Data Digest .006

The one where she says goodbye to 2020.

Hey Friends,

It’s been a whirlwind of a year. I was laid off twice, incorporated my business, got a book deal with Wiley, recorded a LinkedIn Learning course, and landed a new Data Science role! 🥵

This summer, we had a global racial equity reckoning, and we’ve been in a devastating pandemic since March. We have seen an unfortunate, preventable loss of life because of the pandemic and large economic rifts. I’m incredibly thankful several of my family members have recovered from COVID— 19 as so many others are not as lucky.

Right now, I feel even more motivated to do my part in the revolution. I will keep speaking up about bias in Machine Learning because it impacts me, personally, amongst millions of historically marginalized people.

This success doesn’t mean I haven’t faced many challenges this year. This most recent layoff was incredibly difficult as it left me “tech-worker rich” as my past income disqualifies me from most unemployment benefits and stimulus relief. However, I’m still left with no way to pay bills other than support from the amazing folks in my network. Thankfully there are plenty of ways you can support me!

I officially have a Patreon with several tiers and awesome perks ranging from exclusive webinars, podcasts, and access to my Discord channel!

Become a Patron

This edition will be a little different as I’ve taken a long break, and it’s the end of the year! I have a lot to catch up on. We can all kiss 2020 goodbye and hope a new year, fresh slate, and the new administration can get us closer to closing the equity gap and controlling the pandemic.

If you’re compiling your list of 2021 goals or setting your resolutions, I recommend putting at least one of the following books on your must-read list.

🎊 2021 Book Recommendations

Weapons of Math Destruction is my favorite book for beginners to gain an understanding of big data bias. This book has set my foundational knowledge in data ethics and exposes many areas of inequality and how it’s perpetuated by technology.

Algorithms of Oppression sheds light on the many issues around search engines and how they reinforce racist ideas. Safiya Noble has many examples of the phrase “black girls” consistently pulling pornographic images to searches on “black on white crime,” serving right-wing propaganda sites the Charleston shooter referenced in his manifesto on what led him to kill nine church members in their South Carolina Church.

Automating Inequality covers the ways technological tools built to improve decision-making, penalize the poor, and withhold access to vital services like healthcare and housing.

Shameless Self Promo

My E-book Getting Started in Data Science is out, and you can pick it up for only $10! (originally $29) I’m so thankful for the awesome community I’ve been able to cultivate, and I hope this helps you take the next step in your career! For everyone who’s subscribed to my newsletter, get $20 off the book with the code DATADIGEST.

Buy Now

If you have more than enough this year, you can sponsor a copy of Getting Started in Data Science for someone who can’t afford to invest in their learning right now. I’ve had hundreds of requests for the book by those internationally or laid off this year. Give the gift of learning by donating a copy via my Paypal.

Sponsor a Book

Recent Talks

Hawai‘i Data Science Institute - Tangible Steps Towards Algorithmic Accountability

Deeplearning.AI PIE & AI Meetup - Building a Data Science Career

Women in AI Ethics 2020 - Building Diverse Data Teams

💫 Good News

I’m incredibly overwhelmed with the support I’ve received from the following tweet.

I’m excited to announce that I’ll be joining Comet ML as a Data Scientist! I’m excited to be part of a team that’s building products ALL Data Scientists can use to make their ML research more reproducible! More on my awesome new role later, but until then, I urge you to try using Comet on your next project. When recruited, I was genuinely disappointed I hadn’t heard of them sooner.

Data Science Roadmap🗺

Career Coaching

One of the things I’m really excited to offer is Data Science Career Coaching!

I wish group career coaching was more popular in the Data Science space years ago when I was getting started. I’m offering an affordable way for people to get personalized learning plans, activities, and live coaching to help guide their learning and career planning.

Get on the Waitlist

Data Science for All

There are many great ways to learn Data Science, and for those looking for FREE programs, I suggest Data Science for All by Correlation One. It’s a 13-week training that’s on Saturdays taught by Harvard and MIT professors. The program starts next spring, but applications are open, and I highly suggest you at least apply for this opportunity to learn from high-quality instructors for free. You can check out all the details in this brochure for more information.

Apply Now

My Favorite Things 🌟


The book currently on my nightstand is CODE. While this book isn’t focused on data science specifically, it’s helped me understand the building blocks of the data I get to play with every day.


My favorite podcast, as of late, has got to be the Data Futurology podcast. I love this recent episode on soft or professional skills as they can easily be overlooked.

Freebies 🤑

While fairly math-heavy and not geared for beginners, I really enjoy this book on the Mathematics of Machine Learning. This free book has helped me understand ML on a much deeper level by knowing the foundational formulas that compose many popular algorithms.

Events 🚀

I’m launching a monthly webinar series starting in January that will help learners better understand the data science job market and prepare for careers in AI/ML.


🥅 Goals for 2021

This year has changed a lot of things, including the future of work. One thing I’ve been able to do is to assess how the pandemic has impacted my work schedule and plan accordingly.

Final Thoughts

Move into this new year with a growth mindset. Skills you don’t know aren’t things you’re ignorant about; you just haven’t learned them...yet.

Don’t be overwhelmed by all the information that’s out there. Data Science is vast and still growing. It’s okay to feel behind the curve; just stop benchmarking yourself against others who are in the middle of their journey and not at the beginning.

Move where the money resides. My mom always encouraged me to take the job across the country or the world if it meant I would be in a better position and have more options to do what I wanted. Don’t be afraid of industry or role changes as the “pain” is temporary, but the lasting impact on your career is worth it.

Have a safe and Happy New Year!