About the Technology

In real-world practice, data scientists create innovative solutions to novel open ended problems. Easy to learn and use, the Python language has become the de facto language for data science amongst researchers, developers, and business users. But knowing a few basic algorithms is not enough to tackle a vague and thorny problem. It takes relentless practice at cracking difficult data tasks to achieve mastery in the field. That’s just what this book delivers.

About the book

Data Science Bookcamp is a comprehensive set of challenging projects carefully designed to grow your data science skills from novice to master. Veteran data scientist Leonard Apeltsin sets five increasingly difficult exercises that test your abilities against the kind of problems you’d encounter in the real world. As you solve each challenge, you’ll acquire and expand the data science and Python skills you’ll use as a professional data scientist. Ranging from text processing to machine learning, each project comes complete with a unique downloadable data set and a fully-explained step-by-step solution. Because these projects come from Dr. Apelstin’s vast experience, each solution highlights the most likely failure points along with practical advice for getting past unexpected pitfalls. When you wrap up these five awesome exercises, you’ll have a diverse relevant skill set that’s transferable to working in industry.

What's inside

• Five in-depth Python exercises with full downloadable data sets
• Web scraping for text and images
• Organise datasets with clustering algorithms
• Visualize complex multi-variable datasets
• Train a decision tree machine learning algorithm

About the reader

For readers who know the basics of Python. No prior data science or machine learning skills required.

About the author

Leonard Apeltsin is a senior data scientist and engineering lead at Primer AI, a startup that specializes in using advanced Natural Language Processing techniques to extract insight from terabytes of unstructured text data. His PhD research focused on bioinformatics that required analyzing millions of sequenced DNA patterns to uncover genetic links in deadly diseases.