Math404BKK
Applied Probability and Statistics
Faculty
Ivan Gonchar
Senior Data Scientist in ICE Ltd
Course length
Duration
Total hours
Credits
Language
Course type
Fee for single course
Fee for degree students
Skills you’ll learn
Overview
This course focuses on statistical methods used across various fields. It starts with methods to develop statistical models and estimates of their parameters, followed by a hypothesis-testing framework. The course is centred around the practical application of various statistical tests dedicated to the analysis of numeric, categorical, and grouped data, including tests for independence and homogeneity. Additionally, the course briefly covers other important topics in statistics including experimental design, causal inference, A/B tests and regression analysis. At the end of the course, Bayesian statistics and their differences from frequentist statistics are discussed. While providing the theoretical foundation behind the methods studied, this course focuses on solving real-world problems using Python data analysis tools and allows students to master the foundational concepts and techniques of statistics.
Learning highlights
- Learn the basic principles of statistical inference, experimental design, data visualisation, and hypothesis testing.
- Learn to estimate parameters and their uncertainties within a statistical model.
- Practise the application of commonly used statistical tests and understand their limitations.
- Learn how to summarise and analyse numeric and categorical data.
- Understand A/B testing and causal analysis frameworks.
- Understand the difference between frequentist and Bayesian statistics.
Course outline
15 classes
Introduction
Statistical data analysis and probabilistic modelling. Statistical studies and experimental design. Descriptive statistics and EDA.
Estimators and their properties
Point and interval estimates, confidence intervals. Bias and variance of an estimator.
Maximum Likelihood Estimators
Efficiency and Cramer-Rao bound. Delta method.
Empirical distribution and sampling
Sampling strategies and selection bias. Empirical distribution (ECDF). Bootstrap and stochastic simulations.
Hypothesis testing I
General testing framework, test size and P-value. Z-test and t-test.
Hypothesis testing II
Power of a test. UMPTs, Neyman-Pearson lemma, likelihood ratio test.
Categorical data analysis
Multinomial distribution, Pearson chi-squared test. Goodness-of-fit tests for grouped (discrete) data.
Intermediate Quiz. Independence tests
Independence of two discrete and two continuous variables. Kolmogorov-Smirnov test.
Homogeneity tests
Homogeneity hypotheses. Tests for independent and paired (dependent) samples. Single- and two-factor models.
Causal inference
Causation and association. Counterfactual model. Observational and controlled studies.
Problem Solving
Solve composite statistical problems.
Regression analysis
Ordinary least squares and linear regression.
A/B testing
Design of an A/B test. Randomization and bucketing. Tests for proportions.
Bayesian Statistics
Bayes' theorem, priors and posteriors. Relation to frequentist statistics. Bayesian decision theory.
Final Exam
Final Exam
Prerequisites
Univariate and Multivariate Calculus
Probability Theory
Basic Python skills and experience with NumPy and Pandas packages are appreciated.
Methodology
Lectures, seminars, homework assignments, and individual work.
After and before each class, students are expected to read materials on theory to deeply understand the concepts of statistics and methods covered in class.
Students are given homework assignments after each class to practise problem-solving skills.
Grading
Awards
- Enhanceable state academic scholarship, Russia.
2014 – 2016
- Winner of the Contest for young researcher’s scientific projects
2015 - 58th MIPT Scientific Conference with international participation, section of biophysics and biotechnology, Dolgoprudny, Russia
- Nominal scholar of the Government of Moscow, Russia
2013 – 2015
- Scholarship from the Foundation for the Development of Innovation Education, Russia.
2012 – 2014
Ivan received both a BSc and a MSc in Applied Physics and Math from Moscow Institute of Physics and Technology (MIPT) and completed a graduate program in Biophysics at Center for Theoretical Problems of Physico-Chemical Pharmacology (CTP PCP).
He has a strong academic background with experience in cardiovascular physiology, single-molecule biophysics, and stochastic modelling. While pursuing his scientific career as a Researcher at CTP PCP, Ivan became more interested in the foundation of all modern research – proper data analysis – and has shifted his focus to industrial data science and machine learning. Currently, Ivan is a Senior Data Scientist at ICE Ltd developing analytical and forecasting solutions for the airline industry.
See full profileApply for this course
Applied Probability and Statistics
by Ivan Gonchar
Total hours
45 Hours
Dates
Oct 21 - Nov 08, 2024
Fee for single course
€1500
Fee for degree students
€750
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FAQ
Will I receive a certificate after completion?
Yes. Upon completion of the course, you will receive a certificate signed by the director of the program your course belonged to.
Do I need a visa?
This depends on your case. Please check with the Spanish or Thai consulate in your country of residence about visa requirements. We will do our part to provide you with the necessary documents, such as the Certificate of Enrollment.
Can I get a discount?
Yes. The easiest way to enroll in a course at a discounted price is to register for multiple courses. Registering for multiple courses will reduce the cost per individual course. Please ask the Admissions Office for more information about the other kinds of discounts we offer and what you can do to receive one.