#### Practical Data Science with Python

##### By :

#### Practical Data Science with Python

##### By:

#### 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.

Table of Contents (30 chapters)

Preface

Part I - An Introduction and the Basics

Free Chapter

Introduction to Data Science

Getting Started with Python

Part II - Dealing with Data

SQL and Built-in File Handling Modules in Python

Loading and Wrangling Data with Pandas and NumPy

Exploratory Data Analysis and Visualization

Data Wrangling Documents and Spreadsheets

Web Scraping

Part III - Statistics for Data Science

Probability, Distributions, and Sampling

Statistical Testing for Data Science

Part IV - Machine Learning

Machine Learning for Classification

Evaluating Machine Learning Classification Models and Sampling for Classification

Machine Learning with Regression

Optimizing Models and Using AutoML

Tree-Based Machine Learning Models

Support Vector Machine (SVM) Machine Learning Models

Part V - Text Analysis and Reporting

Clustering with Machine Learning

Working with Text

Part VI - Wrapping Up

Data Storytelling and Automated Reporting/Dashboarding

Ethics and Privacy

Staying Up to Date and the Future of Data Science

Other Books You May Enjoy

Index

Customer Reviews