For starters, I’d like to give Thinkful a big shoutout for pointing me in the direction of many of these.
I’ve sorted the resources by media type, and some of them may contain a mix of topics such as statistics, data analysis, python, machine learning, and/or deep learning. I will state (whenever it is applicable) when a resource is for just one data science topic in particular. There are also lots of websites out there to help you learn/practice coding skills in R, Python, SQL, etc. (HackerRank is a fun one), but this article will focus more on ones that help you learn the concepts of data science and how to use coding languages in a data analysis/machine learning application.
Happy learning!
Websites
This website has an awesome archive of resources (the heading for this section links directly to the free ones). From textbooks to Ivy League course notes to blog posts , you can learn a ton from Data Science 101.
GitHub
The wonderful collaborative spirit of GitHub led to this mega list of data science blogs . There is an overwhelming amount of blogs here, so I would make sure that the ones you choose to read/follow are credible.
This online course material is really great for learning basic statistics before you jump right into learning data science. I have quite a few pages from Penn State bookmarked for reference.
Wow! So many free courses on machine learning on this page. I recommend looking at the highest-rated ones and also reading the reviews to find one that suits your needs.
Open-Source Textbooks
I honestly cannot believe this book is free. It goes very deep into using the Pandas and NumPy libraries for data analysis in Python . There is also a GitHub repository with Jupyter Notebooks for each chapter and the solutions to the exercises!
I have not read this one myself, but it comes recommended to me from my data science mentor. It is formatted very nicely and comes with discussion boards and links to supplementary code on GitHub. Topics include many types of neural networks, optimizing algorithms & computational performance, natural language processing, recommendation systems, and more . I’m personally excited to read through this one when I have the time.
Podcasts
This is the only data science podcast that I have listened to, but it has been so interesting! A large focus right now for the host is the interpretability of machine learning , but he covers other related topics in his latest section as well (like adversarial neural networks and ethical algorithm design ). Some episodes are a bit too in-depth on the computer science side of things for me to grasp, but overall it has deepened my understanding of hot topics in the field and helped increase my passion for data science. ( Data Skeptic )
Other
Data Science Meetups
Go on meetup.com and search for data science meetups in your area (this mainly applies to those who live in or near larger cities)*. I live near Denver and I attended a meetup for women in data science called “Code and Coffee.” It was a sweet time to discuss data science with other women who were either already in the field or wanting to break in. The leaders of the group were awesome — they truly wanted to help other women succeed in their career goals or with their side projects.
* with the coronavirus epidemic, meetups are currently not being scheduled. You can still, however, search for groups on their page.
With the number of links I have included in this article, some are bound to expire or change at some point. Please comment below if you find that a link no longer works. Also, feel free to comment with your own favorite free resource that is not included in this list!
All images without credit given are my own, made on canva.com.
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深入浅出Web设计(中文版)
(美)瓦特罗尔、(美)西罗托 / O'Reilly Taiwan公司 / 东南大学出版社 / 2010-11 / 99.00元
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