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How to Win a Data Science Competition

Learn from Top Kagglers

Dmitry Altukhov, Dmitry Ulyanov, Marios Michailidis, Alexander Guschin, and Mikhail Trofimov
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,...
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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 online 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: [email protected]
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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Offers job-ready skills in competitive data competition participation
Provides hands-on experience in real-world tasks through predictive modeling competitions
Emphasizes practical usage of machine learning methods, focusing on high-rank solutions in competitions
Develops advanced feature engineering techniques, such as mean-encodings and nearest neighbors
Teaches how to combine different machine learning models through ensemble techniques
Provides opportunities to analyze and interpret data effectively

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Reviews summary

Kaggle masters

This course, with a focus on practical usage rather than theory, teaches how to solve competitive data science tasks and achieve top ranks in Kaggle competitions. Students will learn how to preprocess data, generate features, form cross validation methodologies, analyze and interpret data, gain knowledge of different algorithms, combine models, and read past winning solutions.
While the focus is on competition performance, many of the skills learned can be applied to real-world tasks.
"Understand how to solve predictive modeling competitions efficiently and learn which of the skills obtained can be applicable to real-world tasks."
Students will master the art of combining different machine learning models through ensembling techniques.
"Master the art of combining different machine learning models and learn how to ensemble."
Students will gain knowledge of different algorithms and learn how to efficiently tune their hyperparameters for top performance.
"Acquire knowledge of different algorithms and learn how to efficiently tune their hyperparameters and achieve top performance."
Students will learn how to preprocess data and generate new features from various sources.
"You will learn how to preprocess the data and generate new features from various sources such as text and images."
This course is designed for those who want to break into competitive data science.
"If you want to break into competitive data science, then this course is for you!"
This is a demanding course that requires prior knowledge and a strong grasp of programming concepts.
"The course is really excellent way of teaching the exploratory data analysis and ensembling concepts, i hope i will start a new life with the knowledge gained by this course"
"It is really a very useful course where we get to learn from experienced people who achieved top ranks on Kaggle."
"It was really hard and comprehensive course but I completed it."
"It's a very challenging course."
"It's a very challenging course. You need strong basics of Machine Learning and programming in Python."
"This course was really challenging."

Career center

Learners who complete How to Win a Data Science Competition: Learn from Top Kagglers will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists use their mastery of organizing, collecting, and analyzing data to form models. These models make new discoveries to help a business grow and make informed decisions. Success in this field can be greatly influenced by enrolling in this course, which covers various methods of generating and organizing models that fit the needs of the business. A background in competitive data science through this course's training on analyzing and interpreting data will certainly give you an edge as an applicant looking to become a Data Scientist.
Machine Learning Engineer
Machine Learning Engineers construct and develop applications using machine learning algorithms. Their goal is to create solutions to real-world problems using advanced techniques. This course's focus on predictive modeling competitions will give you the experience necessary to become a successful Machine Learning Engineer. You'll gain practical knowledge of different algorithms and learn how to efficiently tune their hyperparameters. This, along with knowledge of advanced feature engineering and cross validation methodologies, will prepare you to achieve top performance in this field.
Data Analyst
Data Analysts gather, clean, and analyze data. Using the results, they communicate data-driven insights to make informed business decisions. This course's emphasis on data analysis and interpretation can help you become a highly effective Data Analyst.
Business Intelligence Analyst
Business Intelligence Analysts help businesses make better decisions by providing insights derived from data analysis. This course may be useful for those who wish to become Business Intelligence Analysts because it will teach you how to analyze and solve predictive modeling tasks efficiently, which can be applied to real-world business scenarios.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data and make predictions. They often work in the financial industry, using their skills to make investment decisions. This course may be useful for those who want to become Quantitative Analysts because it covers advanced feature engineering techniques and cross validation methodologies that can be applied to financial data.
Software Engineer
Software Engineers design, develop, test, and maintain software systems. Some of the work of a Software Engineer overlaps with a Data Scientist or Machine Learning Engineer. This course will help you build a foundation for a career in software engineering, with its focus on using Python to work with DataFrames in pandas, plot figures in matplotlib, and import and train models from scikit-learn, XGBoost, and LightGBM.
Statistician
Statisticians collect, analyze, interpret, and present data. Their work is used to make informed decisions in a variety of fields. This course will help you develop the skills you need to be a successful Statistician by teaching you how to analyze and solve predictive modeling tasks efficiently. You will also learn advanced feature engineering techniques and cross validation methodologies that can be applied to a variety of datasets.
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex problems in a variety of industries. This course may be useful for those who want to become Operations Research Analysts because it covers advanced feature engineering techniques and cross validation methodologies that can be applied to real-world problems.
Data Journalist
Data Journalists use data to tell stories and explain complex issues. This information is used to inform the public and hold powerful people accountable. This course may be useful for those who want to become Data Journalists because it covers how to analyze and interpret data, as well as advanced feature engineering techniques and cross validation methodologies.
Financial Analyst
Financial Analysts use financial data to make investment recommendations. This information is used by individuals and institutions to make informed investment decisions. This course may be useful for those who want to become Financial Analysts because it covers how to analyze and interpret data, as well as advanced feature engineering techniques and cross validation methodologies.
Risk Analyst
Risk Analysts use data to identify and assess risks. This information is used to develop strategies to mitigate risks and protect businesses. This course may be useful for those who want to become Risk Analysts because it covers how to analyze and interpret data, as well as advanced feature engineering techniques and cross validation methodologies.
Data Engineer
Data Engineers design, build, and maintain data pipelines that collect, store, and process data. This course may be useful for those who want to become Data Engineers because it covers how to preprocess the data and generate new features from various sources such as text and images.
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. This information is used to develop insurance policies and other financial products. This course may be useful for those who want to become Actuaries because it covers advanced feature engineering techniques and cross validation methodologies that can be applied to financial data.
Market Research Analyst
Market Research Analysts conduct research to understand consumer behavior and market trends. This information is used to develop marketing strategies and make informed business decisions. This course may be useful for those who want to become Market Research Analysts because it covers how to analyze and interpret data, as well as advanced feature engineering techniques and cross validation methodologies.
UX Researcher
UX Researchers use data to understand user needs and improve the user experience. This information is used to design and develop products and services that are easy to use and enjoyable. This course may be useful for those who want to become UX Researchers because it covers how to analyze and interpret data, as well as advanced feature engineering techniques and cross validation methodologies.

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