Name: Python Data Science Handbook
ISBN: 9781098121228

What Hacker News says

boppo1Jun 2026

Context: I did not study CS in college & wished I had so I read the python data science handbook, K&R's C, and Stroustrup's Programming Practice and Principles. I spend a significant amount of time bashing my head against getting code for personal projects to work (it usually did, eventually!).

I am way way way more capable with AI agents for this experience than if I had not invested the time. I can look at the code they wrote & decide if it's good enough or a bad idea. I have instincts about how to do things. I am much more able to plan things with them. My coworkers with degrees are wildly productive.

If you think you will be interacting with code in any significant way, I would at least read those three books, they will help you understand "how computers really work". If you want to have a career in making software, then definitely learn to write programs without AI.

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TechDebtDevinJan 2025

If I could go back I would start by reading Josh Starmer's Statquest Guide to Machine Learning, and then his guide to AI/Nueral Networks[0]. Starmer does the best job at explaining advanced ML topics in a very beginner friendly way, the books are literally written in the format of a children's book.

Then just start tinkering. I got interested in ML because of sport's analytics and betting markets so I read a lot of papers on that topic and books similar to Bayesian Sports Models in R by Andrew Mack[1].Also, Jake VanderPlas's Python Data Science Handbook is good[2].

Ideally, find a vertical you're interested in where experts have applied ML and read their papers/books and work backwards from there.

[0]: https://statquest.org/statquest-store/ [1]: https://www.goodreads.com/book/show/216487475-bayesian-sport... [2]: https://www.oreilly.com/library/view/python-data-science/978...

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FullStackDev1Apr 2023

I have been a Java Developer then full stack developer all my career. I want to get started on AI. I am not sure where to start as it is overwhelming to look at the rapid changes coming in. Are there a list of books i can start with? My goal is to be not outdated in 6 months and lose my job to chatGPT or some other version of it. So i asked ChatGPT to give me a detailed plan and here is what it gave me. Could you please let me know if this is a way to go or if there is a better way

Transitioning from a full-stack engineer to the AI field can be a fulfilling and exciting journey. To make the most of this transition, you should focus on building a strong foundation in mathematics, programming, and machine learning concepts. Here's a detailed learning plan, broken down into four stages:

Stage 1: Building a Strong Foundation

Mathematics:

Linear Algebra: "Linear Algebra and Its Applications" by Gilbert Strang

Calculus: "Calculus: Early Transcendentals" by James Stewart

Probability & Statistics: "Probability and Statistics for Engineers and Scientists" by Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, and Keying E. Ye

Optimization: "Convex Optimization" by Stephen Boyd and Lieven Vandenberghe

Programming:

Python: "Python Crash Course" by Eric Matthes

Git: "Pro Git" by Scott Chacon and Ben Straub

Machine Learning & Data Science Basics:

"Introduction to Data Science" by Laura Igual and Santi Seguí "Python Data Science Handbook" by Jake VanderPlas

Stage 2: Learning Machine Learning and Deep Learning

Online Courses:

Coursera: "Machine Learning" by Andrew Ng

Coursera: "Deep Learning Specialization" by Andrew Ng

Fast.ai: "Practical Deep Learning for Coders"

Books:

"Pattern Recognition and Machine Learning" by Christopher M. Bishop "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

Stage 3: Expanding Knowledge of AI Subfields

Natural Language Processing (NLP):

"Speech and Language Processing" by Daniel Jurafsky and James H. Martin

"Natural Language Processing with Python" by Steven Bird, Ewan Klein, and Edward Loper

Computer Vision:

"Computer Vision: Algorithms and Applications" by Richard Szeliski

"Deep Learning for Computer Vision" by Adrian Rosebrock Reinforcement Learning:

"Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto

"Deep Reinforcement Learning Hands-On" by Maxim Lapan

Stage 4: Staying Current and Gaining Practical Experience

Read research papers:

Subscribe to the arXiv mailing list in your areas of interest Regularly read papers from conferences like NeurIPS, ICML, and ACL Participate in online competitions:

Kaggle: Participate in machine learning competitions to improve your skills and build your portfolio AIcrowd: Another platform for AI competitions Contribute to open-source projects:

TensorFlow, PyTorch, or other popular AI frameworks Look for projects on GitHub related to your interests and contribute by fixing bugs, implementing new features, or improving documentation Network with AI professionals:

Attend AI conferences, workshops, and local meetups Join AI-related forums, LinkedIn groups, or online communities like Reddit's r/MachineLearning Remember that the learning plan can be adjusted based on your pace and interests. The key is to stay consistent and always be curious to learn more. Good luck on your journey into the AI field!

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westurnerAug 2022

Kaggle learn: https://www.kaggle.com/learn

"Python Data Science Handbook" (O'Reilly) http://jakevdp.github.io/PythonDataScienceHandbook

awesome-python#web-content-extracting: https://github.com/vinta/awesome-python#web-content-extracti...

https://learnxinyminutes.com/docs/python/

"What is the best way to learn to code from absolute scratch?" https://news.ycombinator.com/item?id=16299880

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kthejoker2Jan 2021

I typically recommend a few different books to everyone who finishes the bootcamp, based on a self-assessment they take. I recommend some books based on their strengths, so they can find a career path sooner, and some books based on their weaknesses, so they can widen their cone of oppportunity within ML.

In our consultancy, data science is done in Python and SQL (and PySpark, but I don't hand out books on that during bootcamp!), and ML delivery is a combination of math, software engineering, and architecture/product owner disciplines.

If you're strong in software engineering, I recommend Machine Learning Mastery with Python by Jason Brownlee as it's very hands-on in Python and helps you run code to "see" how ML works.

If you're weak in software engineering and Python, I recommend A Whirlwind Tour Of Python by Jake VanderPlas, and its companion book Python Data Science Handbook.

If you're strong in architecting / product management, I recommend Building Machine Learning Powered Applications by Emmanuel Ameisen since it explains it more from an SDLC perspective, including things like scoping, design, development, testing, general software engineering best practices, collaboration, etc.

If you're weak in architecting / product management, I typically recommend User Story Mapping by Jeff Patton and Making Things Happen by Scott Berkun, which are both excellent how-tos with great examples to build on.

If you're strong in math, I recommend Understanding Machine Learning from Theory to Algorithm by Shalev-Shwartz and Ben-David, as it has all the mathematics for ML and actually has some pseudocode for implementation which helps bridge the gap into actual software development (the book's title is very accurate!)

For someone who is weak in the math of ML, I recommend Introduction to Statistical Learning by Hastie et al (along with the Python port of the code https://github.com/emredjan/ISL-python ) which I think does just enough hand holding to move someone from "did high school math 20 years ago" to "I understand what these hyperparameters are optimizing for."

Anyway, I've spent a lot of time reading and reviewing books about ML, and my key takeaway is ones that get you closer to writing actual code to solving actual problems for actual people are the ones to focus on.

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asicspAug 2020

See also "Python Data Science Handbook" by Jake VanderPlas, which uses Jupyter notebooks too.

[0] https://jakevdp.github.io/PythonDataScienceHandbook/

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