Teaching Data Science at General Assembly: Week 4

Grit. It’s how I wake up at 5:30 am every weekday and step into the weight room, swinging kettle bells and olympic lifting the sleepiness out of my eyes.

Grit is what it takes to learn the data science fundamentals in a 12 week course, and my students are finding that out very quickly .


Week 2–3

My instructional team and I have taught lessons that take several university courses on computer science, linear algebra, statistics, probability, and distilled their core concepts into lessons that were taught over 2 and a half weeks. Frequently shifting subjects from day to day can become a bit disorienting for students — educational whiplash, if you will. As students learn the low level fundamentals mentioned it can be a bit disorienting because they are learning seemingly isolated skills and technologies. It’s too early in the course for them to see and apply these skills and tech, synthesizing to create awesome data products and optimize business operations using machine learning and big data.

Patience young jedis, systems based solutions will come in time, for now you’ll be blind folded during your lightsaber training while deflecting blasts from the hovering round droid.

Those Web Development kids and their graffiti (shakes fist furiously in air).

Week 4

Week 4 and the tail end of week 3 introduced students to the wonderful world of machine learning. Yes, ML, that awesome field in computer science that let’s you tell your friends that you work with artificial intelligence — albeit narrow subset of A.I. For those of you that have taken an introductory machine learning course at traditional universities like Stanford or UCBerkeley, or learned ML at bootcamps like GA, you’ll know that ML is taught by first introducing Linear Regression and Logistic Regression. What you typically don’t get a traditional university is a small class size with multiple instructors that are committed to student success through individualize attention and regular feedback.

That’s were I come in! Yes, I have the joy of guiding GA’s next cohort of industry ready data scientist through machine learning and all of its many aspects. Next week — advance machine learning!


Industry Speakers

The instructional team and I reach out to industry data scientist with the goal of having them speak to students about their work. In week 3, we had a former GA student give a talk about how he was able to save his solar energy company tens of thousands of dollars using machine learning and cleaver merging of open source data sets.

Next week, a data scientist from MyFitnessPal will be speaking about his work around A/B testing for user behavior. These talks help students bridge the gap between what they are learning in class and how those skills and tech can be use to solve real world problems and create a huge impact for their future startup.


I tip my hat to my students, they are not only surviving the difficulty of the course but growing their skills and confidence rapidly. It’s amazing to see how far they have come in just 4 weeks. Battled-harden, industry ready data scientist are on the way!


About the Author

Alexander graduated from UC Berkeley with a B.A. in Physics and from Galvanize with a M.S in Data Science. He is passionate about applying data science to solve challenging problems in the health care, clean energy, and environmental spaces.