How a black woman with no CS degree or bootcamp is teaching herself data science
I’m teaching myself data science!!
It’s funny. I tried to write down everything I was doing and planning to discuss that I actually stopped writing this. This was originally initiated on the 17th. You see what trying to be perfect does to you? It makes you not even finish things you’re really excited about! If you don’t get anything from this, get this: perfect is the enemy of done.
How a black woman with no CS degree or boot camp is teaching herself data science
WHY am I doing this?
Long story short: I’m bored and I’m broke (or as the cool kids say, I’m pre-rich!)
My name is Blossom. I’m black. I went to school, but not for a CS degree. And I’m teaching myself data science.
I’m also a business owner. I create content and social media strategies for health professionals. But, two of my past clients were actually tech-related: one was a non-profit (Real I.M.P.A.C.T, STEM education for girls), and the other was International Data Group, otherwise known as IDG. While I was creating content for the two, I stumbled across funny memes that didn’t always make sense to me, but I laughed at them anyway…or rather I pushed air through my nose and smiled.
I knew I wanted to get into tech someway somehow because of my clients. Not only did working for these companies show me that there’s a huge need for black women in tech (if you’re not at the table, you’re ON THE MENU, sis!), but tech is also synonymous to dolla dolla bills, y’all. While I have lost a lot of interest in what my mom thinks about me, it would be nice to prove to her I don’t need to go to med school to be rich and successful. My mom doesn’t really take my business seriously, and she keeps asking me when I’m going to fulfill my 10-year-old dream of becoming a medical doctor (I’m 23 now btw). But what if she learned that I also am an ENGINEER? I mean, data science doesn’t really fall in that category, but it’s close enough.
Not to mention, it’s the only other “real” career path my Nigerian parents would celebrate. The pleasing-my-parents ship has sailed long, long time ago, but the med school book mom’s been banging on my head can finally be closed and lit on fire.
I wanted to get into tech but I didn’t know what. I wasn’t interested at all in building apps or building websites and that’s about all my knowledge of tech jobs. One day I was on Reddit looking for curated content for clients. There was actually this one meme I found: with these south Asian people holding a knife about to cut a cake. The cake was labeled something on the lines of “real tech professions” or something, and everyone was labeled as a tech or math-related job that I don’t remember. I think one of them was machine learning engineer. There was one to the very side of the cake labeled “Data Scientist”, but the joke was he wasn’t holding the knife. His arm was stretched out but if you zoom in he wasn’t holding it at all. Concluding that data scientists aren’t real tech jobs.
And that, my friends, is when I decided to look into data science.
From a meme I found on Reddit.
And I’m really mad right now because I cannot find that meme.
But it created something to this effect:
Learning more about what data science is
Once I learned more about data science, I learned it’s practicalities. From predicting the next TV series you should binge-watch based on your history, to predicting how many fewer COVID cases we may have once the U.S government stops embarrassing us, there was a lot that data science could do for me. I even imagined how I could eventually use data science for social media and my health clients. One of the very, very early projects that I never finished was generating a list of people that I followed who didn’t follow me back. It may sound silly to you, but a few of my clients care about their follow to following ratio. But I had ideas of taking it a step further. Who’s more likely to follow back? What factors contribute to that decision?
Data science by its basic definition (which you will find thousands of them, by the way) sounds a bit abstract, but once I learned how data science is used every day and how even I can use it in my work and for the greater good (health), I was hooked.
Of course, I learned, if you don’t really enjoy something, you’re not going to put much effort into learning more about getting better at it. And in the beginning, I did not enjoy learning about data science or coding. Not because it was complex and the subject matter was confusing. But it was because nearly every tutorial that wasn’t by a South Asian man was narrated by an incredibly boring white man.
Dang where’s the twang? Where are the jokes? Where’s the fun?
Not to mention, there were so many buzz words and so many weird ways to explain simple things. A data frame is really just a data table. Instantiating is to create. Big Data is a scary term that just means a really large data set. For loops are the devil.
I would look up terms and would be even more befuddled after reading the definition.
Maybe because I don’t have a STEM background? I don’t know, but from prior research with Real IMPACT, I know that there are many, many, many social barriers barring women from tapping into the goldmine of financial stability that is a career in tech. And it wouldn’t surprise me if vernacular and communication is one of them.
After all, the tech industry is heavily populated by men. You know what’s not populated by men? Jobs in communication.
But the thing is, even though the tech field is overly saturated with people who don’t look like me, I will say there’s a bit more diversity in certain sectors of tech. For instance, I actually know a few black female software engineers. There are many black and female web developers too on Youtube and Twitter. I even found some black friends in the cybersecurity sector. But when it came to data science? Zilch. None.
I know that data scientist is a more “new” field, so that could have something to do with it. That and the fact that every day there becomes a new job title for it. While I continued to see what data science can do for my life and what I could do for data science, I finally accepted the challenge and started choosing what everyone said I should choose: a programming language. I chose Python because Reddit said it was the easiest.
WHY NO BOOTCAMP?
I know what you’re probably thinking — “are you just going to learn everything from Reddit?”
“You’re so young — why not get your master’s in DS?”
“Why not do a boot camp?”
Simple:
1. No, I’m not going to learn just from Reddit. I’m going to learn from Reddit, YouTube, Github, Kaggle, and Twitter.
2. Money.
3. See 2.
I’m not totally against the idea of going back to school, but a) it wouldn’t be for data science. It would most likely be for statistics or computer science. There are a lot more resources, I believe, to help excel in those choices.
And because of my bachelor’s in Public Health, I took a Biostatistics class. And honestly, I enjoyed it. I liked the idea of having a data set, running tests on them, measuring things like risks, calculating z-scores, and using our results to influence medical and public health decisions. :)
And b) I can’t pay for school right now, nor do I want to deal with the stress. Like I said, I own a business that’s still in its first year of full bloom. Growing it and my income is my main priority right now, and I don’t have the mindset to go back to school yet.
So after watching so many YouTube videos analyzing if boot camps are worth it and reading many people’s journeys into data science, I decided to take the plunge.
I decided to self-study.
And on the ninth(?) month, I decided to start documenting it. And here we are now 😊
The Self-Study Journey:
Because data science is an interdisciplinary field (big word for ‘multiple jobs in one’) of programming, machine learning, and “domain knowledge” (how much you know about your industry), I had to learn to code.
I used Lynda.com (LinkedIn Learning) because it’s free for people who have a library card in my county, and probably yours too. I found an intro to Python course that did its job, kinda. Still a very boring old man explaining the basics, but I stuck through it for maybe half of it.
I started learning to code at around mid-fall of 2019. I remember this because one project we did in the Lynda course involved me spending hours coding a countdown timer to Thanksgiving.
Oh, and if you’re also a self-learner, you probably won’t finish all the courses you take online. I have yet to completely finish one course. It’s just the self-learning culture.
And, some courses aren’t really that great. For instance, there weren’t really any practice problems for my course. It was just this man talking at me and I was writing down what I was seeing. So instead, I looked for practice problems online whenever I dived into a new topic. A few places I went to practice were:
1,500+ Python Practice Challenges // Edabit
Python Exercises, Practice, Solution — w3resource
I took and take notes with OneNote. I disabled the spellcheck red line because it was infuriating me whenever I wanted to name a variable.
And then I just created a new tab and wrote what I learned that lesson.
Not gonna lie, I don’t retain things as well when I don’t write them down. But it’s easier for me to look up things like the proper parameters to use and things of that sort when I can just Ctrl + F and find it.
Besides, from what I’ve heard from others, programming is 98% copying and pasting.
I bought a notebook, though. I want to introduce a hybrid way of taking notes.
I would spend an hour a day coding. I started coding in the night, but then my eyes and my brain started hurting, so I switched to 6 o'clock in the morning before gym. While I was coding, I was slowly making my social media more about coding. I started following coding pages on Instagram. I even created a whole new Twitter (@blossombuilds) just for tech.
Learning to code got lonely quickly. Social media helped, but I wanted to expand my network even larger. I started going to meetup events. There was one in Atlanta for Women in Data.
It was the first networking event that didn’t make me want to hurl. I had meaningful conversations with people, didn’t feel awkward, and still talk to people I met there!
I found the hashtag #100daysofcode so I committed to that until I stopped. I think I stopped at day 16. It wasn’t because I stopped coding. I just stopped writing about it.
But I remember there was a time when I just stopped coding altogether. I was having problems with my family and my finances and had to put it on hold.
When I was able to regain focus, I continued to share my python work with my Twitter and LinkedIn family. I added the lady I met from the Women in Data event on LinkedIn and spoke to her on occasions too.
She actually worked at the CDC, which, if you live in Georgia, is the goalpost for everyone that graduated with a public health degree. Basically, if you don’t work there, your public health degree is a waste (society’s words, not mine).
Not like America cares about public health anyways, as you can see from the current pandemic, but that’s another story.
I talked to her about what she wanted to do, and she wanted to use data science for health care, kinda like me.
We talked a bit more on LinkedIn, and I told her about my data science journey and my experience with coding. I was following the #100daysofcode hashtag and learning the fundamentals, but eventually, I was getting restless. I was making password guessers and countdown timers, but when was I going to get into the nitty-gritty of data science? I’m glad I met that woman because it was a chance for me to pair up my self-learning syllabus to her own. She agreed that I should be focusing on studying python but more so for data science and not just for generic programming. And honestly, I was elated.
Buh-bye, boring Lynda class!
So I focused on doing just that. I think this was around New Year’s time because I bought my first data science course during their Udemy New Year’s special.
Which, by the way, is a complete hoax: apparently, they have specials every day if you go on Incognito mode.
I actually went on Reddit again to see which is the best course for data science and python. I bought 2 courses for $11.99 each.
Now, in the beginning, I definitely wasn’t consistent. To give you perspective, I bought the courses in January and it is now July 2020 and I’m still not done.
It’s no rush for me, though, because these concepts were challenging. The Intro to Python Lynda course I took before contained many videos at maybe 5 minutes long max. Now I was completing videos at a minimum of 15 minutes! Definitely a bigger learning curve. But I will say, the python class really helped me understand the data science course.
The data science course, if you’re wondering, is here: https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/
It’s by Jose Portilla. He is a great teacher, don’t get me wrong. But I found myself nodding off to the sound of his calm voice multiple times. Maybe because it was too early? Maybe because he was boring? Maybe both?
I would give it a 3 out of 5 stars because while it’s a lot of content, it doesn’t really dive deep. There were many parts where we did projects, but he didn’t even interpret his results. And I think the interpretation is important. Multiple times, I just had to google how the heck do I read a confusion matrix and how do I know if my model is any good, because he didn’t explain it.
But who knows, maybe a hidden objective was to learn how to google. Because that’s exactly what I did.
So yes, from January till like May, I was coding and going through my data science coursework. I’d share my findings on twitter with the same #100daysofCode until I abandoned it per usual.
Side note: Do any of you all really make it to 100 days of coding? I code (or read or watch something related to data science)nearly every day sans weekends, but writing about it on Twitter every day is what stumps me.
What happened after May, though? Well, we were weathering the storms of a pandemic at this time and my discipline and motivation took a really big hit. It was hard for me to wake up and code at 6 AM. It was hard for me to wake up that early in a pandemic. I had to adjust my schedule and mindset quickly or my self-learning journey would be in jeopardy.
In my next blog post, I’ll talk about seeking help when self-teaching data science, new data organizations I joined, and how I networked myself into a black and female data scientist mentorship (and friendship).
Thank you all for joining me on this journey! Please tell me in the comments, how did you learn data science? Do you have tips for self-learners?
