Chapter 6 Classes

Breakdown of curriculum for MBAn program. Subject to change.

6.1 Summer Term

6.1.1 Introduction to Data Programming - MBAN 550

This course will provide an introduction to computer programming for business analytics applications using a suitable language like Python or R. The course will cover essential programming concepts such as object-oriented programming, control structures and functions with a focus on developing student skills for working with data. The course will further explore essential libraries and packages available for business analytics applications.

Professor: Bobby Madamanchi

Units: 1.5

Classes per week: two 1 hour 30 min lectures

6.1.2 Advanced Spreadsheet - MBAN 551

Spreadsheets are among the most widely used tools in business analytics. This course introduces students to advanced spreadsheet functionalities including financial, statistical, and time/date functions, goal seeking, data tables, and optimization. Students will learn how to develop macros and use powerful Add-ins that enhance spreadsheet capabilities.

Professor: Ali Hojjat

Units: 1.5

Classes per week: two 1 hour 30 min lectures

6.1.3 Business Immersion - BA 500

The course will equip students with the appropriate understanding, intuition, and language to understand the larger context of business study.

Professor: John Branch

Units: 2.25

Classes per week: two 2 hour 20 min lectures

6.1.4 Software Teams and Project Management - MBAN 501

This course has two interrelated components: Team dynamics and managing software development projects; presented in an integrated fashion. Team dynamics content will include such topics as the emergence of behavioral norms in project teams, team decision making, potential sources of conflict, and managing conflict constructively. Project management content will include topics such as Lean Startup principles and Minimum Viable Products, development approaches (e.g. agile, traditional, waterfall), integrating software tools and dealing with the inevitable surprise changes to timing, scope and content.

Professors: Jeff Domagala & Sanjeev Kumar

Units: 2.25

Classes per week: two 2 hour 20 min lectures

6.2 Fall Term A

6.2.1 Managerial and Financial Accounting - MBAN 502

The course explores the use of accounting systems for both external communication (financial) and internal management (managerial). The course will introduce basic concepts used in the construction of corporate financial statements, how to understand those statements, and how to measure key indicators of corporate performance. On the managerial side, the course will cover how accounting information is used internally to measure and track the performance of a firm on its key success factors, and the use of those measures in management decision-making.

Professor: Christopher Williams

Units: 2.25

Classes per week: two 2 hour 20 min lectures

6.2.2 Probability and Statistics - MBAN 552

This course will cover fundamental topics in probability and random variables, statistical inference techniques including hypothesis testing, and linear regression.

Professor: Stefanus Jasin

Units: 1.5

Classes per week: one 3 hour lecture

6.2.3 Predictive Analytics - MBAN 553

This course introduces students to a supervised learning approach to building predictive models to inform managerial decision making. The class will build on a foundation of linear regression and logistic regression and extend to machine learning approaches such as decision trees, support vector machines, naive Baynes and neural networks.

Professors: Anocha Aribarg & Eric Schwartz

Units: 1.5

Classes per week: two 2 hour 20 min lectures

6.2.4 Data Exploration and Visualization - MBAN 554

This course will teach the essential tools in exploration and visualization of large data sets. After taking this course, students will be able to work with large datasets and form initial hypotheses based on data exploration and visualization, and effectively communicate their analysis using appropriate visuals.

Professor: Lennart Baardman

Units: 1.5

Classes per week: two 2 hour 20 min lectures

6.3 Fall Term B

6.3.1 Decision Strategies - MBAN 503

Many managerial decisions are increasingly based on analysis using quantitative models. This course will introduce a systematic approach to the value and use of data to make informed decisions. The course will stress fundamental concepts that are important to understand when using data in decision-making; for example the value of historical data (when does the past inform the future, and when not), the value of information (when to gather more, or not), evaluating uncertainty, testing hypotheses with limited information, and understanding the dynamic nature of unfolding information and decision-making.

Professors: Achyuta Adhvaryu & Uday Rajan

Units: 2.25

Classes per week: two 2 hour 20 min lectures

6.3.2 Data Architecture and Acquisition - MBAN 555

This course will focus on providing students with an understanding of the underlying IT infrastructure including Enterprise Systems and RDBMS Systems - and how to acquire data from those systems for exploration, cleaning and analysis using tools such as SQL and APIs. The course will help students understand the data generation, storage and processing ecosystem, the role of enterprise architecture and data architecture and their impact on decision making.

Professor: Sanjeev Kumar

Units: 1.5

Classes per week: two 1 hour 30 min lectures

6.3.3 Unsupervised Learning - MBAN 556

This course provides a broad introduction to different unsupervised machine learning algorithms that have potential business applications. The topics covered in the course may include clustering algorithms such as K-means clustering and dimension reduction algorithms such as factor or principal component analysis.

Professor: Sanjeev Kumar

Units: 1.5

Classes per week: two 1 hour 30 min lectures

6.3.4 Causal Inference through Experimentation - MBAN 557

In making business decisions, managers often need to understand how their strategic and tactical decisions (e.g., a price change) can casually affect outcomes of interest (e.g., revenues). Observational data can help suggest a pattern of relationship between variables but such a relationship may not be casual. In this course, students will learn how to make causal inferences through experimentation. Students will acquire the skills to design controlled randomized experiments (e.g., A/B tests, field experiments) and properly analyze experimental data.

Professor: A. Yesim Orhun

Units: 1.5

Classes per week: two 2 hour 20 min lectures

6.4 Winter Term

6.4.1 Business Analytics Consulting Studio

In this course, you’ll work in teams to find creative solutions to a pressing business analytics challenge at a real organization, and go onsite to conduct research and present your final recommendation to leadership.

6.4.2 Prescriptive Analytics

This course will focus on the theory and practice of the core optimization methodologies such as linear programming, integer programming, non-linear programming, heuristic models, and simulations. Example applications will be drawn from an array of business functions.

Professor: TBA

Units: TBA

Classes per week: TBA

6.4.3 Information Security, Privacy and Ethics

The course will introduce fundamental concepts of network security, cyber security, potential threats/malware, and policies/practices to manage security threats; and discuss relevant technical aspects of information security such as authentication approaches, data encryption, digital signature, and public key.

Professor: TBA

Units: TBA

Classes per week: TBA