Data Science from Scratch
Joel Grus works at the Allen Institute for Artificial Intelligence as a research engineer. He previously worked at Google as a software engineer and at various startups as a data scientist. He resides in Seattle and attends data science happy hours on a regular basis.
To truly grasp data science, you must not only master the tools (data science libraries, frameworks, modules, and toolkits), but also the ideas and principles that underpin them. This second edition of Data Science from Scratch, updated for Python 3.6, demonstrates how these tools and techniques function by implementing them from scratch.
If you have a mathematical aptitude and some programming abilities, author Joel Grus will help you become acquainted with the arithmetic and statistics at the heart of data science, as well as the hacking skills required to get started as a data scientist. This updated book shows you how to locate the diamonds in today's jumbled overflow of data, with new material on deep learning, statistics, and natural language processing.
- Take a Python crash course.
- Learn the fundamentals of linear algebra, statistics, and probability, as well as how and when they are applied in data science.
- Data collection, exploration, cleaning, munging, and manipulation
- Explore the principles of machine learning.
- Models such as k-nearest neighbors, Nave Bayes, linear and logistic regression, decision trees, neural networks, and clustering should be implemented.
- Investigate recommender systems, natural language processing, network analysis, MapReduce, and database technologies.
Author: Joel Grus
Link to buy: https://www.amazon.com/Data-Science-Scratch-Principles-Python/dp/1492041130/
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