Dive Into Data Science

Informationen zum Autor 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. Klappentext "Learn how to apply the principles of data science to improve business strategies. Chapters cover concepts such as 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"-- Zusammenfassung 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! Inhaltsverzeichnis 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: Other Languages...

Autorentext

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.

Klappentext

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!

  Inhalt
  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