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)

Understanding Data from a Modeling Perspective

When attempting to understand a business problem in the context of machine learning, we need to identify whether the problem lends itself to supervised or unsupervised learning. Can the problem be solved by modeling a target variable? If yes, then is this variable available in the dataset? Is it numerical or categorical? The answers to these questions will allow us to identify which modeling algorithms will be relevant to our problem. The following diagram provides an overview of this process:

Figure 3.1: A flowchart for identifying types of modeling problems

The preceding flowchart describes the various paths we can follow in order to categorize a dataset for modeling. At the first branch, we are interested in identifying whether a target variable exists. For example, in a weather forecast model, it could be a column recording the amount of rainfall historically. Or perhaps the target variable is a column labeling...