Бакалавриат
2024/2025
Статистический анализ в социально-экономической сфере
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:
20
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
3
Course 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
- Global Statistical System
Assessment Elements
- Group ProjectThe final grade for the project is 0.5 * code with markdown comments , conclusions, graphs + 0.5 * project defense (oral) during the last seminar.
- ExamThe test will be conducted at the Smart LMS course page. The mock test will be provided a few weeks in advance. The test will consist of a quiz and problems. In the problems section the student should calculate statistics and fill in a short answer, plot the graph and find the most appropriate interpretation in an answers list, and do other calculations analyzing a dataset.
- Seminar ParticipationIn order to get full marks for the participation, students need to actively participate in the class discussions, to demonstrate familiarity with assigned readings and lecture material, including being prepared to answer the questions that the class teacher may pose dedicated to the home assignment. Before the class in advance students will get recommendations for better preparation for in-class activities, discussing theoretical moments from the lectures, practicing in Python frameworks for data analysis.
- AttendanceAttendance is not graded. However, uncertified absence can lead to deduction of the grade or even disqualification. Two absences of lectures or seminars separately are excused per semester. In case of the student’s absence for a valid reason, the student must provide a valid Certificate of Illness/Medical Note to the Students’ Office in the span of 1 (one) working day since the end of their sick leave, else their absence will be graded as 0 (zero). Each additional absence beyond the allowed number will lower the final grade for the course by 0.3 points for each absence without compromise (e.g. by the end of the course student collected 7.5 points but missed three lectures and two seminars without a valid reason, then 0.3 points are deducted from the final grade: 7.5 – 0.3 = 7.2).
- Quizzes
- Home AssignmentsIn order to get full marks for the home assignments students need to solve practical tasks in Smart LMS with auto-checking. Home Assignments will be distributed each 1-2 weeks with the deadline before the next seminar.
Interim Assessment
- 2024/2025 2nd modulemin(0.15 * Home Assignments + 0.1 * Quizzes + 0.15 * Seminar Participation + 0.3 * Group Project + 0.3 * Exam + 0 * Attendance, 8) In accordance with paragraph 83.4 of the Regulations for Interim and Ongoing Assessments of Students at National Research University Higher School of Economics, grades awarded based on interim assessment outcomes of the discipline-prerequisites for the independent exam on digital competency may not exceed 8 points.
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.