Foundational knowledge for business analytics, including strategies, methods, and tools integrated with hands-on skills for defining business analytics for data-driven decision making and innovation.

Overview of highly computational modern statistical learning methods; applications of logistic regression, neural networks, LASSO, trees, boosting and GAM, etc., to finance and marketing data using Python.

Data cleaning and reshaping; good vs. bad graphics; univariate, bivariate, trivariate, hypervariate, and time series graphics; interactive graphics; web-related computing. Extensive computer applications using Python.

SQL; relational database systems; data storage; data manipulation; data aggregation.

NoSQL; semi-structured and unstructured databases; data storage; data manipulation; distributed databases.

Decision making under uncertainty using real data applying the most advanced optimization, statistical and probability methods using Python.

Supervised on-the-job business experience in the student’s area of interest (Curricular Practical Training)

Internal and external communication, research methods, reports for decision-making, oral presentations and briefings, strategies to assure communication; field studies.

Data analysis technologies for business decision making; principles and techniques of statistical inference for business problem solving; foundations of data-driven regression and time series analytics.

Managing Business models in digital platform ecosystems; designing new products and services for digital platforms; establishing digital platform leadership; assessing emerging niches in digital spaces.

Advanced applications of data analytics in dynamic strategy formulation and execution; analytics and business methods for data connected enterprises to continuously enhance their competitive advantage.

How companies can implement ‘big data’ initiatives to improve business activities. How leading companies have successfully implemented ‘big data’ initiatives and why some have failed.