Builds production-ready data science environments from scratch for hands-on learning
Teaches Python and R workflows for data cleaning, visualization, and modeling to solve real-world problems
Includes Git version control for managing and organizing code in professional data teams
Connects skills to industry use cases and advanced topics for career readiness
Step-by-step guidance on importing, cleaning, and transforming data for actionable insights
Summarized by Shop
From Beginner to Practitioner: A Practical Path to Learning Data Science
Key Features
● Build production-ready data science environments from scratch.
● Learn Python and R through complete, real-world workflows for cleaning, visualizing, and modeling data.
● Learn real-world and practical workflows used by modern data organizations.
Book Cover Type
Paperback, Soft
Genre
Education, Technology
Language Version
ENG-English
Target Audience
Adults
Table of Contents
1. Overview of Data Science, 2. Programming Languages and Environments, 3. Setting Up Data Science Environment, 4. Importing and Cleaning Data in Python and R, 5. Data Wrangling and Manipulation in Python and R, 6. Data Visualization in Python and R, 7. Introduction to Data Science Algorithms, 8. Implementing Machine Learning Models, 9. Version Control with Git, 10. Data Science and Analytics in Industry, 11. Advanced Topics and Next Steps