📖 My Best Data Science Resources
📖 My Best Data Science Resources
Dishing The Data #57
Good morning, friends!
This week has been absolutely boiling, and my sleep has definitely suffered. The UK and hot weather don’t go together, and my room was literally like an oven. Next week looks a lot cooler, which I look forward to!
Anyway, enough of the “classic British ranting about the weather.” In this edition of Dishing The Data, I want to list the best data science resources that have helped me in my career.
The list is not ordered, and the resources cover domains from Python to machine learning.
(PS: Some links are affiliate links that I get a kickback from with no extra cost to you 😎)
Without further ado:
Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow — If I could only give you one book to help you become a data scientist, it would be this.
Practical Statistics for Data Scientists — Ditto with the above; this is the best book to learn probability and statistics for data science.
Machine Learning Specialization — The first course I took on machine learning back in 2020 is probably the best course on ML in history.
Deep Learning Specialization — This is a follow-up to the machine learning course. It is the best course for learning CNNs, RNNs, and an intro to LLMs.
Hacker Rank — Great for practising coding and interview questions.
LearnSQL.com — This is an excellent website for learning and up-skilling SQL.
Mathematics for Machine Learning — This is a really comprehensive book on the maths behind machine learning, although quite advanced.
W3Schools — The best free resource out there for learning coding.
Learn Python - Full Course for Beginners [Tutorial] — The first course I took on Python.
Kaggle.com — Used this in the early days for some data to build simple projects.
Medium — Gold mine for intuitive explanations.
Neural Networks: Zero to Hero — Learning neural networks from probably the best machine learning researcher.
Of course, I used others, but the above list is probably the most important ones that helped me in my career.
Weekly Favourites ❤️
📩 Newsletter — Noah Kagan. I have nearly finished Million Dollar Weekend, so I signed up for the author's newsletter, Noah Kagan. I highly recommend it for anyone business-minded.
🎙️ Podcast — Inside a YouTuber Success Story: Ali Abdaal’s Journey From Medicine to Mastery on Apple Podcasts. Russell Brunson (author of Dotcom Secrets) did a podcast with Ali Abdaal, which is a great listen if you are interested in business. Dotcom Secrets is on my reading list, so getting insight into funnels and marketing is very useful.
✏️ Article — 2024 Stack Overflow Developer Survey. The annual Stack Overflow developer survey results came out this week, and there were some interesting findings. It's worth reading if you work in the tech space.
(PS: Some links are affiliate links that I get a kickback from with no extra cost to you 😎)
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Awesome set of resources, Egor! I’d suggest adding some real-world projects as a follow-up for beginners who want to see how real ML projects come together.
Really enjoyed this!
Have you done any Kaggle challenges? I always see them and don't know where to start