Program Curriculum

Welcome to our exciting course on business analytics! In this course, you will gain valuable insight into the world of statistical techniques and how they are used by managers to solve real-world business problems and make informed decisions. We will cover a range of regression techniques, exploring their applications across various target variables,  with a particular focus on their real-world applications, using captivating case studies and examples to bring the subject to life. In the final group projects, you will have the chance to put your newfound knowledge to the test by formulating your own business problems, applying the methods you have learned to extract key Business insights, and presenting your findings to your peers. Get ready to take your first steps towards becoming a skilled and confident business analyst!

This course will change the way students think about data and its role in business. Firms, governments, and individuals create massive collections of data as part of their everyday activities. Increasingly, businesses seek to exploit such data to improve decision making. In this course, students will study the fundamental concepts of data science, learn how to use predictive models for decision making, and develop their data-analytic thinking by discussing and working on multiple case studies. In most class sessions, students will implement data-driven solutions to business problems and discuss the pros and cons of these solutions with their classmates and course instructor.

See a preview of the course here: https://www.youtube.com/watch?v=aM-30uQb7mQ.

Covers various discrete and continuous probability models and their applications in business problems, estimation and testing of hypotheses, simple and multiple linear regression analysis.

This course discusses visual methods to explore, analyze, and communicate complex data sets in order to gain insights for making better data-driven business decisions. Emphasis is placed on how to develop an intuitive and efficient way to communicate with stakeholders with diverse and less technical background in the business world. Throughout the course, discussion is aided by implementation of common data visualization softwares such as Microsoft Power BI and Tableau. Essential features of these softwares will be introduced as part of the course.

The course presents concepts of social media and networks, methods and techniques to explore and analyze the data crawled from social media and network, and business application of data mining of social network. All these will be discussed in the context of finance and marketing. Python is the unique programming language for all cases and projects.

This course presents classical and modern approaches for analyzing multivariate and high dimensional data, including principal components, factor analysis, discriminant analysis, clustering, new developments in dimension reduction, large-scale covariance matrix estimation and multiple testing. All of these approaches will be covered in the context of Marketing, Finance and other important business areas. Computational issues for both traditional and new methodologies will also be discussed.

The science and technology of informed decision making with a focus on optimizing business processes. Spreadsheet decision modeling in Excel is used throughout. Emphasis is on problem formulation, spreadsheet-based solution methods, and managerial insights. Applications relate to managerial decision problems in diverse industries and functional areas including finance and accounting, human resources, marketing, and operations.

The course focuses on these common skills, knowledge and tools that are needed by all project managers to successfully manage a project. Topics include investment decisions, resource planning, budgeting, scheduling, earned value management, and risk assessment and control.

This course helps students develop better analytical skills in approaching strategic and tactical business decisions. Students will learn to derive solutions or conclusions that require critical thinking, quantitative analysis, and strategic reasoning skills. These skills are essential and indispensable for major business decisions.

Modern data analysis and the ability to conduct data analysis using statistical software such as R are becoming increasingly important in the era of big data. The course is designed to equip our students with such knowledge and skills. The course intends to introduce students to modern data analysis using R, emphasizing business applications. The main topics covered include data exploration methods, regression analysis, and time series analysis. After this course, students will be able to apply modern data analysis tools to analyze business data and use statistical software R to do estimation, modeling, and forecasting.

From a concern in the fringes of society, sustainability in ESG (Environment, Social, and Governance) has rapidly taken the center stage in economics, policy making, and business. Businesses are increasingly recognizing the need to balance economic growth with environmental and social responsibility. This course is designed to equip the students with the knowledge and tools to navigate the intersection of operations management and sustainability.

From resource optimization and waste reduction to ethical supply chain management and carbon footprint reduction, this course delves into the strategies and practices that drive organizations toward greater sustainability. By exploring real‐world case studies, the students will gain a comprehensive understanding of how sustainable operations can create value for both businesses and society.

In this course, we will study how and why companies integrate environmental and social sustainability concerns in managing their operations. You will learn about global regulatory environments (such as carbon taxes, cap‐and‐trade, and energy‐efficiency mandates), certification regimes (ISO 14000, fair‐trade labels, and other eco‐labels), and how firms respond to them. This course will rely on a mix of case studies to teach practical analytical sustainability frameworks such as life cycle assessment and sustainability disclosure reporting. Adopting a supply chain perspective, in this course, you will learn about opportunities for companies to implement sustainable business strategies at each stage in a supply chain:

• Green business models and new product development
• Sustainable resource extraction
• Responsible supplier sourcing
• End‐of‐life product recovery and recycling
   

Business is to make decisions and many managerial decisions ‐ in finance, operations, marketing, etc. ‐ are based on analysis using quantitative models. Tools in prescriptive analytics (e.g., data, models, software) have dramatically changed the way businesses operate in manufacturing, service, marketing, transportation, and finance. This course provides students with fundamental techniques of using data to make informed managerial decisions. We will focus on various ways of modeling (i.e., thinking structurally about) problems to enhance decision‐making skills. Rather than a survey of all the techniques, we stress those fundamental concepts that are most important for the practical analysis of management decisions. We will showcase how these core concepts are applied especially in machine learning. We focus on evaluating uncertainty explicitly, understanding the dynamic nature of decision‐making, using historical data and limited information effectively, simulating complex systems, and optimally allocating resources. It is impossible to teach students all there is to know about decision analytics techniques in one course; rather, the goal is to enable students to become intelligent users of prescriptive analytics techniques. So emphasis will be on how, what and why certain techniques and tools are useful, and what their ramifications would be when used in practice. This will necessitate some mechanical manipulations of formulas and data, but it is not our goal for you to become adept handlers of mathematical equations and computer software. To give students a perspective on how management science is used in practice, much of the material will be presented in the context of practical business situations from a variety of settings.

We will use the Gurobi optimization package accessed within a Python programming environment (Jupyter Notebook) in this course. Gurobi is an industrial package and provides a free one‐year license for academic purposes. Instructions on how to install Gurobi in a PC or Mac computer will be provided in the first session.

This course introduces students to business application modeling using Visual Basic Applications (VBA) in Excel. Students will learn to develop applications in different business areas, including finance, marketing, technology operations, etc. Essential features of VBA needed for application development will be introduced as part of the course and hence no prior experience with VBA is needed. Emphasis is on extensive hands-on problem solving.

This course introduces the emerging technological paradigm for managing "big data”. Topics covered include a range of big data technologies, such as HDFS, MapReduce, Spark, Hive, Pig, etc.

This course offers essential knowledge and tools for managers of digital business. Topics include e-commerce models, web analytics, Internet marketing, Internet pricing and strategy, web-based personalization, online-intermediaries, etc.

Deep Learning (DL) is a promising way for developing Artificial Intelligence (AI) applications. It is exceptionally useful for training a large amount of unstructured historical datasets, and predicting the most-likely outputs. DL can be applied in many business areas such as finance, marketing, customer services, information security and so on, and most importantly DL can outperform existing non-AI systems due to the nature of neural networks. This course intends to introduce Python programming language for developing DL business applications. Students will apply knowledge in current and future trends of DL to design DL business applications.

This course is about how to preprocess real business data using Python (mainly using pandas), identify and predict the patterns of data and finally explore the real-world application of these patterns.

The course covers the basic principles and approaches of computer simulation, and introduces Monte Carlo simulations with Excel and @risk, and discrete event simulation with Arena, focusing on their applications in solving business and operation problems.

This course focuses on the critical issues in the design, production and delivery of tangible goods, as well as intangible goods, in the business world. Topics include process analysis, capacity and bottleneck issues, waiting time management, inventory management, quality management, lean systems, supply chain management and e-commerce. Quantitative and qualitative tools will be taught to analyze the problems and create innovative solutions.

This course provides an overview of the underlying information technologies used in the finance, banking, and insurance industries. The course covers the critical business, legal and technology issues and the related risks faced by corporate executives when analyzing, designing, launching and managing Financial Technology projects to drive business innovations.

In this course, we will bridge this gap, investigating the technology and business of AI in theory and practice. We will examine what really AI is, how businesses should use it – and how they shouldn’t. The reality is that AI isn’t as complicated as everyone seems to be making it and that all Business Analytics graduates should be able to explain it to their colleagues, bosses, and customers. You will have this ability by the end of the course.

This course provides an overview of information privacy and management in the information economy. It covers the fundamental concepts and dimensions of privacy; the impact of Internet marketing, customer relationship management, Web personalization, and data mining on consumer privacy; privacy enhancing technologies; and regulation of business practices.

This course will teach you the key skills that management consultants use to solve business problems. It teaches you how to frame and structure problems, brainstorm solutions, decide recommendations and create a powerful story to communicate them. This course is intended for MSBA students who are exploring consulting as a career option or who want to be more effective at working out and communicating recommendations.

This course will teach you the key skills that management consultants use to solve business problems. This will be a demanding case-based course where you will learn the tools, skills and approaches adopted in leading management consultancies.

Learning Objectives

Students will know how to solve a business problem, including

• diagnose the situation
• structure the problem
• prove a hypothesis
• apply relevant frameworks
• apply fact-based analytical tools to identify insights
• design compelling communications to share their thinking.

This course gives students the opportunity to practice the skills learned in the previous courses on real-world projects sourced from real-world clients. In this course, students will focus on recommending a solution to a business problem. Later, in Strategic Analytics Implementation, students will focus on implementing this solution.

The course aims to teach students how to put the techniques they learned in previous courses (e.g., Management Consulting) into practice within a corporate environment. The course focuses on crafting a business case for leveraging AI, traditional analytics, or some other technology to meet a strategic or operational business need. This means that students must not only improve their understandings of the theoretical and analytical frameworks learned in previous courses to professional standards, but also learn how to match the right frameworks for the right situations.

The course provides students with the training and hands-on coaching required for them to succeed in gaining approval for a business case from senior clients. The focus will be on understanding and proposing how to change the business strategy and operational processes involved, as well as on understanding the strategic impact of various technologies including AI and analytical tools, upon strategy and operations. To accomplish this, students will also have to learn how to manage key stakeholders and present their recommendations in a professional and appropriate way.

Course Specifics

MGMT 6051F/H are less traditional academic courses than a university-based consultancy (i.e., “lab”) running data-related business projects for corporate clients. All enrolled students will be divided into distinct teams, with each team being responsible for completing one project by the end of the semester. Each team will receive guidance on how to run projects from the instructor and teaching associate.

Following best practices in data science research labs, each project team will report on its progress through weekly project updates and receive feedback from the instructor and other project teams based on these updates. The goal is for the student teams to learn by doing and to deliver useful results to the client.

Class sessions will include one or more of the following elements:

• Instructor lectures will introduce techniques for conceptualizing, managing, and implementing data-oriented projects within a client setting.
• Project updates, in which each project team will report their progress during the previous week, will be an integral part of every class session after the first. Project updates in Strategy Consulting Analytics will focus on the business side (i.e., identifying key questions and issues, determining how to address these points, planning how to achieve these goals). Later, Operations Consulting Analytics will shift to technical execution and implementation.

There will be no required readings assigned for the course, but the instructor may make recommendations on relevant readings.

Intended Learning Outcomes

Your intended learning outcomes for the course will include:

• You will learn how to apply consulting techniques in a corporate environment, working with client-side executives, technologists, and data scientists to deliver the right solutions for the right problems.
• You will learn how to apply technical knowledge within a business environment to solve real problems.
• You will learn how to leverage statistical analyses and machine learning algorithms in a corporate data environment, working with client-side personnel to obtain access to data and analysis platforms.
• You will learn how to understand client-side stakeholder needs to manage the project and affect how the results will be received by the clients. 

This course continues the real-world projects initiated in Strategy Consulting Analytics and Operations Consulting Analytics, focusing on implementing the solution to the business case recommended during Strategy Consulting Analytics and Operations Consulting Analytics.

The course aims to teach students how to implement technology and analytics-oriented business cases within a corporate environment. The course focuses on implementing the business case for leveraging AI, traditional analytics, or some other technology to meet a strategic or operational business need that was developed and received client approval.

The course focuses simultaneously on teaching students how to implement new business processes (e.g., via change management) and new technologies (e.g., AI). The key insight that students should learn is that it is never possible to implement new technologies without correspondingly changing the business processes involved. It is the co-evolution of business processes and technologies that poses a challenge, and this is what students will be learning and working on in the course.

The course provides students with the training and hands-on coaching required for them to succeed at managing this co-evolution. The focus will be on building technology to work within the context of client-side processes and standard operating procedures. To do so, students will not only have to build the appropriate technologies, but also be guided by the business needs. Students will also have to manage key stakeholders along the way.