• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site
Master 2024/2025

Applied Statistics

Type: Compulsory course (Master of Data Science)
Area of studies: Applied Mathematics and Informatics
When: 2 year, 1 module
Mode of studies: distance learning
Online hours: 60
Open to: students of one campus
Master’s programme: Master of Data Science
Language: English
ECTS credits: 3
Contact hours: 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

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

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

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
Assessment Elements

Assessment Elements

  • non-blocking SGA Treatment for Malocclusion
  • non-blocking Quizzes5
    Weekly quizzes.
  • non-blocking Quizzes1
    Weekly quizzes.
Interim Assessment

Interim Assessment

  • 2024/2025 1st module
    0.1 * Quizzes1 + 0.55 * Quizzes5 + 0.35 * SGA Treatment for Malocclusion
Bibliography

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

Authors

  • Боднарук Иван Иванович