Кто читает:: Международная лаборатория прикладного сетевого анализа

Статус:: Курс по выбору

Когда читается:: 2-й курс, 1, 2 модуль

Преподаватель

Батагель Владимир

Full 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.

Course Contents

Introduction to EDA
Data on files
Visualization
Cleaning the data
Symbolic data analysis

Assessment Elements

Project Appropriate clean-up of the data
Project Basic analysis
Project Basic inferences about the data

Interim Assessment

2021/2022 2nd module
0.2 * 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

Магистерская программа «Аналитика данных и прикладная статистика / Data Analytics and Social Statistics»

Exploratory Data Analysis