Статистический анализ в социально-экономической сфере

Бакалавриат 2022/2023

Лучший по критерию «Новизна полученных знаний»

Статус: Курс обязательный (Программа двух дипломов НИУ ВШЭ и Университета Кёнхи «Экономика и политика в Азии»)

Направление: 41.03.01. Зарубежное регионоведение

Кто читает: Факультет мировой экономики и мировой политики

Где читается: Факультет мировой экономики и мировой политики

Когда читается: 2-й курс, 1, 2 модуль

Формат изучения: с онлайн-курсом

Онлайн-часы: 18

Охват аудитории: для своего кампуса

Преподаватели: Карпов Максим Евгеньевич

Язык: английский

Кредиты: 3

Контактные часы: 60

Full Syllabus

Abstract

The course provides students with a basic knowledge of statistics and data analysis techniques. The course consists of three parts. In the first part we will talk about general ideas of statistics and data analysis, mainly discussing descriptive statistics and basic data manipulations. In the second part of the course we will move towards inferential statistics and hypothesis testing. In the third part, we will apply machine learning techniques for data analysis. All the course practice will be conducted in Python. There are 3 credits for this course.

Learning Objectives

Via this course, students will acquire a solid basis in data manipulation and visualization.

Expected Learning Outcomes

After this session, students should be able to: - Apply numerical techniques for describing and summarizing data - Identify, compute, and interpret descriptive statistical summary measures - Differentiate between the measures of central tendency, dispersion, and relative standing

Course Contents

Introduction
Data Basics
Graphical Descriptive Techniques
Numerical Descriptive Techniques
Data Collection and Sampling Theory
Probability
Discrete Probability Distributions
Continuous Probability Distributions
Sampling Distributions
Estimation
Hypothesis Testing Framework
Inference for Numerical Data
Analysis of Variance
Regression Analysis
Descriptive statistics: System of variables
Descriptive statistics: Qualitative and Quantitative Data.
Measures of Central Location
Time series
Index numbers
Global Statistical System
Midterm exam
Social statistics. Demography 1.
Social statistics. Demography 2.
Social statistics. Labour force
System of national accounts: main concepts

Assessment Elements

Quizzes
Academic dishonesty and Plagiarism Be advised that plagiarism is prohibited at HSE University. If a professor or a TA encounters a case of plagiarism, cheating or academic dishonesty, the student will get a zero for a particular assignment. The further violations might be a case for disciplinary action.
Seminar Assignments
Academic dishonesty and Plagiarism Be advised that plagiarism is prohibited at HSE University. If a professor or a TA encounters a case of plagiarism, cheating or academic dishonesty, the student will get a zero for a particular assignment. The further violations might be a case for disciplinary action.
Midterm test
Academic dishonesty and Plagiarism Be advised that plagiarism is prohibited at HSE University. If a professor or a TA encounters a case of plagiarism, cheating or academic dishonesty, the student will get a zero for a particular assignment. The further violations might be a case for disciplinary action.
Final test
Academic dishonesty and Plagiarism Be advised that plagiarism is prohibited at HSE University. If a professor or a TA encounters a case of plagiarism, cheating or academic dishonesty, the student will get a zero for a particular assignment. The further violations might be a case for disciplinary action.
Final Project
Academic dishonesty and Plagiarism Be advised that plagiarism is prohibited at HSE University. If a professor or a TA encounters a case of plagiarism, cheating or academic dishonesty, the student will get a zero for a particular assignment. The further violations might be a case for disciplinary action.

Interim Assessment

2022/2023 2nd module
0.2 * Quizzes + 0.2 * Seminar Assignments + 0.2 * Final test + 0.2 * Final Project + 0.2 * Midterm test

Bibliography

Recommended Core Bibliography

Elementary statistics : a step by step approach, Bluman, A. G., 2018
Frederick J Gravetter, Larry B. Wallnau, Lori-Ann B. Forzano, & James E. Witnauer. (2020). Essentials of Statistics for the Behavioral Sciences, Edition 10. Cengage Learning.
James, G. et al. An introduction to statistical learning. – Springer, 2013. – 426 pp.

Recommended Additional Bibliography

Boris Mirkin. (2011). Core Concepts in Data Analysis: Summarization, Correlation and Visualization (Vol. 2011). Springer.
Döbler, M., & Grössmann, T. (2019). Data Visualization with Python : Create an Impact with Meaningful Data Insights Using Interactive and Engaging Visuals. Packt Publishing.
Frederick J Gravetter, Lori-Ann B. Forzano, & Tim Rakow. (2021). Research Methods For The Behavioural Sciences, Edition 1. Cengage Learning.

Course Syllabus