Master in Business Analytics Concentrations

COURSE CODE: ba865

This course will introduce you to the Python programming language and the ecosystem of software packages needed for Data Science and to build and train Neural Networks in Python, including: NumPy, Pandas, SKlearn, and PyTorch. After reviewing key Python building blocks, the course will focus on Neural Networks and Deep learning Concepts and implementation in PyTorch. This is an intensive course and the majority of it will be presented through interactive python notebooks (Google Colab).

COURSE CODE: ba843

COURSE CODE: ba885

In this course we will open the neural network (NN) "black box" and examine how these mathematical modeling tools evolved to become the powerful data analysis engines that many companies rely on today. We will start with simple, comprehensible, few neuron models that we can build from scratch on our devices, and byte by byte grow our skills to understand and manipulate the enterprise-scale networks with complex architectures that are currently used in businesses ranging from Alpha-Go to Tesla. As we explore the mathematical and computational representations of different network architectures, you will obtain a solid understanding of how to choose and customize NN models that fit best to the task at hand, aware of their strengths and challenges, and what these mean for practitioners in business analytics. Whenever possible, we will draw examples related to global challenges such as climate crisis.

COURSE CODE: ba882

This course will equip students with the essential skills for transitioning data analysis and machine learning tasks to the cloud, supporting production workloads. It covers the creation and deployment of data and ML pipelines, including those for generative AI applications, with a focus on data integration strategies, cloud data warehousing, BI, and ML-Ops. Leveraging prior coursework in data management and machine learning, students will learn to implement ETL/ELT processes, monitor data quality, and deploy models as APIs using cloud services.

COURSE CODE: ba878

This course is designed to provide students with a deeper understanding of the key concepts, methods, and tools in data science, machine learning, and data infrastructure applied to the world of health care. The course will cover both theoretical foundations and practical applications of these topics, with a focus on the integration of data science techniques with data infrastructure. The course will include hands-on examples from real world data sets the will enhance skills and experiences in health care. In addition to reviewing key steps in the data science process (i.e. data preparation, exploratory data analysis, feature engineering, model selection, model evaluation, and model deployment) and machine learning techniques, we’ll explore how to use, apply, and deploy them in various healthcare settings. Students will learn about data architectures, distributed data processing systems, data pipelines, data transformation, and data visualization tools, and how different healthcare players are solving data challenges at scale. By the end of the course, students will have developed a deeper understanding of data science, machine learning, and data infrastructure, and will be able to apply these concepts to solve complex problems in a variety of healthcare domains across a multitude of data types.

COURSE CODE: hm817

For students interested in enhancing their skills for positions in the rapidly growing field of healthcare information technology, this course utilizes healthcare delivery, life science, and business leaders to deliver current perspectives and trends in the use of information technology (IT) to enhance patient care outcomes while delivering more cost-effective care. Students explore various methods and approaches that can be applied to both develop and evaluate IT systems encompassing electronic medical records, administrative systems, healthcare mobile applications, and the emerging field of digital therapeutics. The course provides strategic perspectives important to the healthcare chief information officer, chief medical information officer, other managers, prospective healthcare IT entrepreneurs, and users of both clinical and administrative IT systems. Its focus is not on the technical specialist.

COURSE CODE: hm848

This course examines an array of compelling opportunities for innovation, incremental and disruptive, across products and services, created within existing organizations or by starting new businesses. It bridges design and implementation, examining the unique and complex array of elements that make successful innovation in the health sector so difficult, and developing the skills and knowledge needed to effectively address those challenges. The course provides a conceptual framework, and then emphasizes hands-on engagement, concrete exercises, written cases, and in-class speakers who are engaged in real-world innovation initiatives. Students will have the opportunity to focus on areas of particular interest and relevance to current or future work. They will leave better equipped to drive or support the viable, value-creating innovation so desperately needed in the health sector.

COURSE CODE: ba860

This is an introductory course on Digital Marketing emphasizing analytics that seeks to familiarize students with digital marketing tactics. At the heart of marketing lies consumers and their marketing journey through the stages of awareness, intent, conversion and finally retention. In this course, we will learn how digital has revolutionized the interactions between firms and consumers along this journey. Digital offers powerful tactics to reach consumers along the funnel: online display ads raise awareness, search listings reach consumers with intent, on-site e-commerce marketing facilitate conversion, and social medial both energizes and retains customers.

COURSE CODE: mk864

This course focuses on the practical needs of the marketing manager making pricing decisions. Students learn the techniques of strategic analysis necessary to price more profitably by evaluating the price sensitivity of buyers, determining relevant costs, anticipating and influencing competitors' pricing and formulating an appropriate pricing strategy.

COURSE CODE: mk852

This course will focus on developing marketing strategies driven by marketing analytics. Topics covered include market segmentation, targeting, and positioning, new product test marketing, market response models, customer profitability, social media, and marketing resource allocation. The course will draw on and extend students’ understanding of issues related to quantitative analysis and principles of marketing. The course will use a combination of cases, lectures, simulations, and a hands-on project to develop these skills.

COURSE CODE: mk856

Marketing, in particular, begins and ends with the consumer – from determining consumer needs to ensuring customer satisfaction. In this course, we will explore the most recent scientific research in marketing, psychology, and behavioral economics related to consumer behavior. We will develop your ability to understand and influence what people want, how people decide what and when to buy, and whether people will be satisfied or dissatisfied with their decisions. These psychological insights are particularly useful for marketing strategy, brand positioning, and marketing communication decisions, but also yield insight into common biases in judgment and decision making, beyond marketing, to which you would otherwise fall prey. Why people are willing to drive across town to save $5 on a tank of gasoline, for example, when they would not drive a minute to save $5 on a refrigerator. We will discuss some of these applications in class. In addition, we will examine the methodology of market research (specific to consumer behavior) to build the tools you will need to interpret and base decisions on it. Readings will include primary empirical research articles (e.g., Journal of Consumer Research articles), business journal articles, and research reviews (e.g., Harvard Business Review articles). The course includes lecture, discussion, an exam, and a team term project.