Thomas Miller serves as faculty director of the MS in Predictive Analytics program at Northwestern University. He has designed numerous courses for the program, including Marketing Analytics, Advanced Modeling Techniques, Data Visualization, and the capstone course. Before joining the faculty at Northwestern, Miller spent fifteen years in business IT in the computer and transportation industries. He is also a consulting editor to Pearson Education in practical data science, and his books include “Data and Text Mining: A Business Applications Approach” and “Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science,” among others. Miller holds a Ph.D. in Psychology (psychometrics) and a Master’s degree in Statistics from the University of Minnesota, and an MBA and Master’s degree in Economics from the University of Oregon.
To learn more about Northwestern University’s online Predictive Analytics program, visit the program website.
The gap you refer to is a result of advances in information and communication technology. Organizations are collecting more data than ever before, and the costs of storing data have fallen. Many universities are introducing relevant programs of study such as those you list on your website.
Also, managers are using data more extensively in making critical business decisions. Many are less resistant to analysis and modeling. Managers “in the know” understand that if they are not informed by data, they will fall behind the competition.
It’s also good to be curious, have a learning attitude, and be open to new ideas and technologies.
Data science is multi-disciplinary. Analytics professionals need to understand the languages of IT, modeling, and business. Finding people with this multi-disciplinary background can be difficult. An alternative, of course, is for firms to build cross-functional teams bringing IT, modeling, and business management professionals together.
Check the faculty: Do they have business experience? Do they have doctoral degrees? What fields are represented by these degrees? Look for a diverse faculty from IT, business, engineering, statistics, mathematics, and the sciences. If everyone on the faculty come from the same discipline, such as IT, be wary. Data science is multi-disciplinary and the faculty should reflect that fact.
Check the curriculum: Does it offer a wide range of options? Is there sufficient coverage of both traditional statistics and machine learning? Are there courses dealing with data preparation (data munging) as well as database systems? Are there specialized courses covering marketing, finance, risk, web and network data science, and text analytics? Is there a program in analytics entrepreneurship? How about specialized offerings such as sports analytics?
Check the software environment: Does it use open-source tools such as R and Python computer languages, relational (SQL) and NoSQL databases?
Check the course descriptions or syllabi: Do courses include challenging real-world problems and business case studies? Do they involve programming? Do they utilize database systems across the curriculum (not just in the database courses)?