Chapter 1: Fundamentals of Data Collection, Cleaning, and Preprocessing

Thank you for purchasing this book and welcome to a journal of exploration and excitement! Whether you are already a data scientist, preparing for an interview, or just starting learning, this book will serve you well as a companion. You may already be familiar with common Python toolkits and have followed trending tutorials online. However, there is a lack of a systematic approach to the statistical side of data science. This book is designed and written to close this gap for you.

As the first chapter in the book, we start with the very first step of a data science project: collecting, cleaning data, and performing some initial preprocessing. It is like preparing fish for cooking. You get the fish from the water or from the fish market, examine it, and process it a little bit before bringing it to the chef.

You are going to learn five key topics in this chapter. They are correlated with other topics, such as visualization and basic statistics concepts. For example, outlier removal will be very hard to conduct without a scatter plot. Data standardization clearly requires an understanding of statistics such as standard deviation. We prepared a GitHub repository that contains ready-to-run codes from this chapter as well as the rest.

Here are the topics that will be covered in this chapter:

• Collecting data from various data sources with a focus on data quality
• Data imputation with an assessment of downstream task requirements
• Outlier removal
• Data standardization – when and how
• Examples involving the scikit-learn preprocessing module

The role of this chapter is as a primer. It is not possible to cover the topics in an entirely sequential fashion. For example, to remove outliers, necessary techniques such as statistical plotting, specifically a box plot and scatter plot, will be used. We will come back to those techniques in detail in future chapters of course, but you must bear with it now. Sometimes, in order to learn new topics, bootstrapping may be one of a few ways to break the shell. You will enjoy it because the more topics you learn along the way, the higher your confidence will be.