| Author: | John Paul Mueller, Luca Massaron | ISBN: | 9781119547662 |
| Publisher: | Wiley | Publication: | January 29, 2019 |
| Imprint: | For Dummies | Language: | English |
| Author: | John Paul Mueller, Luca Massaron |
| ISBN: | 9781119547662 |
| Publisher: | Wiley |
| Publication: | January 29, 2019 |
| Imprint: | For Dummies |
| Language: | English |
The fast and easy way to learn Python programming and statistics
Python is a general-purpose programming language created in the late 1980s—and named after Monty Python—that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library.
Python For Data Science For Dummies is written for people who are new to data analysis, and discusses the basics of Python data analysis programming and statistics. The book also discusses Google Colab, which makes it possible to write Python code in the cloud.
- Get started with data science and Python
- Visualize information
- Wrangle data
- Learn from data
The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction.
The fast and easy way to learn Python programming and statistics
Python is a general-purpose programming language created in the late 1980s—and named after Monty Python—that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library.
Python For Data Science For Dummies is written for people who are new to data analysis, and discusses the basics of Python data analysis programming and statistics. The book also discusses Google Colab, which makes it possible to write Python code in the cloud.
- Get started with data science and Python
- Visualize information
- Wrangle data
- Learn from data
The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction.
