You’ve probably heard that data science degrees are hot, hot, hot. Research by MGI and McKinsey’s Business Technology Office predicts that “…by 2018, the United States could face a shortage of 140,000 to 190,000 people with deep analytic skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.”
Where will all these folks with “deep analytic skills” come from? More and more data analytics and data science programs are remodeled or started every year. But we all know that not all graduate programs are created equal. Who offers the best graduate data science programs?
Not to miss out on a good blog post topic, we’re wading into the discussion. But, as usual when we look for “the best” of anything, the topic isn’t simple as it first seems.
Who’s the Best, and What do Top Programs Have in Common?
Lists of top data analytics programs almost never agree. They describe slightly different programs: data analytics versus business analytics and predictive analytics and so on. But, several interesting points turn up consistently:
- Basic requirements include programming, statistics and other mathematics. No surprise here. However, the technical level of student experience varies by the type of analytics. Predictive and data analytics programs require more technical mojo, business analytics somewhat less.
- A bit of creativity always helps. Becoming a data science ace involves more than being a math or programming wizard. It requires creative experimentation, which means intuition and creativity count, too.
We’re looking at the program requirements and focus of the five schools that come up in lists more often than anyone else.
Harvard Data Science Course
Degree: Data Science Certificate
Primary focus: Learning from data to gain useful predictions and insights.
Pre-program requirements: Programming experiences and a basic understanding of statistics.
Take-away skills: teach students to deal with data (collect and prepare it), analyze the collected data and make useful predictions. Specific skills include:
- Wrangling, cleaning, and sampling data to get a suitable data set.
- Managing and getting access to big data quickly and reliably.
- Performing exploratory data analysis to generate hypotheses.
- Making predictions based on statistical methods such as regression and classification.
- Communicating results through data visualization, stories, and interpretable summaries.
University of California at Berkeley
Degree: Master of Information and Data Science (MIDS)
Program focus: make sense of real-world phenomena and everyday activities by mining and compiling big data to discover patterns, relationships and trends.
Pre-program requirements:
- High level of quantitative and analytical reasoning abilities
- Ability to solve problems
- Working knowledge of fundamental data science concepts
- Effective communications
- Programming proficiency
Take-away knowledge and skills:
Concepts of information systems and their role within organizations. Topics include:
- Organizational structure and behavior
- Types of information systems, hardware and software issues
- Data collection tools and techniques, issues of complexity, and the relevance of information systems to larger social issues like sustainability
Stanford University
Degree: Master of Science in Statistics (Data Science emphasis)
Program focus:
- Gain expertise in data science and its applications
- Attract engineering, science students and mathematically oriented students
Pre-program requirements: Heavy emphasis on math and computer programming
Take-away skills: Better understand the mathematical and statistical underpinnings of data science.
Carnegie Mellon University:
Degree:
- Master of Information Systems Management
- Several emphases offered, each with different program lengths and prerequisites.
- Following details describe business intelligence/data analytics emphasis
Program focus:
- Hands-on learning in business intelligence, data analytics and information technology.
- Use applied business methods to acquire skills needed for an analytic technology practice.
Pre-program requirements:
- Relevant work experience preferred (for BI/data analytics emphasis)
- Ability to synthesize complex quantitative and qualitative concepts
Take-away skills:
- Business process analysis and optimization
- Data warehousing management,, data mining and predictive modelling,
- GIS mapping, analytical reporting, segmentation analysis, and data visualization.
North Carolina State University:
Degree: Master of Science in Analytics (MSA)
Program focus:
- Produce future leaders who understand how to extract insight and value from vast quantities of data
- A carefully designed mix of statistics, other types of applied mathematics, computer science, and business practices.
- A hands-on approach to learning.
Pre-program requirements:
- A strong aptitude for mathematics and statistical programming
- Passion for working with data to solve challenging problems
Take-away skills:
- Engage in analytics as part of a team.
- Find and effectively communicate practical understanding gained from a vast quantity and wide variety of data.
- Learn how to tackle real-life analytics problems with data provided by industry and government sponsors.
- Learn how to use the latest analytics tools such as Hadoop and SAS.
