Statistics for Social Science

2021/2022

Учебный год

ENG

Обучение ведется на английском языке

4

Кредиты

Кто читает:

Факультет мировой экономики и мировой политики

Статус:

Курс обязательный

Когда читается:

2-й курс, 1, 2 модуль

Преподаватели

Рогович Татьяна Владимировна

Цветкова Алена Дмитриевна

Course Syllabus

Abstract

This is a required course for students of all undergraduate programs at HSE University. The course provides students with a basic knowledge of statistics and data analysis techniques. Via this course, students will acquire a solid basis in data manipulation and visualization. The course consists of two parts. In the first part, we will talk about general ideas of statistics and data analysis. We will mainly discuss descriptive statistics and basic data manipulations. In the second part of the course, we will move towards inferential statistics and hypothesis testing. Students can achieve excellent results by doing a considerable amount of practical exercises both in class and at home, and taking part in group projects. The course consists of lectures and seminars. All the course practice will be conducted in Python.

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
• ● To be able to choose and implement the right strategy to deal with missing data and outliers
• ● To be able to conduct a simple research project involving data manipulation and statistical tests
• ● To be able to interpret visualized data
• ● To be able to state a hypothesis and choose correct methods for testing
• ● To choose correct ways and tools for data visualization
• ● To know how to compute and interpret the basic descriptive statistics (measures of central tendency, variance, correlation coefficients, etc.)
• ● To know how to compute and interpret the results of the basic statistical tests and models (Student’s T-test, Chi-square, linear regression model, k-nearest Neighbors model)
• ● To know how to load, clean, and do basic data manipulations via Python and its libraries

Course Contents

• Week 1. Lecture Theme: Course Logistics. Intro in Statistics. Seminar Theme: NA
• Week 2. Lecture Theme: Data collection. Populations and samples. Frequency Tables and distribution. Types of variables. Seminar Theme: Intro to working with data in Python (Pandas library)
• Week 3. Lecture Theme: Descriptive statistics: central tendency, variability. Seminar Theme: Loading datasets in Pandas. Filtering, sorting, grouping.
• Week 4. Lecture Theme: Z-Score and standardization. Outliers. How to deal with missing data. Seminar Theme: Central tendency and variability. On paper and in Python.
• Week 5. Lecture Theme: Intro to Data visualization. Basic graph types. Seminar Theme:Calculating Z-Score. Standardizing variables. (Paper + Python). Strategies of dealing with missing data in Pandas
• Week 6. Lecture Theme: Good and bad visualizations. What to do and what to avoid. Seminar Theme: Introduction to the visualization libraries in Python. Basic graph types.
• Week 7. Lecture Theme: MIDTERM TEST Seminar Theme: Advanced visualization techniques in Python.
• Week 8. Lecture Theme: Hypothesis testing. Error types Seminar Theme: Interactive visualizations with Plotly.
• Week 9. Lecture Theme: Non-parametric tests. Chi-square test Seminar Theme: Automatic reports generation in Python.
• Week 10. Lecture Theme: Parametric tests. Student’s test Seminar Theme: Chi-Square test in Python and on paper.
• Week 11. Lecture Theme: Correlation Seminar Theme: T-Test in Python and on paper.
• Week 12. Lecture Theme: Linear Regression Seminar Theme: Correlation in Python and on paper.
• Week 13. Lecture Theme: K Nearest Neighbors Seminar Theme: Linear Regression in Python.
• Week 14. Lecture Theme: Introduction to Machine Learning Seminar Theme: K Nearest Neighbors in Python.
• FINAL TEST. Seminar Theme: 2 seminars 1. Simple Machine Learning in Python 2. Final Project consultation

Assessment Elements

• Homework 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.
• 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

• 2021/2022 2nd module
  0.25 * Homework assignments + 0.15 * Final test + 0.2 * Final Project + 0.15 * Midterm test + 0.1 * Quizzes + 0.15 * Seminar Assignments

Bibliography

Recommended Core Bibliography

• 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.

Recommended Additional Bibliography

• Frederick J Gravetter, Lori-Ann B. Forzano, & Tim Rakow. (2021). Research Methods For The Behavioural Sciences, Edition 1. Cengage Learning.