Бакалавриат
2022/2023
Основы бизнес-аналитики и анализ данных
Статус:
Курс по выбору (Совместная программа по экономике НИУ ВШЭ и РЭШ)
Направление:
38.03.01. Экономика
Кто читает:
Отдел сопровождения учебного процесса в Совместном бакалавриате ВШЭ-РЭШ
Где читается:
Факультет экономических наук
Когда читается:
3-й курс, 1, 2 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
6
Контактные часы:
64
Course Syllabus
Abstract
This business analytics course consists of two parts, each dedicated to its own business function: operations and marketing. Both operations and marketing rely extensively on analytics, but in slightly different ways. Roughly speaking, the goal of operations is to reduce costs while the goal of marketing is to increase revenues. Therefore, operations tends to look inward: it aims to put the productive resources inside the company to good use, emphasizing optimization problems; and marketing tends to look outward, focusing more on data analysis of customer data. In the operations part of the course, we will focus on two themes: making optimal decisions and dealing with uncertainty and risk. We will start with basic business process analysis and find out how it could be analyzed using linear optimization techniques. Next, we will introduce uncertainty in our models of business processes and find out how Markov modeling techniques can help us understand and manage the resulting inefficiencies. (For example, how should an intensive care unit admit its patients?) After that, we will combine the two perspectives by looking at inventory management under uncertainty. For example, how should a fashion retailer decide on the order quantity for a new cool T-shirt? Developing these ideas, we will arrive at basic models of stochastic optimization, and apply them to strategic problems: for example, if a company is investing in two new car factories, should these factories be able to produce multiple car models or should they be focused on a single one? Finally, we will arrive at multi-period models that could be solved with dynamic programming, which have applications from retail assortment planning to plane ticket selling to patient appointment scheduling. The marketing part of the course will adopt the following perspective. Marketing as a business discipline is highly misunderstood. People often think of marketing in terms of highly visible, specialized, tactical activities, such as advertising, promotions and sales. That is wrong. Marketing is much more than specialized tactics. Marketing is the science of managing value, a process that entails analytical, strategic and tactical activities. This course will provide you with a sound framework to understand marketing as a value management process, as well as introduce several fundamental data analytics approaches for marketing applications. The course involves a mix of lectures and case-based discussions that will teach you an analytical approach to understanding, identifying and creating value
Learning Objectives
- Learn how to analyze the environment in which a company operates
- Learn how to develop a marketing strategy, and design actionable marketing tactics.
Expected Learning Outcomes
- -Learn about “cutting edge” directions in modern marketing academic research.
- - Acquire an understanding of the basic marketing concepts
- - Apply statistical methods to analyze different aspects of the environment - Identify and address the key decisions facing marketing managers - Practice the process of analyzing a marketing situation or opportunity, formulating market strategy, and developing and implementing a marketing plan - Learn about “cutting edge” directions in modern marketing academic research.
- - Identify and address the key decisions facing marketing managers
- - Practice the process of analyzing a marketing situation or opportunity, formulating market strategy, and developing and implementing a marketing plan
Assessment Elements
- Class attendance and participation10% of the final grade
- Homeworks (Operations analytics)32% of the final grade
- Midterm project13% of the final grade
- Mini-Assignments5% of the final grade
- Homeworks (data assignments and cases)25% of the final grade
- Final project15% of the final grade
Interim Assessment
- 2022/2023 1st module0.5 * Midterm project + 0.5 * Homeworks (Operations analytics)
- 2022/2023 2nd module0.25 * Final project + 0.25 * Homeworks (data assignments and cases) + 0.25 * Mini-Assignments + 0.25 * Class attendance and participation
Bibliography
Recommended Core Bibliography
- Matt Taddy. (2019). Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions. McGraw Hill.
Recommended Additional Bibliography
- Hochreiter Ronald. (2017). Applied Mathematical Programming and Modelling 2016. ITM Web of Conferences, 14, 00001. https://doi.org/10.1051/itmconf/20171400001