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Бакалаврская программа «Социология и социальная информатика»

Business Analytics

2021/2022
Учебный год
ENG
Обучение ведется на английском языке
5
Кредиты
Статус:
Курс по выбору
Когда читается:
3-й курс, 1, 2 модуль

Преподаватель

Course Syllabus

Abstract

The course is targeted at undergraduate social science students aiming at careers in business-oriented jobs in marketing, sales and service analytics. The course consists of lectures and seminars. The lecture part provides a gentle introduction to several fundamental concepts in business analytics (lifetime value, churn, segmentation) and analytical techniques (binary logistic regression, complexity reduction, survival analysis, cluster analysis) while guiding students through tailored cases relevant to the data economy. The seminar part is blended with MOOCs that introduce specific data analysis techniques as tools for solving typical problems in business analytics. We will discuss the business context of each case, use analytical techniques that solve the tasks at hand and discuss the effective delivery of results in reports. This is a rather intense course that requires motivation and genuine interest in business analytics, teamwork, and a large amount of independent study. To succeed in the course, participants are expected to be familiar with the linear regression fundamentals and data management techniques in R.
Learning Objectives

Learning Objectives

  • to stimulate students to apply the methods and concepts they learned in data analysis and research methods to solving practical business and marketing analytics tasks.
Expected Learning Outcomes

Expected Learning Outcomes

  • Students can plan the analytical data cycle, from formulating requests for data collection, to data cleaning and dimension reduction, to data analysis and reporting the recommendations
  • Students collect information on the business context of a given case and evaluate possible solutions to a given task
  • Students perform various statistical analyses, use them appropriately, and develop suggestions (recommendations, policies, scenarios) for the task (churn prevention, segmentation, etc.)
  • Students select data features to be used in segmentation procedures; as a team, students develop analytical pipelines covering necessary tasks and combining individual results into a summary report
  • Students solve analytical problems using data analysis techniques suitable for the task; develop policies and scenarios using several methods of analysis
  • Students work individually and in teams to interpret the results and develop policies and scenarios for the company
Course Contents

Course Contents

  • The trade of business analytics
  • Introduction to Data Analysis for Consumer Behavior and Client Analytics. Customer Lifetime Value
  • Customer Churn. Churn Prevention
  • Predicting Customer’s Time to Churn
  • Customer Segmentation and Cohort Analysis
  • What-If Analysis
  • Consumer Preferences
  • Customer Satisfaction
  • Market Basket Analysis
Assessment Elements

Assessment Elements

  • non-blocking Online Practice
  • non-blocking Engagement
  • non-blocking Project 1
  • non-blocking Project 2 (ind)
  • non-blocking Project 2 (team)
    Important: if working together online, keep an online document with your ideas and progress (e.g., in Google Docs or MS Teams) and submit the link to this file along with your project.
  • non-blocking Career Essay
Interim Assessment

Interim Assessment

  • 2021/2022 1st module
  • 2021/2022 2nd module
    0.1 * Career Essay + 0.25 * Engagement + 0.15 * Online Practice + 0.2 * Project 1 + 0.1 * Project 2 (ind) + 0.2 * Project 2 (team)
Bibliography

Bibliography

Recommended Core Bibliography

  • Chapman, C., & Feit, E. M. (2015). R for Marketing Research and Analytics. Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=964737
  • Provost, F., & Fawcett, T. (2013). Data Science for Business : What You Need to Know About Data Mining and Data-Analytic Thinking (Vol. 1st ed). Beijing: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=619895

Recommended Additional Bibliography

  • Struhl, S. M. (2017). Artificial Intelligence Marketing and Predicting Consumer Choice : An Overview of Tools and Techniques. London: Kogan Page. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1494508