Data Science from Scratch

Free download Data Science from Scratch (First Principles With Python) in PDF written by Joel Grus.

According to the Author, “Data scientist has been called “the sexiest job of the 21st century,” presumably by someone who has never visited a fire station. Nonetheless, data science is a hot and growing field, and it doesn’t take a great deal of sleuthing to find analysts breathlessly prognosticating that over the next 10 years, we’ll need billions and billions more data scientists than we currently have.
But what is data science? After all, we can’t produce data scientists if we don’t know what data science is. According to a Venn diagram that is somewhat famous in the industry, data science lies at the intersection of:

• Hacking Skills
• Math and Statistics Skills
• Substantive Expertise

Although I originally intended to write a book covering all three, I quickly realized that a thorough treatment of “substantive expertise” would require tens of thousands of pages. At that point, I decided to focus on the first two. My goal is to help you develop the hacking skills that you’ll need to get started doing data science. And my goal is to help you get comfortable with the mathematics and statistics that are at the core of data science.
This is a somewhat heavy aspiration for a book. The best way to learn hacking skills is by hacking on things. By reading this book, you will get a good understanding of the way I hack on things, which may not necessarily be the best way for you to hack on things. You will get a good understanding of some of the tools I use, which will not necessarily be the best tools for you to use. You will get a good understanding of the way I approach data problems, which may not necessarily be the best way for you to approach data problems.

There are lots and lots of data science libraries, frameworks, modules, and toolkits that efficiently implement the most common (as well as the least common) data science algorithms and techniques. If you become a data scientist, you will become intimately familiar with NumPy, with scikit-learn, with pandas, and with a panoply of other libraries. They are great for doing data science. But they are also a good way to start doing data science without actually understanding data science.
In this book, we will be approaching data science from scratch. That means we’ll be building tools and implementing algorithms by hand in order to better understand them. I put a lot of thought into creating implementations and examples that are clear, well-commented, and readable. In most cases, the tools we build will be illuminating but impractical. They will work well on small toy data sets but fall over on “web scale” ones.

Table of Contents

1. Introduction
2. A Crash Course in Python
3. Visualizing Data
4. Linear Algebra
5. Statistics
6. Probability
7. Hypothesis and Inference
8. Gradient Descent
9. Getting Data
10. Working with Data
11. Machine Learning
12. K-Nearest Neighbors
13. Naive Bayes
14. Simple Linear Regression
15. Multiple Regression
16. Logical Regression
17. Decision Trees
18. Neural Networks
19. Clustering
20. Natural Language Processing
21. Network Analysis
22. Recommender Systems
23. Database and SQL
24. MapReduce
25. Go Forth and Do Data Science
26. Indexes

Free download Data Science from Scratch in PDF Written by Charles R. Severance from following download links.

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