Data Analytics in Business
Georgia Tech · MS in Analytics
I serve as the Lead Graduate Associate for MGT 6203 a core course in Georgia Tech's MS in Analytics, offered each semester to on-campus master's students and to learners worldwide through the edX MicroMasters. I coordinate the teaching team and support students as they work through the material below.
Turning data into business decisions
Businesses, consumers, and societies now leave behind massive amounts of data as a by-product of their activities, and leading companies in every industry use analytics to replace intuition and guesswork in their decision-making. This course prepares students to lead in that shift.
MGT 6203 teaches the scientific process of transforming data into insight. It introduces a series of econometric, statistical, and machine-learning models covering core concepts, underlying theory, model development, algorithm design, and implementation in R against real-world business data. Through extensive demonstrations and exercises, students gain hands-on experience applying these methods to practical problems, learning both cutting-edge technique and the judgment to apply it properly and avoid common pitfalls.
What students learn
Set up popular econometric models and reason about their key properties and applicability.
Explain the core algorithms behind common machine-learning methods and the problems they solve.
Develop econometric models from linear regression to binary-response, censored, count, survival, and discrete-choice models.
Diagnose endogeneity and perform causal analysis using instrumental variables.
Implement unsupervised methods k-means clustering, collaborative filtering, and text mining.
Design and implement sophisticated classifiers with neural networks and deep learning.
Use the R language and its ecosystem to implement each analytic method.
Match the right analytic method to the business context where it applies.