Book Image

The Applied Data Science Workshop - Second Edition

By : Alex Galea
Book Image

The Applied Data Science Workshop - Second Edition

By: Alex Galea

Overview of this book

From banking and manufacturing through to education and entertainment, using data science for business has revolutionized almost every sector in the modern world. It has an important role to play in everything from app development to network security. Taking an interactive approach to learning the fundamentals, this book is ideal for beginners. You’ll learn all the best practices and techniques for applying data science in the context of real-world scenarios and examples. Starting with an introduction to data science and machine learning, you’ll start by getting to grips with Jupyter functionality and features. You’ll use Python libraries like sci-kit learn, pandas, Matplotlib, and Seaborn to perform data analysis and data preprocessing on real-world datasets from within your own Jupyter environment. Progressing through the chapters, you’ll train classification models using sci-kit learn, and assess model performance using advanced validation techniques. Towards the end, you’ll use Jupyter Notebooks to document your research, build stakeholder reports, and even analyze web performance data. By the end of The Applied Data Science Workshop, you’ll be prepared to progress from being a beginner to taking your skills to the next level by confidently applying data science techniques and tools to real-world projects.
Table of Contents (8 chapters)

Approaching Data Science Problems

It's important to ensure you have a well-structured plan for your data science project before you start the analysis and modeling phases. We'll outline some factors to keep in mind when making this plan, and then go over some technical details regarding preparing data for modeling in the next section.

Since this book is centered around Jupyter Notebooks, we'll start by highlighting how useful they are for the planning phase of a data science project. They offer a very convenient medium for documenting your analysis and modeling plans, for example, by writing rough notes about the data or a list of models we are interested in training. Having these notes in the same place as your proceeding analysis can help others understand what you're doing when they see your work or provide context for you when you look back after leaving it for a while.

A large part of data science involves the use of machine learning to build predictive...