My Account
Log in Registration
All
Cart
Highlights
Fiction Books
Non-Fiction Books
Children's Books
Rare Books
Music
DVD & Blu-Ray
Video Games
Category
Wishlists
Cart
All
Free US shipping over $10
Trustpilot
Proud to be B-Corp

Introduction to Data Science Martina Topic (Leeds Business School, UK)

Introduction to Data Science By Martina Topic (Leeds Business School, UK)

Introduction to Data Science by Martina Topic (Leeds Business School, UK)

Reviews:
$78.36
Condition - Very Good
Only 1 left
Very Good

Summary

The book begins by going over the basics of R and the tidyverse. You learn R throughout the book, but in the first part we go over the building blocks needed to keep learning during the rest of the book.

Faster Shipping

Get this product faster from our US warehouse

Introduction to Data Science Summary

Introduction to Data Science: Data Analysis and Prediction Algorithms with R by Martina Topic (Leeds Business School, UK)

  • Covers the basics of R and the tidyverse
  • Demonstrate how to use ggplot2 to generate graphs and describe important Data Visualization principles
  • Introduces Data Wranglin topics such as web scrapping, using regular expressions, and joining and reshaping data tables using the tidyverse tools
  • Illustrates the importance of statistics in data analysis using case studies
  • Uses the caret package to build prediction algorithms including K-nearest Neighbors and Random Forests
  • Includes tools used on a day-to-day basis in data science projects including RStudio, UNIX/Linux shell, Git and GitHub, and knitr and R Markdown

Introduction to Data Science Reviews

I think the book would be perfect for schools looking to make a transition to a model where introduction to data science takes the place of introduction to statistics and maybe introductory computer science. ~Arend Kuyper, Northwestern University

A great introduction to data science and modern R programing, with tons of examples of application of the R abilities throughout the whole volume. The book suggests multiple links to the internet websites related to the topics under consideration that makes it an incredibly useful source of contemporary data science and programing, helping to students and researchers in their projects. ~Technometrics

Introduction to Data Science will teach you to juggle with your data and get maximum results from it using R. I highly recommended this book for students and everybody taking the first steps in data science using R. ~ Maria Ivanchuk, ISCB News

About Martina Topic (Leeds Business School, UK)

Rafael A. Irizarry is professor of data sciences at the Dana-Farber Cancer Institute, professor of biostatistics at Harvard, and a fellow of the American Statistical Association. Dr. Irizarry is an applied statistician and during the last 20 years has worked in diverse areas, including genomics, sound engineering, and public health. He disseminates solutions to data analysis challenges as open source software, tools that are widely downloaded and used. Prof. Irizarry has also developed and taught several data science courses at Harvard as well as popular online courses.

Table of Contents

I R. 1 Installing R and RStudio. 2. Getting Started with R and RStudio. 3. R Basics. 4. Programming basics. 5. The tidyverse. 6. Importing data. II Data Visualization. 7. Introduction to data visualization. 8. ggplot2. 9. Visualizing data distributions. 10. Data visualization in practice. 11. Data visualization principles. 12. Robust summaries. III Statistics with R. 13. Introduction to Statistics with R. 14. Probability. 15. Random variables. 16. Statistical Inference. 17. Statistical models. 18. Regression. 19. Linear Models. 20. Association is not causation. IV Data Wrangling. 21. Introduction to Data Wrangling. 22. Reshaping data. 23. Joining tables. 24. Web Scraping. 25. String Processing. 26. Parsing Dates and Times. 27. Text mining. V Machine Learning. 28. Introduction to Machine Learning. 29. Smoothing. 30. Cross validation. 31. The caret package. 32. Examples of algorithms. 33. Machine learning in practice. 34. Large datasets. 35. Clustering. VI Productivity tools. 36. Introduction to productivity tools. 37. Accessing the terminal and installing Git. 38. Organizing with Unix. 39. Git and GitHub. 40. Reproducible projects with RStudio and R markdown.

Additional information

CIN0367357984VG
9780367357986
0367357984
Introduction to Data Science: Data Analysis and Prediction Algorithms with R by Martina Topic (Leeds Business School, UK)
Martina Topic (Leeds Business School, UK)
Chapman & Hall/CRC Data Science Series
Used - Very Good
Hardback
Taylor & Francis Ltd
2019-11-08
713
N/A
Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
This is a used book - there is no escaping the fact it has been read by someone else and it will show signs of wear and previous use. Overall we expect it to be in very good condition, but if you are not entirely satisfied please get in touch with us

Customer Reviews - Introduction to Data Science