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)

Pipelines

During your data science journey, you will have realized that there are various processes to go through before you get any final outcomes, including data cleaning, data transformation, modeling, and so on. You will have found that when we perform these activities separately, we have to write individual steps to carry them out. When we have to do these steps for a new dataset, even if it is similar to another one that we have worked on before, we have to repeat the steps again. As you will have noticed, these steps are tedious and repetitive.

Pipeline is a utility from the scikit-learn library that automates many of these tasks. Using this utility, we can stack multiple processes together for use on multiple datasets. This helps us with a lot of the manual steps involved in the data science journey.

Have a look at Figure 16.1; we have used pipeline processes on multiple different datasets. However, thanks to the pipeline processes we are provided with, we can be sure...