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Regular version of the site
Master 2024/2025

Contemporary Data Analysis: Methodology and Methods of Interdisciplinary Research

Type: Compulsory course (Data Analytics and Social Statistics)
Area of studies: Applied Mathematics and Informatics
When: 1 year, 1 module
Mode of studies: distance learning
Online hours: 40
Open to: students of one campus
Instructors: Irina Pavlova
Master’s programme: Аналитика данных и прикладная статистика
Language: English
ECTS credits: 3

Course Syllabus

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

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

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

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

Assessment Elements

  • non-blocking Graded quizzes
    Students will have 1 attempt to complete every graded quiz.
  • non-blocking 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

Interim Assessment

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

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