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

You just learned a lot regarding how to analyze a dataset. This a very critical step in any data science project. Getting a deep understanding of the dataset will help you to better assess the feasibility of achieving the requirements from the business.

Getting the right data in the right format at the right level of quality is key for getting good predictive performance for any machine learning algorithm. This is why it is so important to take the time analyzing the data before proceeding to the next stage. This task is referred to as the data understanding phase in the CRISP-DM methodology and can also be called Exploratory Data Analysis (EDA).

You learned how to use descriptive statistics to summarize key attributes of the dataset such as the average value of a numerical column, its spread with standard deviation or its range (minimum and maximum values), the unique values of a categorical variable, and its most frequent values. You also saw how to use data visualization...