Evgeniy Riabenko is a data scientist with more than 10 years of experience in both industry and academia. He got his PhD in mathematical modelling from Moscow State University, and has expertise in statistics, machine learning, optimisation, time series analysis, topic modelling, and bioinformatics. For 7 years he taught statistics for data analysis courses at Moscow State University, Moscow Institute of Physics and Technology and Higher School of Economics; an adapted version of the course is available on Coursera.
After completing this course, a student will be able to:
• Identify cases where statistical analysis should be applied
• Select the most optimal statistical method for the analysis
• Check if the data in hand satisfies the underlying assumptions of the method
• Run the analysis using R
SKILLS:
- Statistics
- Data Analysis
- Machine Learning
- Bioinformatics
- R
- Matlab
- Python
DATE: 30 Apr - 18 May, 2018
DURATION: 3 Weeks
LECTURES: 3 Hours per day
LANGUAGE: English
LOCATION: Barcelona, Harbour.Space Campus
COURSE TYPE: Offline
WHAT YOU WILL LEARN
COURSE OUTLINE
ABOUT EVGENIY
BIBLIOGRAPHY
HARBOUR.SPACE
This advanced course is devoted to the vast array of statistical analysis methods with focus on the applications. Instead of proving theorems or calculating Lebesgue integrals, we would consider various standard data analysis tasks that require statistics, study the taxonomy of statistical methods, learn their limits and assumptions, and, of course, apply them to different real-world datasets and problems using R.
EVGENIY RIABENKO
HARBOUR.SPACE UNIVERSITY
DATE: 30 Apr – 18 May, 2018
DURATION: 3 Weeks
LECTURES: 3 Hours per day
LANGUAGE: English
LOCATION: Barcelona, Harbour.Space Campus
COURSE TYPE: Offline
Session 2
Testing parametric hypotheses
Hypotheses about proportions, means, and variances
Session 3
Testing parametric hypotheses
Sign, rank, permutation and bootstrap tests
Session 4
Multiple hypothesis testing
Familywise error rate, false discovery rate, and methods to control them
Session 1
Introduction
Foundations of statistics: estimation and hypothesis testing
All rights reserved. 2018