Магистратура
2021/2022





Анализ данных
Лучший по критерию «Полезность курса для расширения кругозора и разностороннего развития»
Статус:
Курс обязательный (Системная и программная инженерия)
Направление:
09.04.04. Программная инженерия
Кто читает:
Департамент программной инженерии
Где читается:
Факультет компьютерных наук
Когда читается:
1-й курс, 3, 4 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Преподаватели:
Меликян Алиса Валерьевна
Прогр. обучения:
Системная и программная инженерия
Язык:
английский
Кредиты:
4
Контактные часы:
56
Course 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 module0.2 * 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