Book Image

The Kaggle Book

By : Konrad Banachewicz, Luca Massaron
5 (2)
Book Image

The Kaggle Book

5 (2)
By: Konrad Banachewicz, Luca Massaron

Overview of this book

Millions of data enthusiasts from around the world compete on Kaggle, the most famous data science competition platform of them all. Participating in Kaggle competitions is a surefire way to improve your data analysis skills, network with an amazing community of data scientists, and gain valuable experience to help grow your career. The first book of its kind, The Kaggle Book assembles in one place the techniques and skills you’ll need for success in competitions, data science projects, and beyond. Two Kaggle Grandmasters walk you through modeling strategies you won’t easily find elsewhere, and the knowledge they’ve accumulated along the way. As well as Kaggle-specific tips, you’ll learn more general techniques for approaching tasks based on image, tabular, textual data, and reinforcement learning. You’ll design better validation schemes and work more comfortably with different evaluation metrics. Whether you want to climb the ranks of Kaggle, build some more data science skills, or improve the accuracy of your existing models, this book is for you. Plus, join our Discord Community to learn along with more than 1,000 members and meet like-minded people!
Table of Contents (20 chapters)
Part I: Introduction to Competitions
Part II: Sharpening Your Skills for Competitions
Part III: Leveraging Competitions for Your Career
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Summary (and some parting words)

In this chapter, we have discussed how competing on Kaggle can help improve your career prospects. We have touched on building connections, both by teaming up on competitions and participating in events related to past competitions, and also on using your Kaggle experience in order to find a new job. We have discussed how, based on our experience and the experience of other Kagglers, results on Kaggle alone cannot ensure that you get a position. However, they can help you get attention from recruiters and human resource departments and then reinforce how you present competencies in data science (if they are supported by a carefully-built portfolio, as we described in the previous chapter).

This chapter also marks the conclusion of the book. Through fourteen chapters, we have discussed Kaggle competitions, Datasets, Notebooks, and discussions. We covered technical topics in machine learning and deep learning (from evaluation metrics to simulation...