How to Win a Data Science Competition: Learn from Top Kagglers

2020/2021

Academic Year

ENG

Instruction in English

5

ECTS credits

Delivered at:

Big Data and Information Retrieval School

Course type:

Compulsory course

When:

4 year, 3 module

Instructor

Остяков Павел Александрович

Course Syllabus

Abstract

If you want to break into competitive data science, then this course is for you! Participating in predictive modelling competitions can help you gain practical experience, improve and harness your data modelling skills in various domains such as credit, insurance, marketing, natural language processing, sales’ forecasting and computer vision to name a few. At the same time you get to do it in a competitive context against thousands of participants where each one tries to build the most predictive algorithm. Pushing each other to the limit can result in better performance and smaller prediction errors. Being able to achieve high ranks consistently can help you accelerate your career in data science. In this course, you will learn to analyse and solve competitively such predictive modelling tasks.

Learning Objectives

• To study the modern approaches to fitting high-performance models for real-world data analysis problems
• To master modern tools for building machine learning models

Course Contents

• Strategies for participation in competitions
  Introduction to Kaggle competitions, main strategies for participating in them.
• Tricks of the deep learning
  Revealing myths about DL competitions, speeding up convergence, gpu-based speeding up, overfitting and validation.
• Leaks in the data and how to use them
  Discussing how to use data leaks in the competition to improve the score on the private leaderboard.

Assessment Elements

• Online course
• Competition

Interim Assessment

• Interim assessment (3 module)
  0.7 * Competition + 0.3 * Online course

Bibliography

Recommended Core Bibliography

• Mehryar Mohri, Afshin Rostamizadeh, & Ameet Talwalkar. (2018). Foundations of Machine Learning, Second Edition. The MIT Press.

Recommended Additional Bibliography

• Christopher M. Bishop. (n.d.). Australian National University Pattern Recognition and Machine Learning. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.EBA0C705