I outlined my whole plan here on my Patreon Campaign. You’ll see a new page on this site soon acknowledging supporters, and I’ll update you on the progress.
Whether you can give financially, or even if you just share the campaign with your data science friends, you are helping Becoming a Data Scientist podcast, the learning club, Data Sci Guide, Jobs for New Data Scientists, and all of my websites get off the ground! Thank you!!
Data Scientist, Author, and manager of data science teams Enda Ridge talks to us about data governance, data provenance, reproducible analysis, work pipelines and products, and people, among other topics covered in his book “Guerrilla Analytics – A practical Approach to Working with Data: The Savvy Manager’s Guide”.
Podcast Audio Links:
Link to podcast Episode 7 audio
Podcast’s RSS feed for podcast subscription apps
Podcast on Stitcher
Podcast on iTunes
Podcast Video Playlist:
Youtube playlist of interview videos
More about the Data Science Learning Club:
Data Science Learning Club Welcome Message
Learning Club Activity 7: Linear Regression [coming soon]
Data Science Learning Club Meet & Greet
More show Notes Coming Soon!
Enda’s book on Amazon:
In Episode 2 of the Becoming a Data Scientist Podcast, we meet Safia Abdalla, who started programming and even exploring machine learning and natural language processing as a teenager, and is now a student at Northwestern University, a conference speaker and trainer, co-organizer of PyLadies Chicago, and a contributor to Project Jupyter.
Podcast Audio Links:
Link to podcast Episode 2 audio
Podcast’s RSS feed for podcast subscription apps
(I will distribute the feed out to iTunes and Pocket Cast ASAP. It’s available on Stitcher now!)
Podcast Video Playlist:
Youtube playlist where I’ll publish future videos
More about the Data Science Learning Club:
Data Science Learning Club Welcome Message
Learning Club Activity 2: Creating visuals for exploratory data analysis
Data Science Learning Club Meet & Greet
Here are the links to things Safia references in the video:
information retrieval
Introduction to Information Retrieval by C. D. Manning, P. Raghavan, H. Schütze
natural language processing
NLTK
machine learning
Northwestern Neuroscience and Robotics Lab
pyladies
Chicago PyLadies Meetups
mathematicalmonk’s YouTube series on machine learning
@captainsafia on twitter
Safia’s website
Safia’s blog
JupyterDay Chicago 2016 (post by Safia on jupyter.org)
Jupyter documentation
In this episode we meet Will Kurt, who talks about his path from English & Literature and Library & Information Science degrees to becoming the Lead Data Scientist at KISSmetrics. He also tells us about his probability blog, Count Bayesie, and I introduce Data Science Learning Club Activity 1. Will has some great advice for people learning data science!
Podcast Audio Links:
Link to podcast Episode 1 audio
Podcast’s RSS feed for podcast subscription apps
(I will distribute the feed out to sites like iTunes and Stitcher this week)
Podcast Video Playlist:
Youtube playlist where I’ll publish future videos
More about the Data Science Learning Club:
Data Science Learning Club Welcome Message
Learning Club Activity 1: Find and explore a dataset
Data Science Learning Club Meet & Greet
Here are the links to things Will references in the video:
Library and Information Science
Andrew Ng’s Machine Learning course on Coursera
probabalistic graphical models
Count Bayesie blog
Count Bayesie – Parameter Estimation and Hypothesis Testing
Donald Knuth
Literate programming
Claude Shannon’s Mathematical Theory of Communication
Count Bayesie – Measure Theory
Bayes’ Theorem with Lego
Voight-Kampff and Bayes Factor
Black Friday Puzzle – Markov Chains
Zen Buddhism concept of “beginner’s mind”
Count Bayesie Recommended Books on Probability and Statistics
]]>I had an idea today that would take it a step further. Imagine how book clubs work where you pick a book, go off and read it, then gather occasionally to discuss and record your thoughts. Except instead of a book club, it’s a data science learning club!
I’m imagining picking a topic/project, finding resources showing how to do it, and introducing it to the club at the end of a podcast episode. Then, everyone that wants to participate in learning how to do that particular thing will go off for maybe 2 weeks, work on it and learn what they can, ask questions to each other in a common area like a blog post comment thread, create things and post them to a shared space, then at the end of the period post comments about what they learned and how it went. People could write blog posts about their projects and I would collect those and link to all of them from the original post. Anyone that already knows how to do it could help answer questions if they wanted to participate, too. I might invite some of the participants to talk about their learning experience on a follow-up episode, then the notes and results would be posted for future learners to find.
I think learning together would be fun and valuable, and this type of experience would fall somewhere between learning on your own and taking a class. It would include the pros of learning on your own and exploring, while offsetting some of the cons of going at it alone. It would be a significant time commitment on my part, so I want to make sure other people would join in before I commit. What do you think? Would you join a “data science learning club” and participate in something like this and find it valuable? It’s kind of like the Summer of Data Science, but we’d be learning the same things simultaneously and sharing our results. No one would be “teaching” the group necessarily, but we’d share resources and answer each other’s questions based on what we did individually.
Let me know in the comments or on twitter if you would find this valuable and if you want me to lead it!
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