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)

Our First Analysis – the Boston Housing Dataset

The dataset we'll be looking at in this section is the so-called Boston Housing dataset. It contains US census data concerning houses in various areas around the city of Boston. Each sample corresponds to a unique area and has about a dozen measures. We should think of samples as rows and measures as columns. This data was first published in 1978 and is quite small, containing only about 500 samples.

Now that we know something about the context of the dataset, let's decide on a rough plan for the exploration and analysis stages. If applicable, this plan will accommodate the relevant questions under study. In this case, the goal is not to answer a question, but to show Jupyter in action and illustrate some basic data analysis methods.

Our general approach to this analysis will be to do the following:

  • Load the data into Jupyter using a pandas DataFrame
  • Quantitatively understand the features
  • Look for...