Dive Into Data Science
Use Python To Tackle Your Toughest Business Challenges
Published on 4. July 2023
Book
Paperback/Softback
288 pages
978-1-7185-0288-8 (ISBN)
Description
This beginner's book will teach you how to apply the principles of data science to improve your business strategies - no math proficiency required! Easy-to-follow chapters take the reader through concepts like A/B testing, supervised and unsupervised machine learning, web scraping, and more. Each concept is illustrated using real-world business applications, real-world data, and useful Python code examples. The tone is conversational, and the author avoids the dense mathematical theories associated with data science in favour of simple explanations and practical applications. By the end of the book, readers should be comfortable working with data, applying data to business problems, and using best practices to analyse data using Python.
Reviews / Votes
"Strikes a nice balance of explaining fundamental data science concepts and theories, while also equipping readers with hands-on practice with Python. . . . definitely a good place to start data science."-Ben Dickson, TechTalks
"Dive Into Data Science is a book every budding data analyst will want. Tuckfield covers the field with nuance and insight to show you what you can do and lead you to new ground. . . . If you have any interest in data science you'll find this book a welcomed joy and refer to it for years to come."
-David S. Mazel, Principal/Manager Systems Engineer, Regulus-Group
"Another nice learning resource for newbie data scientists."
-Daniel D. Gutierrez, Editor-in-Chief, insideBIGDATA
"I highly recommend this book . . . The trick to writing an introductory Data Science book is to land on the right balance between 'now watch what this magical line of Python code does' and 'here's what's happening and what this approach is good for'. I think Dr. Tuckfield has done it."
-Dr. Daniel Zingaro, author of Algorithmic Thinking and Learn to Code by Solving Problems
"Dive Into Data Science is a concise, accessible guide that demystifies the essential tools and techniques used by data scientists, allowing anyone with a basic understanding of programming to explore and analyze data like a pro."
-Allen B. Downey, Staff Producer at Brilliant and author of Elements of Data Science and Modeling and Simulation in Python
"Comprehensive and thoroughly 'user friendly' in in organization and presentation, [Dive Into Data Science] is the ideal instructional reference guide to utilizing Python as a basic and essential part of doing business in today's increasing complex, competitive, and digital world . . . . a highly recommended addition to personal, professional, community, corporate, college, and university library Computer Programming & Business Management collections and supplemental MBA curriculum studies lists."
-Midwest Book Review
More details
Language
English
Place of publication
San Francisco
United States
Dimensions
Height: 231 mm
Width: 181 mm
Thickness: 22 mm
Weight
578 gr
ISBN-13
978-1-7185-0288-8 (9781718502888)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
E-Book
07/2023
No Starch Press
€31.49
Available for download
Person
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.
Content
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
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