Dive Into Data Science: Use Python To Tackle Your Toughest Business Challenges

Description

Learn how to use Data Science and Python to solve everyday business problems. Dive into the exciting world of Data Science with this practical introduction. Packed with essential skills and useful examples, Dive Into Data Science will show you how to obtain, analyze, and visualize data so you can leverage its power to solve common business challenges. With only a basic understanding of Python and high school math, you’ll be able to effortlessly work through the book and start implementing data science in your day-to-day work. From improving a bike sharing company to extracting data from websites and creating recommendation systems, you’ll discover how to find and use data-driven solutions to make business decisions. Topics covered include conducting exploratory data analysis, running A/B tests, performing binary classification using logistic regression models, and using Machine Learning algorithms. You’ll also learn how to Forecast consumer demand Optimize marketing campaigns Reduce customer attrition Predict website traffic Build recommendation systems With this practical guide at your fingertips, harness the power of programming, mathematical theory, and good old common sense to find data-driven solutions that make a difference. Don’t wait; dive right in! Who Is This Book For? While we explain each code snippet in everyday language to make the book digestible for someone with no Python experience and not much programming experience, someone who has at least some basic understanding of the fundamentals of programming—things like variable assignment, for loops, if...then statements, and function calls—will be the most prepared to benefit from the content in this book. This book was written with the following groups in mind: 1) Aspiring data scientists These days, it seems like everyone wants to be a data scientist, and every company wants to hire data scientists. This book will help a beginner who is just entering the job market get the skills they need to work in the data science field. It can also help people who already have other careers and want to jump laterally to become data scientists or start doing more data science in their current roles. 2) Students This book is suitable for an introductory class on data science at the undergraduate level or for interested students to read independently. 3) Professionals Several types of professionals, including project managers, executive-level leaders, developers, and businesspeople in general, can benefit from understanding what their data scientist colleagues do all day. Gaining the skills in this book can help them work with data scientists more fruitfully. 4) Interested amateurs You don’t have to read this book just for career advancement purposes. Data science is a new, exciting field, and any interested amateur will find this book fascinating and edifying. Introduction Chapter 1: Exploratory Data Analysis Chapter 2: Forecasting Chapter 3: Group Comparisons Chapter 4: A/B Testing Chapter 5: Binary Classification Chapter 6: Supervised Learning Chapter 7: Unsupervised Learning Chapter 8: Web Scraping Chapter 9: Recommendation Systems Chapter 10: Natural Language Processing Chapter 11: Data Science in Other Languages Index