Book Image

Practical Data Science with Python

By : Nathan George
Book Image

Practical Data Science with Python

By: Nathan George

Overview of this book

Practical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science. The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion. As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments. By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source.

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Table of Contents (30 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
1
Part I - An Introduction and the Basics
Part I - An Introduction and the Basics
Free Chapter
2
Introduction to Data Science
Introduction to Data Science
The data science origin story
The top data science tools and skills
Specializations in and around data science
Data science project methodologies
Test your knowledge
Summary
3
Getting Started with Python
Getting Started with Python
Installing Python with Anaconda and getting started
Python basics
Software engineering best practices
Test your knowledge
Summary
4
Part II - Dealing with Data
Part II - Dealing with Data
5
SQL and Built-in File Handling Modules in Python
Introduction
Loading, reading, and writing files with base Python
Using SQLite and SQL
Using the SQLAlchemy package in Python
Test your knowledge
Summary
6
Loading and Wrangling Data with Pandas and NumPy
Loading and Wrangling Data with Pandas and NumPy
Data wrangling and analyzing iTunes data
Understanding NumPy basics
Using NumPy mathematical functions
Test your knowledge
Summary
7
Exploratory Data Analysis and Visualization
Exploratory Data Analysis and Visualization
EDA and visualization libraries in Python
Performing EDA with Seaborn and pandas
Using EDA Python packages
Using visualization best practices
Making plots with Plotly
Test your knowledge
Summary
8
Data Wrangling Documents and Spreadsheets
Data Wrangling Documents and Spreadsheets
Parsing and processing Word and PDF documents
Reading and writing data with Excel files
Test your knowledge
Summary
9
Web Scraping
Web Scraping
Understanding the structure of the internet
Performing simple web scraping
Parsing HTML from scraped pages
Using APIs to collect data
The ethics and legality of web scraping
Test your knowledge
Summary
10
Part III - Statistics for Data Science
Part III - Statistics for Data Science
11
Probability, Distributions, and Sampling
Probability, Distributions, and Sampling
Probability basics
Distributions
Sampling from data
Test your knowledge
Summary
12
Statistical Testing for Data Science
Statistical Testing for Data Science
Statistical testing basics and sample comparison tests
Other statistical tests
Test your knowledge
Summary
13
Part IV - Machine Learning
Part IV - Machine Learning
14
Preparing Data for Machine Learning: Feature Selection, Feature Engineering, and Dimensionality Reduction
Preparing Data for Machine Learning: Feature Selection, Feature Engineering, and Dimensionality Reduction
Types of machine learning
Feature selection
Feature engineering
Dimensionality reduction
Test your knowledge
Summary
15
Machine Learning for Classification
Machine Learning for Classification
Machine learning classification algorithms
Test your knowledge
Summary
16
Evaluating Machine Learning Classification Models and Sampling for Classification
Evaluating Machine Learning Classification Models and Sampling for Classification
Evaluating classification algorithm performance with metrics
Sampling and balancing classification data
Test your knowledge
Summary
17
Machine Learning with Regression
Machine Learning with Regression
Linear regression
Regression models on big data
Forecasting
Test your knowledge
Summary
18
Optimizing Models and Using AutoML
Optimizing Models and Using AutoML
Hyperparameter optimization with search methods
Using learning curves
Optimizing the number of features with ML models
Using AutoML with PyCaret
Test your knowledge
Summary
19
Tree-Based Machine Learning Models
Tree-Based Machine Learning Models
Decision trees
Feature importance from tree-based methods
Boosted trees: AdaBoost, XGboost, LightGBM, and CatBoost
Test your knowledge
Summary
20
Support Vector Machine (SVM) Machine Learning Models
Support Vector Machine (SVM) Machine Learning Models
How SVMs work
Using SVMs
Test your knowledge
Summary
21
Part V - Text Analysis and Reporting
Part V - Text Analysis and Reporting
22
Clustering with Machine Learning
Clustering with Machine Learning
Using k-means clustering
Hierarchical clustering
DBSCAN
Other unsupervised methods
Test your knowledge
Summary
23
Working with Text
Working with Text
Text preprocessing
Basic text analysis
Unsupervised learning
Supervised learning
Sentiment analysis
Test your knowledge
Summary
24
Part VI - Wrapping Up
Part VI - Wrapping Up
25
Data Storytelling and Automated Reporting/Dashboarding
Data Storytelling and Automated Reporting/Dashboarding
Data storytelling
Automated reporting and dashboarding
Test your knowledge
Summary
26
Ethics and Privacy
Ethics and Privacy
The ethics of machine learning algorithms
Data privacy
Data privacy regulations and laws
k-anonymity, l-diversity, and t-closeness
Differential privacy
Using data science for the common good
Other ethical considerations
Test your knowledge
Summary
27
Staying Up to Date and the Future of Data Science
Staying Up to Date and the Future of Data Science
Blogs, newsletters, books, and academic sources
Data science competition websites
Online learning platforms
Cloud services
Other places to keep an eye on
Strategies for staying up to date
Other data science topics we didn't cover
The future of data science
Summary
28
Other Books You May Enjoy
Other Books You May Enjoy
29
Index
Index

Preface

"Better than any statistician at computer science and better at statistics than any computer scientist" – this is a phrase I've heard said about data scientists since I started my official data science training. It might be true, but data science has grown to incorporate so many different fields and technologies that it might not be able to be captured with such a simple statement anymore. Not to mention that statistics, and especially computer science, cover a lot of ground, too. But as a quick-and-dirty way to describe data science in three words, "statistics + computer science" works.

Many people learn data science to improve their lives. For me, I wanted to transition out of the physical sciences, which are bound by physical locations, and have more freedom to travel around the world. Working in a digital space like data science allows for that, while high-tech manufacturing doesn't. For others, the increase in pay is alluring. For many of us, we see the stories about data scientists being happy and highly paid and are immediately interested in learning more. Some people learn data science due to their intellectual curiosity and the fun of it. In any case, if you want to be a data scientist, you'd better enjoy working with computers and data!

I wrote this book for a few reasons, and one good reason to create teaching materials or even teach courses is you will learn the materials better by teaching it. So, one thing I'd recommend doing if you want to really learn is to create some teaching materials. An easy way to do this is to write a blog post about using data science to solve a problem. It could be any dataset from Kaggle, for example, or some data you've got access to and are allowed to share.

In the book, we use Python to carry out data science. However, there are a plethora of tools for doing data science, so don't feel like Python is the only way. There is a debate among data scientists whether or not a data scientist must be able to program. On the one hand, being able to code enables us to use cutting-edge tools and integrate into other software products more easily.

On the other hand, not all data science work is the same, and some doesn't have to be done with code. Many people doing data science use R and other tools (such as GUIs) to carry out their work. However, Python seems to be the top choice and integrates nicely into software stacks at companies. Python, like any other skill, requires practice and dedication to master. This book is here to get you started, and I hope you have fun learning Python and data science and are excited to continue your data science journey well beyond this book.

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