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

How to Win a Data Science Competition: Learn from Top Kagglers, National Research University Higher School of Economics

438 ratings
98 reviews

About this Course

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. When you finish this class, you will: - Understand how to solve predictive modelling competitions efficiently and learn which of the skills obtained can be applicable to real-world tasks. - Learn how to preprocess the data and generate new features from various sources such as text and images. - Be taught advanced feature engineering techniques like generating mean-encodings, using aggregated statistical measures or finding nearest neighbors as a means to improve your predictions. - Be able to form reliable cross validation methodologies that help you benchmark your solutions and avoid overfitting or underfitting when tested with unobserved (test) data. - Gain experience of analysing and interpreting the data. You will become aware of inconsistencies, high noise levels, errors and other data-related issues such as leakages and you will learn how to overcome them. - Acquire knowledge of different algorithms and learn how to efficiently tune their hyperparameters and achieve top performance. - Master the art of combining different machine learning models and learn how to ensemble. - Get exposed to past (winning) solutions and codes and learn how to read them. Disclaimer : This is not a machine learning course in the general sense. This course will teach you how to get high-rank solutions against thousands of competitors with focus on practical usage of machine learning methods rather than the theoretical underpinnings behind them. Prerequisites: - Python: work with DataFrames in pandas, plot figures in matplotlib, import and train models from scikit-learn, XGBoost, LightGBM. - Machine Learning: basic understanding of linear models, K-NN, random forest, gradient boosting and neural networks....

Top reviews


Mar 29, 2018

Top Kagglers gently introduce one to Data Science Competitions. One will have a great chance to learn various tips and tricks and apply them in practice throughout the course. Highly recommended!


Nov 10, 2017

This course is fantastic. It's chock full of practical information that is presented clearly and concisely. I would like to thank the team for sharing their knowledge so generously.

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96 Reviews

By Mike Korotkov

Jan 17, 2019

Отличный курс

By Amandeep Singh

Jan 14, 2019

Great Course

By Ivan Slobozhan

Jan 12, 2019

Great course!

By Maciej

Jan 10, 2019

Very dry presentation. Video is not a good medium for this material.

By Aman Sharma

Jan 09, 2019

Teaching style is not engaging at all. I am very confused

By Mostafa M. Mohamed

Jan 07, 2019

Really rich course with a lot of practical information, I learned a lot from it.

By robert

Jan 02, 2019

Challenging in a fun way, puts things I've learnt before in a different perspective. Overall very practical knowledge with lots of use-cases and not much theory. it's like an awesome lab in grad school.


Dec 23, 2018

This course is just what I was looking for as I am really interested in competitive Machine Learning and data science. Hopefully , I will be able to perform better in competitions from now on.

But the only down side I can think of is that the programming assignments are pretty difficult at times, but none the less it was a great experience.

By Waylon Wu

Dec 20, 2018

This course is Okay but not perfect. I learned something from this course.

By Oleg Ovcharenko

Dec 09, 2018

Very handy course, except I wasn't motivated enough to do home assignments. However, I gained a lot of new concepts