Data science is a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems.

At the core is data. Troves of raw information, streaming in and stored in enterprise data warehouses. Much to learn by mining it. Advanced capabilities we can build with it. Data science is ultimately about using this data in creative ways to generate business value.

This aspect of data science is all about uncovering findings from data. Diving in at a granular level to mine and understand complex behaviours, trends, and inferences. It's about surfacing hidden insight that can help enable companies to make smarter business decisions.

How do data scientists mine out insights? It starts with data exploration. When given a challenging question, data scientists become detectives. They investigate leads and try to understand pattern or characteristics within the data. This requires a big dose of analytical creativity.

Then as needed, data scientists may apply quantitative technique in order to get a level deeper – e.g. inferential models, segmentation analysis, time series forecasting, synthetic control experiments, etc. The intent is to scientifically piece together a forensic view of what the data is really saying.

This data-driven insight is central to providing strategic guidance. In this sense, data scientists act as consultants, guiding business stakeholders on how to act on findings.

Data scientists play a central role in developing data product. This involves building out algorithms, as well as testing, refinement, and technical deployment into production systems. In this sense, data scientists serve as technical developers, building assets that can be leveraged at wide scale.


Fundamental Requirements:

  1. a) Linear Algebra: To understand various algorithms.
  2. b) Multivariable Calculus:To understand various algorithms.
  3. c) Probability and Statistics:To understand various algorithms.
  4. d) Coding in at least one Language preferably Python/R: Scripting and Development.
  5. e) SQL and Excel:Probably the two tools you will use the most.
  6. f) Knowledge of machine Learning algorithms


A data scientist’s role is to work between the IT and business worlds, deciphering large chunks of data, analysing problems, gaining a deeper insight into what the data signifies and devising business strategies. So a graduate with a data science degree can work as business intelligence analyst, data mining engineer, data architect or data scientist. We’re living in a digital age and we’re controlled by data in our everyday lives. Hence, finding a job with a data science degree won’t be a herculean task.

Data Scientists are people with some mix of coding and statistical skills who work on making data useful in various ways. There are two main types:

Type A Data Scientist: The A is for Analysis. This type is primarily concerned with making sense of data or working with it in a fairly static way. The Type A Data Scientist is very similar to a statistician (and may be one) but knows all the practical details of working with data that aren't taught in the statistics curriculum:  data cleaning, methods for dealing with very large data sets, visualization, deep knowledge of a particular domain, writing well about data, and so on. 

The Type A Data Scientist can code well enough to work with data but is not necessarily an expert. The Type A data scientist may be an expert in experimental design, forecasting, modeling, statistical inference, or other things typically taught in statistics departments. Generally speaking though, the work product of a data scientist is not "p-values and confidence intervals" as academic statistics sometimes seems to suggest (and as it sometimes is for traditional statisticians working in the pharmaceutical industry, for example). At Google, Type A Data Scientists are known variously as Statistician, Quantitative Analyst, Decision Support Engineering Analyst, or Data Scientist, and probably a few more.

Type B Data Scientist: The B is for Building. Type B Data Scientists share some statistical background with Type A, but they are also very strong coders and may be trained software engineers.  The Type B Data Scientist is mainly interested in using data "in production."  They build models which interact with users, often serving recommendations (products, people you may know, ads, movies, search results). 

At Google, a Type B Data Scientist would typically be called a Software Engineer. Type B Data Scientists may use the term Data Scientist to refer just to themselves, and since the definition of the field is very much in flux, they may be right. But I see the term being used most often in the general way I am proposing here. 

This categorization is crude. Many Data Scientists are some mix of A and B. 


At Bachelor’s level, Data Science is only available at Narsee Monjee Institute of Management Studies in the form of the bachelor’s degree “BSc in Applied Statistics and Analytics”.

The following institutes offer Data Science at Post-Graduate level:



Jointly offered by IIT Kharagpur, IIM Calcutta and ISI Kolkata

PG Diploma in Business Analytics

Great Lakes Institute of Management

PG Progamme in Business Analytics and Business Intelligence

Indian School of Business

Certificate Programme in Business Analytics

IIIT Bangalore and UPGrad

PG Diploma in Data Analytics

Aegis School of Business

PG Diploma in Data Science, Business Analytics and Big Data; in association with IBM

Indian Institute of Information Technology and Management, Kerala (IITM-K)

MSc in Computer Science with Specialisation in Data Analytics

Indian Institute of Science, Bangalore

MTech in Computational and Data Science

IIM Calcutta

Advanced Programme in Data Sciences (APDS)

IIM Bangalore

Executive Programme in Business Analytics and Intelligence

IIT Hyderabad

Executive M. Tech in Data Science

IIIT Delhi

M. Tech in Computer Science with Specialization in Data Engineering

The Central University of Rajasthan (in collaboration with Tata Consultancy Services Limited)

MSc in Big Data Analytics

St. Xavier’s College, Mumbai

PG Diploma in Data Science

St. Xavier’s College, Ahmedabad (in collaboration with Tata Consultancy Services Limited)

MSc in Big Data Analytics

Joint collaboration of University of Mumbai, Department of Economics (Autonomous) and NISM

PG Diploma in Data Science (PGCDS)

Jadavpur University

PGD in Data Science and Industrial Applications


The following academic institutions provide Bachelor’s in Data Analytics across the globe:





Massachusetts Institute of Technology (MIT)

BSc in Business Analytics

BSc in Computer Science, Economics and Data Science

University of Pennsylvania

Bachelors in Survey Research and Data Analytics


Monash University

B. Com in Business Analytics

Australian National University

Bachelor of Applied Data Analytics (Hons)

University of New South Wales

Bachelor of Data Science and Decisions


Aarhus University, Denmark

BSc in Economics and Business Administration (Business Analytics)

New Zealand

Auckland University of Technology

Bachelor of Mathematical Sciences in Analytics

Massey University

Bachelor of Information Sciences in Data Science


National University of Singapore (NUS)

Bachelor of Science in Business Analytics

Bachelor of Science in Data Science and Analytics

Chinese University of Hong Kong

Bachelor in Quantitative Finance and Risk Management Science

United Kingdom

University of Warwick

BSc in Data Science

University of Essex

BSc in Data Science and Analytics






Massachusetts Institute of Technology (MIT)

MSc in Business Analytics

University of Chicago

MSc in Analytics

Stanford University

MSc in Statistics

University of Washington

MSc in Data Science

New York University

MSc in Data Science

Canegie Mellon University

MSc in Computational Data Science

MSc in Machine Learning

Georgia Institute of Technology

MSc in Analytics

Texas A&M University

MSc in Analytics

Columbia University

MSc in Data Science


Deakin University

Master of Business Analytics

James Cook University

Master of Data Science

University of Technology Sydney

Master of Analytics (Research)

United Kingdom

University of Oxford

Advanced Diploma in Data and Systems Analysis

University of Strathclyde

Master of Information Technology Management

Imperial College London

MSc in Computing (Machine Learning)

Bournemouth University

MSc in Applied Data Analytics

City University London

MSc in Data Science

University of Warwick

MSc in Data Analytics

University College Cork

MSc in Data Science and Analytics

New Zealand

University of Otago

Master of Business Data Science


National University of Singapore (NUS)

Master of Science in Business Analytics

Chinese University of Hong Kong

MSc in Data Science and Business Statistics

The University of Hong Kong

Master in Statistics


Data Sciencetech Institute, France

Advanced MSc in Information Systems and Artificial Intelligence for Big Data Engineering

Aarhus University, Denmark

Master in Economics and Business Administration- Business Intelligence

Linkoping University, Sweden

MSc in Statistics and Data Mining

KTH Royal Institute of Technology, Sweden

Master in Machine Learning

Barcelona Graduate School of Economics, Spain

Master in Data Science

HTW Berlin

MSc in Project Management and Data Science

IE School of Human Sciences and Technology, Spain

Master in Business Analytics and Big Data

Maastricht University, The Netherlands

Masters in International Business in Information Management and Business Intelligence

Radbound Universiteit Nijmegen, The Netherlands

MSc in Business Administration, specializing in Business Analysis and Modelling

Vrije Universiteit Amsterdam

Master’s in Business Analytics

University of Helsinki, Finland

MSc in Computer Science: Algorithms, Data Analytics and Machine Learning


Queen’s University

Master of Management Analytics

University of Alberta

MSc in Statistical Machine Learning

York University

Master of Business Analytics


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