Delivered at:: International Laboratory for Applied Network Research

Course type:: Compulsory course

When:: 1 year, 1 module

Instructor

Pavlova, Irina

Full Syllabus Ask Question

Abstract

This course is a required foundational course for masters’ students in applied statistics and data analysis, designed to familiarize them with the most recent developments in interdisciplinary statistical methods. The students will get an overview of data and approaches to analyzing them (remember, “data” is always plural!), including complex models. The course will also emphasize problem formulation at the intersection of mathematics and social sciences, integrate the most important concepts from probability theory, and overall, is designed as a "gateway" to graduate work in statistics, where the mathematical concepts are bridged with applied concepts and research design, depending on the discipline.

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

Be able to apply data analysis tools to real-life problems.
Be able to build and estimate formalized mathematical models, describing real-life situations.
Be able to criticize constructively and determine existing issues with the use of statistical methods in published work .
Be able to estimate the data, find appropriate functions describing the data, visualize data.
Have a working knowledge of different ways of using software programs for data analysis.
Have a working knowledge of mathematics of data analysis.
Know contemporary software programs used to analyze data.
Know the four major areas that contemporary field of statistics is based on: data management, statistical inference, statistical prediction, and statistical reporting.
Know the most recent advances in network science and applied statistical methods, complex statistical modeling, analysis, and forecasting.

Course Contents

Section 1 Introduction and the data
Section 2 Data issues that go bump in the night
Section 3 Descriptive Analytics
Section 4 Inferential analytics
Section 5 Predictive Analytics
Section 6 Prescriptive Analytics

Assessment Elements

Graded quizzes
Students will have 1 attempt to complete every graded quiz.
Final project
Final project has to be completed individually. Students may select 1 project from a variety offered projects of different levels of difficulty and intensity.

Interim Assessment

2024/2025 1st module
0.4 * Final project + 0.6 * Graded quizzes

Bibliography

Recommended Core Bibliography

Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research, Second Edition (Vol. Second edition). New York: The Guilford Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=831411
Denis, D. J. (2016). Applied Univariate, Bivariate, and Multivariate Statistics. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1091881
Raykov, T., & Marcoulides, G. A. (2006). A First Course in Structural Equation Modeling (Vol. 2nd ed). Mahwah, NJ: Routledge. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=188193
Statistics and Causality : Methods for Applied Empirical Research, edited by Wolfgang Wiedermann, and Eye, Alexander von, John Wiley & Sons, Incorporated, 2016. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/hselibrary-ebooks/detail.action?docID=4530803.

Recommended Additional Bibliography

Khandker, S. R., Koolwal, G. B., & Samad, H. A. (2010). Handbook on Impact Evaluation : Quantitative Methods and Practices. Washington, D.C.: World Bank Publications. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=305052
Little, R. J. A., & Rubin, D. B. (2002). Statistical Analysis with Missing Data (Vol. Second edition). Hoboken: Wiley-Interscience. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=838162
Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal Inference in Statistics : A Primer. Chichester, West Sussex, UK: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1161971

Authors

Pavlova Irina Anatolevna

Master’s Programme 'Data Analytics and Social Statistics'

Contacts

Contemporary Data Analysis: Methodology and Methods of Interdisciplinary Research