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.

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Table of Contents (8 chapters)
Preface
About the Book
Installing Libraries
1
1. Introduction to Jupyter Notebooks
1. Introduction to Jupyter Notebooks
Introduction
Basic Functionality and Features of Jupyter Notebooks
Jupyter Features
Summary
Free Chapter
2
2. Data Exploration with Jupyter
2. Data Exploration with Jupyter
Introduction
Our First Analysis – the Boston Housing Dataset
Summary
3
3. Preparing Data for Predictive Modeling
3. Preparing Data for Predictive Modeling
Introduction
Machine Learning Process
Approaching Data Science Problems
Understanding Data from a Modeling Perspective
Introducing the Human Resource Analytics Dataset
Summary
4
4. Training Classification Models
4. Training Classification Models
Introduction
Understanding Classification Algorithms
Summary
5
5. Model Validation and Optimization
5. Model Validation and Optimization
Introduction
Assessing Models with k-Fold Cross Validation
Dimensionality Reduction with PCA
Summary
6
6. Web Scraping with Jupyter Notebooks
6. Web Scraping with Jupyter Notebooks
Introduction
Internet Data Sources
Introduction to HTTP Requests
Data Workflow with pandas
Summary
Appendix
1. Introduction to Jupyter Notebooks
2. Data Exploration with Jupyter
3. Preparing Data for Predictive Modeling
4. Training Classification Models
5. Model Validation and Optimization
6. Web Scraping with Jupyter Notebooks

Summary

In this chapter, we have seen how to use Jupyter Notebooks to perform parameter optimization and model selection.

We built upon the work we did in the previous chapter, where we trained predictive classification models for our binary problem and saw how decision boundaries are drawn for SVM, KNN, and Random Forest models. We improved on these simple models by using validation curves to optimize parameters and explored how dimensionality reduction can improve model performance as well.

Finally, at the end of the last exercise, we explored how the final model can be used in practice to make data-driven decisions. This demonstration connects our results back to the original business problem that inspired our modeling problem initially.

In the next chapter, we will depart from machine learning and focus on data acquisition instead. Specifically, we will discuss methods for extracting web data and learn about HTTP requests, web scraping with Python, and more data processing...

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