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)

Data Workflow with pandas

As we've seen time and time again in this book, pandas is an integral part of performing data science with Python and Jupyter Notebooks. DataFrames offer us a way to organize and store labeled data, but more importantly, pandas provides time-saving methods for transforming data. Examples we have seen in this book include dropping duplicates, mapping dictionaries to columns, applying functions over columns, and filling in missing values.

In the next exercise, we'll reload the raw tables that we pulled from Wikipedia, clean them up, and merge them together. This will result in a dataset that is suitable for analysis, which we'll use for a final exercise, where you'll have an opportunity to perform exploratory analysis and apply the modeling concepts that you learned about in earlier chapters.

Exercise 6.04: Processing Data for Analysis with pandas

In this exercise, we continue working on the country data that was pulled from Wikipedia...