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)

Introduction

In the previous chapter, you saw how critical it was to get a very good understanding of your data and learned about different techniques and tools to achieve this goal. While performing Exploratory Data Analysis (EDA) on a given dataset, you may find some potential issues that need to be addressed before the modeling stage. This is exactly the topic that will be covered in this chapter. You will learn how you can handle some of the most frequent data quality issues and prepare the dataset properly.

This chapter will introduce you to the issues that you will encounter frequently during your data scientist career (such as duplicated rows, incorrect data types, incorrect values, and missing values) and you will learn about the techniques you can use to easily fix them. But be careful – some issues that you come across don't necessarily need to be fixed. Some of the suspicious or unexpected values you find may be genuine from a business point of view. This...