Data science book club

Hello everyone. I'm working through Data Science from Scratch by Joel Grus. It involves coding algorithms from scratch in Python. I sometimes get stuck or wonder if my solution is optimal. It’d be awesome to have a few people to compare code with and discuss the why behind it. We can pick a chapter, implement the exercises separately, then come together to review our approaches. Ideal for those who want to really understand data science fundamentals by coding them. I’m in GMT but very flexible on coordination.
Competent
English,Hindi
Description
To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, and toolkits—but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today’s messy glut of data.
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Data Science from Scratch, 2nd Edition
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Data science book club
Hello everyone. I'm working through Data Science from Scratch by Joel Grus. It involves coding algorithms from scratch in Python. I sometimes get stuck or wonder if my solution is optimal. It’d be awesome to have a few people to compare code with and discuss the why behind it. We can pick a chapter, implement the exercises separately, then come together to review our approaches. Ideal for those who want to really understand data science fundamentals by coding them. I’m in GMT but very flexible on coordination.
Competent
English,Hindi
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Type
-
Provider
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PricingExclusively Paid
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Duration9h 18m
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CertificateNo Certificate
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Pages403 pages
To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, and toolkits—but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch.
If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today’s messy glut of data.
- Get a crash course in Python
- Learn the basics of linear algebra, statistics, and probability—and how and when they’re used in data science
- Collect, explore, clean, munge, and manipulate data
- Dive into the fundamentals of machine learning
- Implement models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering
- Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
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