Delivered at:: School of Software Engineering

Course type:: Compulsory course

When:: 2 year, 1, 2 module

Instructor

Melikyan, Alisa

Full Syllabus

Abstract

Students will study modern methods of data analysis and will acquire practical skills in using Python programming language for data manipulation and analysis. At the end of the course students will be able to carry out preliminary preparation of data, choose an appropriate method of data analysis depending on the type of data and the research task, conduct quantitative data analysis and interpret the obtained results.

Learning Objectives

give students an introduction to the most widely used data analysis methods
explain the data analysis methods using real data and concentrating on complications that may occur during the analysis in real-life research
teach students how to organize their own research project using the knowledge obtained during the course
explain how to use data analysis tools in the most effective way to perform the research tasks

Expected Learning Outcomes

create a cluster model and describe it
create a factor model and describe it
create a regression model and describe it
formulate research hypotheses and construct models
prepare empirical data for their further analysis
select appropriate methods of data analysis depending on the research question and types of empirical data

Course Contents

Introduction to data analysis
Descriptive data analysis
Investigating relationships between variables
Regression analysis
Factor analysis
Cluster analysis
Panel data analysis
Time series analysis

Assessment Elements

Exam
Written work done on a computer.
Home Project
Practical Tasks
Control Work 1
Control Work 2

Interim Assessment

2024/2025 2nd module
0.15 * Control Work 1 + 0.15 * Control Work 2 + 0.3 * Exam + 0.2 * Home Project + 0.2 * Practical Tasks

Bibliography

Recommended Core Bibliography

Core concepts in data analysis: summarization, correlation and visualization, Mirkin, B., 2011
Introduction to econometrics, Dougherty, C., 2016
McKinney, W. (2012). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython. Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=495822

Recommended Additional Bibliography

Idris, I. (2016). Python Data Analysis Cookbook. Birmingham, UK: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1290098
Lutz, M. (2006). Programming Python (Vol. 3rd ed). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=415084
McKinney, W. (2018). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython (Vol. Second edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1605925

Authors

Butskaia Evgeniia Aleksandrovna
MELIKYAN ALISA VALEREVNA

Master’s Programme 'System and Software Engineering'

Contacts

Data Analysis