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)

Performing Data Aggregation

Alright. We are getting close to the end of this chapter. But before we wrap it up, there is one more technique to explore for creating new features: data aggregation. The idea behind it is to summarize a numerical column for specific groups from another column. We already saw an example of how to aggregate two numerical variables from the ATO dataset (Average net tax and Average total deductions) for each cluster found by k-means using the .pivot_table() method in Chapter 5, Performing Your First Cluster Analysis. But at that time, we aggregated the data not to create new features but to understand the difference between these clusters.

You may wonder to yourself in which cases you would want to perform feature engineering using data aggregation. If you already have a numerical column that contains a value for each record, why would you need to summarize it and add this information back to the DataFrame? It feels like we are just adding the same information...