Book Image

Python: Real-World Data Science

By : Fabrizio Romano, Dusty Phillips, Phuong Vo.T.H, Martin Czygan, Robert Layton, Sebastian Raschka
Book Image

Python: Real-World Data Science

By: Fabrizio Romano, Dusty Phillips, Phuong Vo.T.H, Martin Czygan, Robert Layton, Sebastian Raschka

Overview of this book

The Python: Real-World Data Science course will take you on a journey to become an efficient data science practitioner by thoroughly understanding the key concepts of Python. This learning path is divided into four modules and each module are a mini course in their own right, and as you complete each one, you’ll have gained key skills and be ready for the material in the next module. The course begins with getting your Python fundamentals nailed down. After getting familiar with Python core concepts, it’s time that you dive into the field of data science. In the second module, you'll learn how to perform data analysis using Python in a practical and example-driven way. The third module will teach you how to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis to more complex data types including text, images, and graphs. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. In the final module, we'll discuss the necessary details regarding machine learning concepts, offering intuitive yet informative explanations on how machine learning algorithms work, how to use them, and most importantly, how to avoid the common pitfalls.
Table of Contents (12 chapters)
Free Chapter
Table of Contents
Python: Real-World Data Science
Meet Your Course Guide
What's so cool about Data Science?
Course Structure
Course Journey
The Course Roadmap and Timeline

Chapter 13. Next Steps…

During the course of this book, there were lots of avenues not taken, options not presented, and subjects not fully explored. In this Appendix, I've created a collection of next steps for those wishing to undertake extra learning and progress their data mining with Python. Consider this Hero mode, the second question, of the book.

This appendix is broken up by chapter, with articles, books, and other resources for learning more about data mining. Also included are some challenges to extend the work performed in the chapter. Some of these will be small improvements; some will be quite a bit more work—I've made a note on those tasks that are noticeably more extensive than the others.

Chapter 1 – Getting Started with Data Mining

Scikit-learn tutorials

Included in the scikit-learn documentation is a series of tutorials on data mining. The tutorials range from basic introductions...