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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 

STATISTICAL DATA ANALYSIS

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
RESERVE MY SPOT

We offer innovative university degrees taught in English by industry leaders from around the world, aimed at giving our students meaningful and creatively satisfying top-level professional futures. We think the future is bright if you make it so.

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

STATISTICAL DATA ANALYSIS

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

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Session 1

Introduction

Foundations of statistics: estimation and hypothesis testing

All rights reserved. 2018

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