Week two has officially ended for the Galvanize data science cohort in Denver, and there is no better time to discuss takeaways from the week – as well as first impressions of the program. There was a flood of information and experiences thrown at us, so I write this in order to speak on the week’s events as well as organize the new “data” that has been entered into my brain.
General Schedule
Due to the length of the immersive program at Galvanize being only three months, the daily workload is quite demanding. A typical day begins at 9am with a mini quiz – a short challenge that is meant to test everyone on the previous day’s material. At 9:30, the students submit their solutions and a morning lecture is given (typically an hour to an hour and a half). After this lecture, the class breaks for the individual assignment/lunch, a span of typically three hours.
The individual assignments are constructed so that if one has a relatively solid understanding of the material, they should be capable of finishing the assignment with about an hour to spare for lunch. That being said, we are constantly reminded that it is not required of anyone to finish the assignment. In contrast to an assignment completed sloppily, the instructors would rather a portion of the assignment be completed with quality work.
After lunch, there is another lecture, often ranging from one and a half to two and a half hours. This often happens due to the concepts being increasingly difficult compared to the first half of the day, and more time must be devoted to their explanation.
The longest portion of each day – typically three to four hours – is the pair programming assignment. Here we are encouraged to cooperate with one of our fellow students (a new person each day) in order to solve complex problems related to that day’s topics. The instructors often stress the importance of these exercises, as they prepare us for working in a team setting. If you’re interested, you can find a focus on pair programming in my previous post.
The Actual Material
The influx of material each day is large, and there is no doubt the curriculum is meant to push the limits of our mental capacity. The first day of class, we learned everything about SQL that we would ever have to use in a practical sense – joins, temporary tables, pivoting data, and data wrangling.
Two days later, the day was dedicated to working in NumPy , pandas, and SciPy, followed by matplotlib and Seaborn the next. These are all packages that can be used with the Python programming language for data analysis and visualiztion. It was clear to see why the required pre-course work was so extensive, as one would undoubtedly be lost without some familiarity.
The second week, focus was shifted to probability and statistics. The pre-course work had contained a few examples of coding and statistics merging together, but this week served as an introduction to the sheer magnitude of things you can do. I’ve barely started, but I’m gradually becoming more aware of the capabilities of data science’s multidisciplinary nature.
Though the flood of information is intense, I have been able to keep up quite well. While that does require a significant amount of personal study, I also believe a large part is thanks to the structure of course. The concepts are explained concisely, and about 5-6 hours everyday is dedicated to putting those concepts into practice. This has been fundamental in solidifying concepts for the students, and I am grateful for the amount of time that can be focused on implementing what is taught. Moreover, if there is any confusion, the instructors are constantly in the same room (sometimes the same table), and can explain concepts individually if need be.
Extra Endeavors
I’ve also individually started a nanodegree program on Udacity for machine learning. Machine learning is built on the foundation of coding and statistics that fills the first few weeks of curriculum. The nanodegree program was co-created by Google and Kaggle (a platform where companies post their data and people compete to produce the best model). Given that one works for 10 hours a week, Udacity expects the program to take 42 weeks to complete. My goal is to complete the material in two months, so the intensity has definitely been increased. But, if I’m going to be drowning in data science for the next three months anyways, I might as well dive in!
My main motivation for taking the course was to get a head start on machine learning concepts, as well as get a nice qualification under my belt. The program consists of five projects and one capstone project. Since Galvanize has a capstone project as well, I plan to finish the nanodegree material and create an amazing capstone project, which I will use for both Galvanize and Udacity.
I have seen some capstone projects from previous cohorts, and I can’t help but think to myself “Am I really going to be able to do those things at the end of the program?” As time continues, my excitement for the future exponentially grows. I anticipate the day where I will have a project of my own to present.
In this past week, I have worked on assignments with a former drumming instructor/performer and an ex-entrepreneur who had attempted to innovate LED technology. I am honored to be surrounded by fellow students and instructors from whom I can learn so much, and appreciate the opportunity to be a part of such a community.
Thus far, Galvanize has been an eye-opening introduction to the world of data. I have become more aware of the labor and skill behind initiatives that utilize data science to better humanity. I look forward to what next week will bring and will continue learning so that I may one day be capable of the same feats.
Galvanized Into Data 