Hey y'all, thanks for signing up for The Data Digest!
I’m Ayodele and you can consider me your Data Science Tour Guide. I hope to introduce you to topics in Data Science, Machine Learning, and Artificial Intelligence.
Who Am I?
I changed careers from Social Media Marketing to Data Science in 2016 and have learned so much along the way. I have spoken at several industry conferences and appeared on several podcasts discussing Machine Learning. This newsletter is my way of sharing my best methods for learning Data Science, biasses we have to reduce, and tips to support you in your career and secure a six-figure bag. I hope to help aspiring Data Scientists navigate the fast-paced field and find fulfilling careers. For more about me, read more about my road to becoming a Data Scientist.
Data Science Roadmap 🗺
One of the hardest parts about breaking into data science is knowing what path you should take. There are bootcamps, Master’s degrees, online certifications, and self-taught routes, but in an industry so new, each still seems undefined. I’ll lay out resources to help you decide what roles in Data Science fits you best.
There's a lot of hype around jobs in ML, but not a lot of resources on deciding what career paths may be best for you. I love this quiz because it considers your previous experience and interests to point you in the right direction.
This large and nearly exhaustive list of Data Science topics can seem overwhelming, but it's a great start to plan out your learning. Topics are sequential in each category and this outlines a great way to start digesting technical concepts.
Bad, AI 👾
We need to see a drastic culture change amongst the people making machine learning products. One of the reasons we desperately need more women and people of color in tech is because as we’re more likely to be marginalized, it’s easier to spot misuses in the tech we create.
Clearview AI is working on some genuinely creepy tech. Unfortunately, Clearview AI is making it easier for law enforcement to match our photos with our online identities.
My Favorite Things 🌟
Speaking of creepy uses for technology, one of my new favorite Podcasts aptly titled Creepy Tech by Lydia Shompole details the way products marketed to us as helpful assistants and productivity tools are really spying on us and making our data less secure.
Think like a Programmer by V. Anton Spraul. I got this book as part of a Humble Bundle and suggest it for anyone transitioning careers from a non-technical field to technical or just wanting to think in programmatic terms. This book has helped me to see programming problems I've run into as puzzles and gave me a framework for considering constraints to better solve problems.
You have 1 day left to pick up this Humble Bundle of Data Science and Machine Learning books from Pluralsight. Pay what you want from $1-$20.
I've been working closely with Time Series data recently at work and this YouTube playlist of videos really helped demystify some of the "magic" behind time series prediction models.
This book is a great resource for getting started in Python Data Science by leveraging popular packages like NumPy, pandas, matplotlib, and sci-kit-learn to clean, visualize, and model data.
Enroll in Coursera's Applied ML course taught by Anna Koop of the Alberta Machine Intelligence Institute. This 9 hour course is the first in Coursera's four-part Machine Learning Specialization. You can audit the course for free now.
If you follow me on Twitter you may know I’m a Women in Data Science Ambassador and putting on a one-day conference on May 9th in San Diego highlighting awesome womxn in Data Science.
With talks like: “I’m Just an Engineer”: What are the Ethical Responsibilities of Developers, How to Break into Data Science, and Reproducing WEB Du Bois’ Paris Exposition Exhibit, the diverse single-track conference is set to be a treat.
Can’t make it in person? Catch the Livestream!
Connect with me!
Feel free to check out my blog as well.
Give me a hand
If you’re trying to transition careers to Data Science I could really use your opinion. I’m creating tools to help career changers successfully make a path for themselves and information on what and how you’d like to learn would be appreciated.
If you’ve got 5-10 minutes please fill out this survey on the types of resources you think would best help you make the jump.
Until next time, Keep coding on!