Book Image

The Data Science Workshop

By : Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare
Book Image

The Data Science Workshop

By: Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare

Overview of this book

You already know you want to learn data science, and a smarter way to learn data science is to learn by doing. The Data Science Workshop focuses on building up your practical skills so that you can understand how to develop simple machine learning models in Python or even build an advanced model for detecting potential bank frauds with effective modern data science. You'll learn from real examples that lead to real results. Throughout The Data Science Workshop, you'll take an engaging step-by-step approach to understanding data science. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend training a model using sci-kit learn. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding. Every physical print copy of The Data Science Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your data science book. Fast-paced and direct, The Data Science Workshop is the ideal companion for data science beginners. You'll learn about machine learning algorithms like a data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead.
Table of Contents (18 chapters)

Summary

We first learned how to analyze a dataset and get a very good understanding of its data using data summarization and data visualization. This is very useful for finding out what the limitations of a dataset are and identifying data quality issues. We saw how to handle and fix some of the most frequent issues (duplicate rows, type conversion, value replacement, and missing values) using pandas' APIs.

Finally, in this chapter, we went through several feature engineering techniques. It was not possible to cover all the existing techniques for creating features. The objective of this chapter was to introduce you to critical steps that can significantly improve the quality of your analysis and the performance of your model. But remember to regularly get in touch with either the business or the data engineering team to get confirmation before transforming data too drastically. Preparing a dataset does not always mean having the cleanest dataset possible but rather getting...