Магистратура
2020/2021
Статистические методы анализа
Лучший по критерию «Полезность курса для Вашей будущей карьеры»
Лучший по критерию «Полезность курса для расширения кругозора и разностороннего развития»
Статус:
Курс обязательный (Менеджмент и аналитика для бизнеса)
Направление:
38.04.02. Менеджмент
Кто читает:
Департамент менеджмента
Где читается:
Санкт-Петербургская школа экономики и менеджмента
Когда читается:
1-й курс, 2 модуль
Формат изучения:
без онлайн-курса
Прогр. обучения:
Менеджмент и аналитика для бизнеса
Язык:
английский
Кредиты:
6
Контактные часы:
48
Course Syllabus
Abstract
The course covers a wide range of statistical methods and some important machine learning techniques used in today’s business analytics for exploratory and segmentation analysis, as well as for the estimation of relationships and predictive modeling. Students will get ready for data management and the analysis of survey, sales and other types of data commonly used in marketing and management. Students will learn how to use the R language – the most popular language for statistical computing, modeling and data management thanks to the fact that 50% of the course is dedicated to hands-on R coding.
Learning Objectives
- Choose methods adequately corresponding to the objectives of a research project
- Collect, store, process and analyze data according to high standards
- Conduct empirical research in management and marketing using modern analytic software tools
- Develop and apply new research methods
- Solve economic and managerial problems using best practices of data analysis using modern computational tools
Expected Learning Outcomes
- Choose methods adequately corresponding to the objectives of a research project
- Collect, store, process and analyze data according to high standards
- Conduct empirical research in management and marketing using modern analytic software tools
- Develop and apply new research methods
- Solve economic and managerial problems using best practices of data analysis using modern computational tools
Course Contents
- Introduction. Review of basic probability and statistics concepts.
- Introduction to the R Language
- Describing Data
- Relationships between Continuous Variables
- Comparing Groups: Tables and Visualizations
- Comparing Groups: Statistical Tests
- Identifying Drivers of Outcomes: Linear Models
- Reducing Data Complexity (Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA))
- Additional Linear Model Topics (Collinearity, Logistic, Hierarchical Linear Models (HLM))
- Confirmatory Factor Analysis and Structural Equation Modeling (SEM)
- Segmentation: Clustering and Classification
- Choice Modeling (Choice-based conjoint analysis)
- Association Rules for Market Basket Analysis
Assessment Elements
- In-class Activity
- Midterm Exam
- ExamThe exam is conducted in written form. There will be not less than 60 minutes and not more than 90 minutes. to answer the questions. The exam is conducted on the platform (insert the platform name and link). You need to connect to the exam at the time mentioned in the schedule The student's computer must meet the requirements: R and R studio installed, access to LMS lms.hse.ru A short-term communication failure during the exam is considered less than 5 minutes. In case of any failure, the student should contact the lecturer vie email eantipov@hse.ru or using MS Teams chat with a screenshot of the problem. In case of such a problem the test can be retaken only of the student has contacted the lecturer in no more than 90 minutes from the start of the exam.
- Kahoot tests
Interim Assessment
- Interim assessment (2 module)0.25 * Exam + 0.25 * In-class Activity + 0.25 * Kahoot tests + 0.25 * Midterm Exam
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
Recommended Additional Bibliography
- Bolstad, W. M. (2017). Introduction to Bayesian Statistics (Vol. Third edition). Hoboken, N.J.: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1342637