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

In this chapter, you learned how important it is to prepare any given dataset and fix the main quality issues it has. This is critical because the cleaner a dataset is, the easier it will be for any machine learning model to easily learn about the relevant patterns. On top of this, most algorithms can't handle issues such as missing values, so they must be handled prior to the modeling phase. In this chapter, you covered the most frequent issues that are faced in data science projects: duplicate rows, incorrect data types, unexpected values, and missing values.

The goal of this chapter was to introduce you to the concepts that will help you to spot some of these issues and easily fix them so that you have the basic toolkit to be able to handle other cases. As a final note, throughout this chapter, we emphasized how important it is to discuss the issues we find with the business or the data engineering team we are working with. For instance, if you've detected...