Program overview
Gain an interdisciplinary understanding of the essential fundamentals of analytics, including analysis methods, analytical tools, such as R, Python and SQL, and business applications.
Using common analytics software and tools, statistical and machine learning methods, and data-intensive computing and visualization techniques, learners will gain the experience necessary to integrate all of these parts for maximum impact.
Project experience is also included as part of the MicroMasters® program. Through these projects, learners will hone their skills with data collection, storage, analysis, and visualization tools, as well as gain instincts for how and when each tool should be used.
These projects provide hands-on experience with real-world business applications of analytics and a deeper understanding of how to apply analytics skills to make the biggest difference.
What you will learn
- Use essential analytics tools like R, Python, SQL, and more.
- Understand fundamental models and methods of analytics, and how and when to apply them.
- Learn to build a data analysis pipeline, from collection and storage through analysis and interactive visualization.
- Apply your new analytics skills in a business context to maximize your impact.
Program Class List
1Computing for Data Analysis
Course Details
1
A hands-on introduction to basic programming principles and practice relevant to modern data analysis, data mining, and machine learning.
2Data Analytics for Business
Course Details
2
This course prepares students to understand business analytics and become leaders in these areas in business organizations.
3Introduction to Analytics Modeling
Course Details
3
Learn essential analytics models and methods and how to appropriately apply them, using tools such as R, to retrieve desired insights.
Meet your instructors
Joel Sokol
Director of the Master of Science in Analytics program
He received his PhD in operations research from MIT and his bachelor’s degrees in mathematics, computer science, and applied sciences in engineering from Rutgers University. His primary research interests are in sports analytics and applied operations research. He has worked with teams or leagues in all three of the major American sports. Dr. Sokol's LRMC method for predictive modeling of the NCAA basketball tournament is an industry leader, and his non-sports research has won the EURO Management Science Strategic Innovation Prize. Dr. Sokol has also won recognition for his teaching and curriculum development from IIE and the NAE, and is the recipient of Georgia Tech's highest awards for teaching.
Richard W. Vuduc
Associate Professor of Computational Science and Engineering
Associate Professor of Computational Science and Engineering at the Georgia Institute of Technology. He received his Ph.D. in Computer Science from the University of California, Berkeley.
Sridhar Narasimhan
Professor at The Georgia Institute of Technology
Sridhar Narasimhan is Professor of IT Management and Co-Director -Business Analytics Center (BAC), Scheller College of Business. The BAC partners with its Executive Council companies in the analytics space and supports Scheller’s BSBA, MBA, and MS Analytics programs. Professor Narasimhan has developed and taught the MBA IT Practicum course. Since 2016, he has been teaching Business Analytics to undergraduate and MBA students at Scheller. Professor Narasimhan is the founder and first Area Coordinator of the nationally ranked Information Technology Management area. In fall 2010, he was the Acting Dean and led the College in its successful AACSB Maintenance of Accreditation effort. He was Senior Associate Dean from 2007 through 2015.
Charles Turnitsa
Professor at The Georgia Institute of Technology
