Data Analysis

Master 2021/2022

Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'

Type: Compulsory course (System and Software Engineering)

Area of studies: Software Engineering

Delivered by: School of Software Engineering

Where: Faculty of Computer Science

When: 1 year, 3, 4 module

Mode of studies: offline

Open to: students of one campus

Instructors: Alisa Melikyan

Master’s programme: Software and Systems Engineering

Language: English

ECTS credits: 4

Contact hours: 56

Full Syllabus

Abstract

The course is taught to students of a master degree of Computer science faculty in NRU HSE in the third and fourth modules of the first year of training. The number of credits is 4. Training in an audience takes 64 hours, including 24 hours of lectures and 40 hours of seminars. The control includes in-class tasks, a homework, a control work, and an examination work.The main purpose of the course is to teach students how to use different data analysis methods to analyze real data.

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

Regular Tasks (RT)
Tasks are aimed at developing students’ skills in data analysis.
Research Project (RP)
Control Work 1 (CW1)
A written work which is performed in class.
Examination Work (EW)
Control Work 2 (CW2)
A written work which is performed in class.

Interim Assessment

2021/2022 4th module
0.2 * Research Project (RP) + 0.15 * Control Work 1 (CW1) + 0.3 * Examination Work (EW) + 0.2 * Regular Tasks (RT) + 0.15 * Control Work 2 (CW2)

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

Course Syllabus