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

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!

ISBN-13: 9781718502888

Media Type: Paperback

Publisher: No Starch Press

Publication Date: 07-04-2023

Pages: 288

Product Dimensions: 7.00(w) x 9.10(h) x 0.70(d)

Bradford Tuckfield is a data scientist, a consultant, and a writer. He received a PhD from the Wharton School of the University of Pennsylvania, and a BS in Mathematics from Brigham Young University. He is the author of Dive Into Algorithms (No Starch Press) and Applied Unsupervised Learning with R (Packt). In addition to working as a data scientist and tech manager for top finance firms and startups, his research has appeared in academic journals spanning math, business management, and medicine.

Table of Contents

Acknowledgments
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 Show More