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

Approx. 54 hours to complete

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Course Summary

Learn how to analyze and manipulate data, build predictive models, and compete in global data science competitions with this course.

Key Learning Points

Gain hands-on experience in data analysis and machine learning
Learn from top data scientists and compete in real-world competitions
Build a portfolio of projects to showcase your skills to potential employers

Learning Outcomes

Develop a strong foundation in data science concepts and techniques
Gain practical experience in analyzing and manipulating data
Build a portfolio of projects to showcase your skills to potential employers

Prerequisites or good to have knowledge before taking this course

Basic programming knowledge in Python
Familiarity with data structures and algorithms

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced
Project-based

Similar Courses

Applied Data Science with Python
Machine Learning

Related Education Paths

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Description

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. Do you have technical problems? Write to us: coursera@hse.ru

Outline

Introduction & Recap
About the University
Introduction
Meet your lecturers
Course overview
Competition Mechanics
Kaggle Overview [screencast]
Real World Application vs Competitions
Recap of main ML algorithms
Software/Hardware Requirements
About University
Rules on the academic integrity in the course
Welcome!
Week 1 overview
Disclaimer
Explanation for quiz questions
Additional Materials and Links
Explanation for quiz questions
Additional Material and Links
Practice Quiz
Recap
Recap
Software/Hardware
Graded Soft/Hard Quiz

Feature Preprocessing and Generation with Respect to Models
Overview
Numeric features
Categorical and ordinal features
Datetime and coordinates
Handling missing values
Bag of words
Word2vec, CNN
Explanation for quiz questions
Additional Material and Links
Explanation for quiz questions
Additional Material and Links
Feature preprocessing and generation with respect to models
Feature preprocessing and generation with respect to models
Feature extraction from text and images
Feature extraction from text and images

Final Project Description
Final project overview
Final project
Final project advice #1

Exploratory Data Analysis
Exploratory data analysis
Building intuition about the data
Exploring anonymized data
Visualizations
Dataset cleaning and other things to check
Springleaf competition EDA I
Springleaf competition EDA II
Numerai competition EDA
Week 2 overview
Additional material and links
Exploratory data analysis

Validation
Validation and overfitting
Validation strategies
Data splitting strategies
Problems occurring during validation
Validation strategies
Comments on quiz
Additional material and links
Validation
Validation

Data Leakages
Basic data leaks
Leaderboard probing and examples of rare data leaks
Expedia challenge
Comments on quiz
Additional material and links
Final project advice #2
Data leakages

Metrics Optimization
Motivation
Regression metrics review I
Regression metrics review II
Classification metrics review
General approaches for metrics optimization
Regression metrics optimization
Classification metrics optimization I
Classification metrics optimization II
Week 3 overview
Comments on quiz
Additional material and links
Metrics
Metrics

Advanced Feature Engineering I
Concept of mean encoding
Regularization
Extensions and generalizations
Comments on quiz
Final project advice #3
Mean encodings

Hyperparameter Optimization
Hyperparameter tuning I
Hyperparameter tuning II
Hyperparameter tuning III
Practical guide
KazAnova's competition pipeline, part 1
KazAnova's competition pipeline, part 2
Week 4 overview
Comments on quiz
Additional material and links
Additional materials and links
Practice quiz
Graded quiz

Advanced feature engineering II
Statistics and distance based features
Matrix factorizations
Feature Interactions
t-SNE
Comments on quiz
Additional Materials and Links
Graded Advanced Features II Quiz

Ensembling
Introduction into ensemble methods
Bagging
Boosting
Stacking
StackNet
Ensembling Tips and Tricks
CatBoost 1
CatBoost 2
Validation schemes for 2-nd level models
Comments on quiz
Additional materials and links
Final project advice #4
Ensembling
Ensembling

Competitions go through
Crowdflower Competition
Springleaf Marketing Response
Microsoft Malware Classification Challenge
Walmart: Trip Type Classification
Acquire Valued Shoppers Challenge, part 1
Acquire Valued Shoppers Challenge, part 2
Week 5 overview
Additional material and links

Final Project

Summary of User Reviews

The Competitive Data Science course on Coursera has received great reviews from users. Many have praised the course for its comprehensive curriculum and practical approach to learning. Overall, users have found the course to be highly beneficial for their data science careers.

Key Aspect Users Liked About This Course

The course provides hands-on experience with real-world datasets.

Pros from User Reviews

Comprehensive curriculum covering a wide range of topics in data science
Practical approach to learning with real-world datasets and projects
Engaging and knowledgeable instructors who are responsive to student needs

Cons from User Reviews

Some users have found the course to be too challenging for beginners
The course can be time-consuming and require a significant time commitment
The course may require additional resources or outside help for certain topics

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