Магистратура
2021/2022
Исследовательский анализ данных
Статус:
Курс по выбору (Прикладная статистика с методами сетевого анализа)
Направление:
01.04.02. Прикладная математика и информатика
Где читается:
Международная лаборатория прикладного сетевого анализа
Когда читается:
1-й курс, 1, 2 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для всех кампусов НИУ ВШЭ
Преподаватели:
Батагель Владимир
Прогр. обучения:
Прикладная статистика с методами сетевого анализа
Язык:
английский
Кредиты:
4
Контактные часы:
40
Course Syllabus
Abstract
This course is dedicated to numerical and graphical techniques for summarizing and displaying data. Special attention is paid to exploration versus confirmation. Connections with conventional statistical analysis and data mining are explored with implications for social sciences. Special attention is paid to applications to large data sets.
Learning Objectives
- The course gives students an important foundation to develop and conduct their own research as well as to evaluate research of others.
Expected Learning Outcomes
- Have the skill to work with statistical software, required to analyze the data.
- Be able to develop and/or foster critical reviewing skills of published empirical research using applied statistical methods.
- Be able to criticize constructively and determine existing issues with applied linear models in published work
- Be able to explore the advantages and disadvantages of various approaches to exploratory analysis, and demonstrate how they relate to other methods of analysis.
- Be able to work with major data analysis programs, especially R, so that they can use them and interpret their output.
- Have an understanding of the basic principles of exploratory analysis and lay the foundation for future learning in the area.
- Have the skill to meaningfully develop an appropriate model for the research question.
- Know modern extensions to data exploration, including working with “problem data”.
- Know the basic principles behind working with all types of data for building all types of models
- Know the theoretical foundation of working with data.
Assessment Elements
- Project Appropriate clean-up of the data
- Project Basic analysis
- Project Basic inferences about the data
Interim Assessment
- 2021/2022 2nd module0.2 * Project Basic inferences about the data + 0.5 * Project Basic analysis + 0.3 * Project Appropriate clean-up of the data
Bibliography
Recommended Core Bibliography
- Fox, J., Jr, & Weisberg, H. S. (2010). An R Companion to Applied Regression. Thousand Oaks: SAGE Publications, Inc. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1236075
- Montgomery, D. C., Vining, G. G., & Peck, E. A. (2012). Introduction to Linear Regression Analysis (Vol. 5th ed). Hoboken, NJ: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1021709
- Yan, X., Su, X., & World Scientific (Firm). (2009). Linear Regression Analysis: Theory And Computing. Singapore: World Scientific. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=305216
Recommended Additional Bibliography
- Elliott, A. C., & Woodward, W. A. (2016). SAS Essentials : Mastering SAS for Data Analytics (Vol. Second edition). Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1051725
- Hocking, R. R. (2013). Methods and Applications of Linear Models : Regression and the Analysis of Variance (Vol. Third edition). Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=603362