Магистратура
2024/2025
Прикладная статистика
Статус:
Курс обязательный
Направление:
01.04.02. Прикладная математика и информатика
Где читается:
Факультет компьютерных наук
Когда читается:
2-й курс, 1 модуль
Формат изучения:
с онлайн-курсом
Онлайн-часы:
60
Охват аудитории:
для своего кампуса
Прогр. обучения:
Магистр по наукам о данных (о)
Язык:
английский
Кредиты:
3
Контактные часы:
12
Course Syllabus
Abstract
The main objective of statistics is quantification of uncertainty in estimation and inference. Since most, if not all, real-world datasets are noisy or sampled, data analyses need statistics. In this 6 week course you’ll learn about common and useful statistical tools and their implementations in Python.
Learning Objectives
- Formulate data analysis question as a statistical hypothesis, and select the most appropriate tool to test it.
- Quantify the uncertainty in estimates of parameters of interest with confidence intervals.
- Estimate causal effects from observational data with the help of causal graphs.
Expected Learning Outcomes
- Remember important statistics.
- Remember important distributions.
- Understand maximum likelihood estimation principle.
- Know properties of maximum likelihood estimates.
- Know how to build asymptotic confidence interval for a mean.
- Know how to build bootstrap confidence intervals.
- Remember how to test hypotheses.
- Know how to test 3 types of hypotheses about proportions.
- Understand 3 ways to test hypotheses using likelihood.
- Know how to test 3 types of hypotheses about normal means.
- Know how to test if the sample comes from a specified distribution.
- Understand and know how to use sign tests.
- Understand and know how to use rank tests.
- Understand and know how to use permutation tests.
- Understand bootstrap tests.
- Know how to test independence of categorical variables.
- Understand how to select the most appropriate test for the problem.
Course Contents
- 1. Estimation techniques
- 2. Parametric hypothesis testing
- 3. Nonparametric hypothesis testing
- 4. Testing hypothesis with many variables
- 5 . Observational causal inference: graphs
- 6. Observational causal inference: adjustments
Interim Assessment
- 2024/2025 1st module0.1 * Quizzes1 + 0.55 * Quizzes5 + 0.35 * SGA Treatment for Malocclusion
Bibliography
Recommended Core Bibliography
- Computer age statistical inference : algorithms, evidence, and data science, Efron, B., 2017
- Introductory econometrics : a modern approach, Wooldridge J.M., 2006
- Multiple Comparisons Using R, 187 p., Bretz, F., Hothorn, T., Westfall, P., 2011
- Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal Inference in Statistics : A Primer. Chichester, West Sussex, UK: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1161971
Recommended Additional Bibliography
- Regression and other stories, Gelman, A., 2021