WhatsTheBigData

Graduate Programs 

in Data Science and Big Data Analytics

Last updated: July 9, 2019

See below for online programs and courses and other options and resources

Aegis School of Business, Data Science & Telecommunication (India)

Post Graduate Program in Business Analytics and Big Data

American Sentinel University

Master of Health Care Informatics

Arizona State University

MS in Business Analytics

Barcelona Graduate School of Economics (Spain)

Master in Data Science

Barcelona Technology School (Spain)

Master in Big Data Solutions

Ben-Gurion University of the Negev (Beer-Sheva, Israel)

MSc in Information Systems Engineering with focus on Data Mining and Business Intelligence

Bentley University

M.S. in Marketing Analytics

Berlin School of Economics and Law (Germany)

Master of Science in Business Intelligence and Process Management

Brown University

Master’s in Data Science

Carnegie Mellon University

Master of Computational Data Science

Master of Information Systems Management (MISM) degree with a Business Intelligence and Data Analytics (BIDA) concentration

MS in Machine Learning

Catholic University of America

The Master of Science in Business Analysis

Central Connecticut State University

Master of Science in Data Mining

Graduate Certificate in Data Mining

Central Michigan University

Graduate Certificate in Data Mining

City University London (UK)

MSc in Data Science

Clarkson University

MS in Data Analytics

Columbia University

Master of Science in Data Science

Master of Science in Information and Knowledge Strategy

Certification of Professional Achievement in Data Sciences

Cornell University 

Masters of Engineering in Operations Research and Information Engineering – Data Analytics Concentration

Dartmouth College

PhD in Quantitative Biomedical Sciences

Data ScienceTech Institute (Paris and Nice, France)

MSc in Data Science

MSc in Big Data Analytics

DePaul University

M.S. in Predictive Analytics

Drexel University

Master of Science in Business Analytics

Donau-Universität Krems (Austria)

MSc in Data Studies

Dublin City University (Ireland)

MSc in Computing (Data Analytics Major)

Dublin Institute of Technology (Ireland)

MSc in Computing (Data Analytics)

Ecole Central Paris (France)

MSc in Data Science

Eindhoven University of Technology (Netherlands)

M.S. in Data Science

Fordham University

MS in Business Analytics

George Mason University

MS in Computational Science

PhD in Computational Sciences and Informatics

Georgetown University

Master of Science in Analytics with a concentration in Data Science

George Washington University

MS in Business Analytics

Harrisburg University

M.S. in Analytics

Harvard University

Master of Science (SM) in Computational Science and Engineering (CSE)

Heriot-Watt University (Edinburgh, UK)

MSc in Data Science

IE University (Madrid, Spain)

Master in Business Analytics and Big Data

Illinois Institute of Technology

Master of Data Science

Master of Science in Marketing Analytics and Communication

MBA in Business Analytics

Imperial College (London, UK)

MSc in Data Science and Management

Indiana University Bloomington

M.S. in Data Science

Indiana University-Purdue University Indianapolis School of Informatics

PhD in Informatics

Iowa State University

Master of Business Analytics

Jacksonville University

Master of Science in Applied Business Analytics

Jheronimus Academy of Data Science (Netherlands)

Data Science Master’s Programs

John Hopkins University

Master of Science in Bioinformatics

Kennesaw State University

Master of Science in Applied Statistics

Lipscomb University

Master of Science in Informatics and Analytics 

Loras College

MBA in Business Analytics

Loyola University Maryland

Master of Science in Data Science

Louisiana State University

Master of Science in Analytics

Maastricht University (Maastricht, the Netherlands)

MSc in International Business/Business Intelligence

Macquarie University (Sydney, Australia)

Master of Data Science

Merrimack College

Master of Science in Data Science

Michigan State University

MS in Business Analytics

MIT

Master of Business Analytics (M.B.An.)

New College of Florida

Master in Data Science

New Jersey Institute of Technology

Graduate Certificate in Data Mining

New York University

MS in Data Science

MS in Business Analytics

MS in Applied Urban Science and Informatics

North Carolina State University

M.S. in Analytics

Northeastern University

Graduate Certificate in Data Science

MS in Bioinformatics

Northwestern University

Master of Science in Analytics

Nova Northeastern University

Graduate Certificate In Business Intelligence / Analytics

Oakland University

Master of Science in IT Management – Business Analytics

Pace University

MS in Customer Intelligence and Analytics

Purdue University

MBA in Business Analytics

Rensselaer Polytechnic Institute

Master of Science in Business Analytics

Robert Gordon University (Aberdeen, UK)

MSc in Data Science

Rutgers University

MBA in Analytics and Information Management

MBS in Analytics

Saint Mary’s College

Master of Science in Data Science

Saint Peter’s University

Master of Science in Data Science

Stanford University

Matser of Science in Biomedical Informatics

Mining Massive Data Sets Graduate Certificate

Stevens Institute’s Wesley J. Howe School of Technology Management

Master of Science in Business Intelligence and Analytics

Graduate Certificate in Business Intelligence and Analytics

Swansea University (Wales, UK)

MSc in Health Data Science

Syracuse University School of Information Studies

Graduate Certificate of Advanced Studies in Data Science

MS in Applied Data Science

Texas A&M University

Master of Science in Analytics

Università di Pisa

Master in Big Data

University College Dublin (Ireland)

MSc in Business Analytics

University College London (UK)

MSc in Machine Learning

University of Auckland (Auckland, New Zealand) 

Master of Professional Studies–Data Science

University of California, San Diego

Master of Advanced Study in Data Science and Engineering

University of Chicago

Master of Science in Analytics

M.S. in Computer Science/Data Analytics

University of Cincinnati

MS in Business Analytics

University of Connecticut

MS in Business Analytics and Project Management

University of Dundee (Dundee, UK)

MSc in Data Engineering

MSc in Data Science

University of Edinburgh (UK)

Informatics MSc

PhD in Data Science

University of Essex (Colchester, UK)

MSc Big Data and Text Analytics

University of Glasgow (UK)

MSc Data Science

University of Illinois at Urbana-Champaign

Master of Science in Statistics/Analytics Concentration

University of Iowa

Graduate Certificate in Business Analytics

University of Magdeburg (Magdeburg, Germany)

MSc in Data and Knowledge Engineering

University of Maryland

Master of Science in Business for Marketing Analytics

University of Maryland University College

Master of Science in Data Analytics

University of Massachusetts, Dartmouth

MS in Data Science

University of Michigan-Dearborn

MS in Business Analytics

University of Rochester Simon School of Business

MS in Business Analytics

University of San Francisco

M.S. in Analytics

University of Southern California Marshall School of Business

Master of Science in Business Analytics

University of Stirling (UK)

MSc in Big Data

University of Tennessee

Master’s in Business Analytics

Graduate Certificate in Business Analytics

Ph.D. in Analytics

University of Texas at Austin

Master of Science in Business Analytics

University of Virginia

Master of Science in Data Science

University of Warwick (UK)

MSc in Data Analytics

University of Washington

Certificate in Data Science

Virginia Commonwealth University

Master of Science in Business with a decision sciences and business analytics concentration

Worcester Polytechnic Institute

M.S. in Data Science

PhD in Data Science

York University

MSc in Business Analytics

Fellowship/Training Programs

DS12

The Data Incubator

Data Science for Social Good 

Data Science DOJO

Insight Data Science Fellows

NYC Data Science Academy

Zipfian Academy

Online University Programs

Aegis School of Business, Data Science and Telecommunication

Post Graduate Program in Business Analytics and Big Data

American Sentinel University

Master of Geospatial Information Systems Program

Arizona State University

Master of Advanced Study in Health Informatics

Bay Path University

MS in Applied Data Science

Brandeis University

Master of Science in Strategic Analytics

Carnegie Mellon University

Master of Science in Business Analytics

City University of New York

M.S. In Data Analytics

Elmhurst College

M.S. in Data Science

Harrisburg University

M.S. in Analytics

Indiana University

M.S. in Data Science

Lewis University

Master of Science in Data Science

Maryville University

Master of Science in Business Data Analytics

Northwestern University

Master of Science in Predictive Analytics

Nova Northeastern University

Graduate Certificate In Business Intelligence / Analytics 

Saint Joseph’s University

Master of Science in Business Intelligence & Analytics

Saint Mary’s College

MS in Data Science

Southern Methodist University

Master of Science in Data Science

Thomas Edison State College

MBA in Data Analytics

University of North Carolina

MBA (Data Analytics and Decision Making Concentration)

University of California, Berkeley 

Master of Information and Data Science

University of California, Riverside

Master of Science in Engineering (Data Science specialization)

University of Wisconsin

Master in Data Science

Online Courses (Free)

Coursera

Computing for Data Analysis

Data Analysis and Statistical Inference

Web Intelligence and Big Data

Process Mining: Data science in Action

Data Society Online Courses

Intro to Data Science, Machine Learning, etc.

edX

Data Science and Machine Learning Essentials

Wiretaps to Big Data: Privacy and Surveillance in the Age of Interconnection

Harvard University

Data Science

Visualization

Udacity

Introduction to Artificial Intelligence

Introduction to Statistics

Online Courses (for a fee)

365DataScience

Data Scientist Track

Booz Allen Hamilton

Explore Data Science

Coursera

Data Science Specialization

Executive Data Science Specialization

MIT

Tackling the Challenges of Big Data

Udacity

Exploratory Data Analysis

Intro to Data Science

Introduction to Hadoop and MapReduce

University of California, Irvine

Introduction to Data Science

Note: For similar lists, including undergraduate programs, see here and here and here and here

Resources

2019 Salaries Of Data Scientists

A Very Short History of Data Science

A Very Short History of Big Data

A Very Short History Of Artificial Intelligence (AI)

Harvard Data Science Review

 

 

 

Big Data and Data Science Events

September – December 2012

Now that most of the hype around big data has died down, overtaken by the buzz over the Internet of Things, we are sometimes treated to serious discussions of the state-of-the-art (or science, for that matter) in data analysis. If you are planning a career as a data scientist or you are a business executive trying to understand what the data scientists are telling you, three recent books provide excellent and accessible overviews:

The Analytics Revolution: How to Improve Your Business By Making Analytics Operational In The Big Data Era by Bill Franks

Data Mining For Dummies by Meta S. Brown

Data Science For Dummies by Lillian Pierson

Bill Franks is the Chief Analytics Officer for Teradata, and his specialty is translating complex analytics into terms that business users can understand. The Analystics Revolution follows Franks’ Taming the Big Data Tidal Wave, which was listed on Tom Peters’ 2014 list of “Must Read” books.

“With all the hype around big data, it is easy to assume that nothing of interest was happening in the past if you don’t know better from experience” says Franks. The over-excitement about big data caused many organizations to re-create solutions that already exist and build new groups dedicated to big data analysis, separate from their traditional analytics functions. As a correction, Franks advocates “a new, integrated, and evolved analytics paradigm,” combining traditional analytics on traditional data with big data analytics on big data.

The focus of this new approach–and the book–is Operational Analytics. It takes us from the descriptive and predictive analytics of traditional and big data analytics to prescriptive analytics. It pays close attention to the numerous decisions and actions, mostly tactical, taking place every day in your business. Most important, it places great emphasis on the process of analytics, on embedding it everywhere, and on automating the required response to events and changing conditions.

“Of course,” says Franks, “it takes human intervention to decide that an operational analytics process is needed and to build the process.”  But once the process is designed and turned on, the process accesses data, performs analysis, makes decisions, and then actually causes actions to occur. And humans are crucial to the success of this new brand of automated analytics, not only at the design phase, but also in the on-going monitoring and tweaking of the process.

An example of operational analytics is the development of an improved maintenance schedule using sensor data. There will be no value in the Internet of Things without an automated process for data analysis and action based on that analysis. “As traditional manufacturers suddenly find themselves embedding sensors, collecting data, and producing analytics for their customers, industry lines blur. Not only are new competencies needed, but the reason customers choose a product may have less to do with traditional selection criteria than with the data and analytics offered with the product,” says Franks.

The practical advice Franks provides in the book ranges from how to set up an analytics organization to developing and maintaining a corporate culture dedicated to discovery (finding new insights in the data and quickly acting on them) to implementing operational analytics. The Analytics Revolution is an excellent guide to the new business world of blurred industry lines and innovative data products.

If you are ready to move on from understanding the why of analytics today and how to think about it in a broad business and organizational context to a more specific understanding of the how of analyzing data, Data Mining for Dummies by Meta Brown should be your first step. The book was written for “average business people,” showing them that you don’t need to be a data scientist and “you don’t need to be an expert in statistics, a scientist, or a computer programmer to be a data miner.”

Brown is a consultant, speaker and writer with hands-on experience in business analytics. She’s the creator of the Storytelling for Data Analysts and Storytelling for Tech workshops. In Data Mining for Dummies, Brown tells the story of what data miners do.

It starts with a description of a day in the life of a data miner and goes on to discuss in clear, easy-to-understand prose all the key data mining concepts, how to plan and organize for data mining, getting data from internal, public and commercial sources, how to prepare data for exploration and predictive modeling, building predictive models, and selecting software and dealing with vendors. Data Mining for Dummies is an excellent step-by-step guide to understanding data mining and how to become a data miner.

If you are ready to move on from understanding data mining and being a data miner to more advanced tools and applications for data analysis, Data Science for Dummies by Lillian Pierson should be your first step. The book was written for readers with some technical and math skills and experience, but it aims to provide a general introduction to one and all: “Although data science may be a new topic for many, it’s a skill that any individual who wants to stay relevant in her career field and industry needs to know.”

Pierson is a data scientist and environmental engineer and the founder of Data-Mania, a start-up that focuses mainly on web analytics, data-driven growth services, data journalism, and data science training services. “Data scientists,” she explains, “use coding, quantitative methods (mathematical, statistical, and machine learning), and highly specialized [domain] expertise in their study area to derive solutions to complex business and scientific problems.

Data Science for Dummies is an excellent practical introduction to the fundamentals of data science.  It provides a guided tour of the data science landscape today, from data engineering and processing tools such as Hadoop and MapReduce to supervised and unsupervised machine learning, statistics and mathematical modeling, using open-source applications such as Python and the R statistical programming language, finding resources for publicly-available data, and data visualization techniques for showcasing the results of your analysis. Stressing the importance of domain expertise for data scientists, Pierson provides detailed examples of applying data science in specific domains such as journalism, environmental intelligence, and e-commerce.

“A lot of times,” says Pierson, “data scientists get caught up analyzing the bark of the trees that they simply forget to look for their way out of the forest.” The three books reviewed here provide a handy map to the maze of data analysis and a safe conduct pass for business executives, IT staff, and students, ensuring that they successfully get in and out of the data forest. Remember, as ones and zeros eat the world, data is the new product and operational analytics, data mining, and data science is the new process of innovation.