all rolled into one.


So I hope this article helps you gain a direction to start your career in Data Science.  Data Science, Machine Learning, Big Data are the next big thing are going to be huge in the coming years. I hope this article gives you direction to start your career in any of these disruptive fields!


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