Details zum E-Book

Practical Data Science with Python. Learn tools and techniques from hands-on examples to extract insights from data

Practical Data Science with Python. Learn tools and techniques from hands-on examples to extract insights from data

Nathan George

E-book
Practical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science.

The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion.

As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments.

By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source.
  • 1. Introduction to Data Science
  • 2. Getting Started with Python
  • 3. SQL and Built-in File Handling Modules in Python
  • 4. Loading and Wrangling Data with Pandas and NumPy
  • 5. Exploratory Data Analysis and Visualization
  • 6. Data Wrangling Documents and Spreadsheets
  • 7. Web Scraping
  • 8. Probability, Distributions, and Sampling
  • 9. Statistical Testing for Data Science
  • 10. Preparing Data for Machine Learning: Feature Selection, Feature Engineering, and Dimensionality Reduction
  • 11. Machine Learning for Classification
  • 12. Evaluating Machine Learning Classification Models and Sampling for Classification
  • 13. Machine Learning with Regression
  • 14. Optimizing Models and Using AutoML
  • 15. Tree-Based Machine Learning Models
  • 16. Support Vector Machine (SVM) Machine Learning Models
  • 17. Clustering with Machine Learning
  • 18. Working with Text
  • 19. Data Storytelling and Automated Reporting/ Dashboarding
  • 20. Ethics and Privacy
  • 21. Staying Up to Date and the Future of Data Science
  • Titel: Practical Data Science with Python. Learn tools and techniques from hands-on examples to extract insights from data
  • Autor: Nathan George
  • Originaler Titel: Practical Data Science with Python. Learn tools and techniques from hands-on examples to extract insights from data
  • ISBN: 9781801076654, 9781801076654
  • Veröffentlichungsdatum: 2021-09-30
  • Format: E-book
  • Artikelkennung: e_2a7s
  • Verleger: Packt Publishing