Bachelor
2021/2022
Introduction to Data Analysis
Type:
Compulsory course
Area of studies:
Public Policy and Social Sciences
Delivered by:
Faculty of World Economy and International Affairs
When:
1 year, 2 module
Mode of studies:
distance learning
Open to:
students of one campus
Language:
English
ECTS credits:
3
Contact hours:
28
Course Syllabus
Abstract
This course offers an introduction to the modern data science methods that are useful for both research and industrial careers. The main focus of the course is to teach students to find data on the Internet, to process it and to perform a simple data analysis. Students are trained to develop critical thinking and to apply the scientific approach to problem solving. The course starts from the basics of working with data. Students will be taught to perform a basic data analysis in Google Sheets. Students will learn how to sort and filter data, to calculate various distribution characteristics and to create graphs and charts in accordance with the standards of their design. A part of the course also concerns the main methods of data storage and its usage. Students will study the main methods that lead to scientific results of the analyses in humanities, including time series and linear regression analyses. Students will learn to apply all these techniques in Google Sheets.
Learning Objectives
- To provide an introduction to modern data science techniques
- To introduce the main concepts of scientific data analysis
- To show the best practices of working with data
- To train basic skills in Google Sheets
Expected Learning Outcomes
- Demonstrate knowledge of basic concepts of data science
- Formulate and solve simple scientific problems
- Perform exploratory data analysis in Google Sheets
- To understand the notions of continuous random variable and of probability distribution. Know how to apply the central limit theorem
Course Contents
- Introduction to Data Analysis
- Normal distribution
- The simpliest text analysis
- Sampling and confidence intervals
- Simple linear regression
- Confidence intervals
- Linear regression analysis
- Preparation for the exam
Assessment Elements
- Homework 1 (Data Culture)
- Homework 2 (Data Culture)
- Exam (Data analysis and Data Culture)
- Homework 3 (Data Culture)
- Labwork
Interim Assessment
- 2021/2022 2nd module0.2 * Homework 1 (Data Culture) + 0.2 * Labwork + 0.2 * Exam (Data analysis and Data Culture) + 0.2 * Homework 2 (Data Culture) + 0.2 * Homework 3 (Data Culture)
Bibliography
Recommended Core Bibliography
- Introduction to mathematical statistics, Hogg, R. V., 2005
Recommended Additional Bibliography
- Bąska, M., Pondel, M., & Dudycz, H. (2019). Identification of advanced data analysis in marketing: A systematic literature review. Journal of Economics & Management, 35(1), 18–39. https://doi.org/10.22367/jem.2019.35.02
- Springston, M., Ernst, J. V., Clark, A. C., Kelly, D. P., & DeLuca, V. W. (2019). data analysis. Technology & Engineering Teacher, 79(4), 26–29. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=139712968