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)

Introducing the Human Resource Analytics Dataset

Having learned about basic data cleaning concepts and seen them implemented with pandas and scikit-learn, we'll put what we've learned into practice on a diverse dataset that has real-world context. In the following chapters, we'll model this dataset with a variety of machine learning techniques, so let's take some time to get familiar with it now. Let's imagine the following situation:

Suppose you are hired to do freelance work for a company who wants to find insights into why their employees are leaving. They have compiled a set of data they think will be helpful in this respect. It includes details of employee satisfaction levels, evaluations, time spent at work, department, and salary.

The company shares their data with you by sending you a file called hr_data.csv and asks you what you think can be done to help stop employees from leaving.

Our aim is to apply the concepts we've discussed thus...