Data Science Learning Journey – Month #2

About five weeks ago, I published my first (month #1) update on my latest journey: Data Science.I’m back for another round—with a whole lot more learning.

My first month was focused on learning SQL—a language that several people had insisted was “table stakes” for getting into Data Science or any kind of analytics or insights role in today’s business landscape.

I totally believe that and started with SQL, using several online tools for learning queries and then testing myself in real-time.

As I’ve undergone this learning journey, I’ve looked as deeply as grad school programs to enhance my skillset. One that’s very popular for Analytics (really synonymous in a lot of ways with Data Science) is Georgia Tech’s OMSA program. I won’t go into that much but that program offers what’s called a “MicroMasters” through EdX where you can take up to three (3) real courses in the program. I’m doing so now.

EdX gives you the option to simply audit the class and get the materials (free) or take it as a student with homework, midterms, and everything. I’m doing the latter and we’ve just started Week 5.

While I’ve still been practicing my SQL skills (and utilizing them on data for my day-to-day job which has been an incredible plus to put learning into action), the course I’m taking, Introduction to Analytics Modeling, has taken up most of my Learning time.

Some notes from the course experience so far:

  • The learning curve was very steep in the first few weeks, for two reasons
    • One – the course is short and dives into content right away, which means utilizing math and stats concepts I hadn’t encountered since senior year of high school really and starting to put them into practice
    • Two – learning R, the programming language. Much like SQL, this is widely used in analytics and also like SQL, I didn’t know a lick of it before starting to learn. So I had to learn R, which I did through a combination of DataCamp (a very positive learning experience, and another place to practice SQL) and LinkedIn Learning (which is also tremendous but I work there so I’m biased, also this helped a lot with math concepts like p-value and exponential smoothing – concepts which, if they sound foreign, did for me as well just a few weeks ago but now I’m using them)

The combination of these meant that the first few weeks were a deep onboarding—my brain full of new notes and R language functions. I’m feeling much more in control now though we’ll see how midterms go in a few weeks—the rumor is that they can be quite ambiguous and somewhat heady.

Even feeling more confident the course is among the harder learning initiatives I’ve taken on. On the forums for the class it seems like some are in my boat (especially those, like me, coming from a non-STEM background) and easier for others. Perhaps the best result from this is a real idea of where I want to continue my journey—if I decide to go to grad school then this is the best vision of what that will be like (and it can count as credit in the GA tech program). So we’ll see — I’ll come out of this knowing about regression and clustering and ideas on representing data and understanding concepts like outliers, but I think I’ll go back to working on my SQL skills for some time after.

Who knows, I may even take a month off to get back to writing fiction 🙂

Lastly, as a plus, if you’d like you can watch and listen to me on my friend Davidson’s podcast— talking  about my delight for learning and more.