Gordon Dri is a Data Scientist at Oracle and the co-designer/co-instructor of our upcoming Live Online Beginner Python & Math for Data Science part-time professional development course, which starts April 23rd and runs through May 31st on Monday and Thursday evenings from 6;30 - 9:30pm PDT. Check out an on-demand sample class with Gordon (and co-instructor Roberto Reif) here. As for me, I'm Jerod Rubalcava, Executive Director of Professional Development at Metis. Thanks for reading!
When I created the concept for our Live Online Beginner Python & Math for Data Science course, I wanted the focus to be on true beginners. Data science is intimidating, and it can be hard to know where to begin one's journey – so that's exactly why we built this course!
For the past few years, Data Scientist has been recognized as one of the best and most popular jobs on the market, but the gap from beginner to professional is too large. We need to focus on the fundamentals in order to get people excited about data science, but also – and just as importantly – to show them there is no reason to be intimidated. As experts and educators within the industry, we need to make bridging that gap accessible, fun, and realistic.
As I began thinking about the team for the course design, I knew we’d need actual data science practitioners with experience as educators. I needed to ensure they'd be able to break down the foundational concepts into easily digestible ideas. That’s where course designers and instructors Gordon Dri (Data Scientist, Oracle) and Robert Reif (Sr. Data Scientist, Metis) came into play. They’re both exceptional data scientists who have worked in academia for years. We worked diligently as a team to create a course that anyone, anywhere can take with no worries about limitations on experience or hefty prerequisites to hold them back. Everything about this course – from curriculum and structure to teaching methodologies and learning objectives – is meant to be accessible for beginners, who we had in mind every step of the way.
If you are interested in starting your journey in data science, but have no idea where to begin – or perhaps you don't even know what data science really is but you want to learn more – then this course is for you.
Below, please enjoy a Q&A I conducted with Gordon Dri, who talks about the development of the course, why he's excited to teach it, and why he believes it represents the only true way to learn data science.
"...this course is the equivalent of learning the alphabet of a new language.When you first decide to pick up a new language, the most common first step is to learn the alphabet, which becomes the foundation for words, which leads to the sentence, which leads to paragraphs and conversations."
What makes you interested in and/or excited about teaching a course like this? What led to its development?
There are two main things that get me excited about teaching a course like this: 1) The course is tailored to complete beginners and is designed in a way to cater to that audience. This means that there are absolutely no barriers to someone enrolling in this course. This excites me because it opens the door to people who typically may get turned away or intimidated by more advanced courses, and I am always an advocate for inclusivity rather exclusivity. 2) The course teaches the underlying programming and mathematical principles that serve all data science endeavors. Unfortunately, all too often I see students trying to get into data science the "quick and easy" way, which is to go right to a Python script and blindly and aimlessly run a function with absolutely no appreciation for what is happening and why it is necessary. The prospect of students learning from first principles excites me because I truly think it is necessary to become successful in this field.
Both ideas above me are what led to the development of the course - firstly, we saw a need to provide training to absolute beginners and secondly, we saw too many students jumping to more advanced topics without having an understanding and appreciation for the basics.
The Metis website describes the course in detail, but if you ran into someone asking about the course, what highlights would you note off the top of your head?
I would tell them that this course is the equivalent of learning the alphabet of a new language. When you first decide to pick up a new language, the most common first step is to learn the alphabet, which becomes the foundation for words, which leads to the sentence, which leads to paragraphs and conversations. This course teaches the "Data Science Alphabet" so that students can continue to form the concepts into words, sentences, and eventually conversations. In data science, the alphabet consists of beginner Python programming principles and basic mathematics principles such as linear algebra, calculus, probability, and statistics.
You mentioned a bit about why this course is good for beginners, but can you go into a bit more detail on that?
In my opinion, all data science journeys should start with first principles. An individual who has never programmed or taken advanced mathematics cannot enter the field of data science at the same level as someone with graduate degrees, and they should not try to. Unfortunately, the hype around data science is encouraging people to "fast-track" their path to becoming data scientists by skipping all the foundation material, and I think they are setting themselves up for failure. In order to be successful in data science, you must deeply understand the basics including how to program efficiently and explain principles in mathematics.
As a testament to this philosophy, the Deep Learning book by Goodfellow, Bengio, and Courville, which has become a staple in the data scientist's toolkit, allocates almost 100 pages to basics of calculus, linear algebra, probability, and information theory. Even before they hit the machine learning topics on page 100, they have gone through the basics, which represents their sentiment toward learning this material. We, at Metis, believe this course is not just the best way to start your data science journey, but the only way.
How did you get started on your own data science journey?
I first became interested in data science interning at a commercial real estate company during university. I was tasked with a very open-ended request: analyze the building's data (electricity, water consumption, building performance, etc.) and report back on where we are doing well and what we should improve. At the time, I did not have many tools to perform this analysis, but the more I researched and learned, I became increasingly excited at the possibilities of using computer science and mathematics to interpret data to find meaning. After finishing the internship, I knew I wanted to focus my career on this type of work, but I also knew I had to fill the gaps in my skillset. It was at this time when I decided to pursue the field more formally through a master's degree. I recently completed my Masters of Science in Analytics from the University of Chicago, and I would say I am well on my way through my own data science journey, but I'm always looking to raise the bar for myself and learn more as the field continues to change.
Whose work inspires yours?
I am a huge fan of Sports Analytics and follow the industry closely after attending MIT Sloan's Sports Analytics Conference the last few years. Specifically, I'm a fan of Patrick Lucey (Director of Data Science at STATS) and follow his work very closely. He previously worked for Disney Research and has presented at least one paper at each of the last 3 Sloan conferences (which is very impressive given the number of research papers competing each year).This year, he was asked to sit on a panel where he was joined by Sam Hinkie (Ex-GM of the Philadelphia 76ers) and others, and they talked about artificial intelligence and its impact on sports and the world at large. I recently had the chance to meet him and some of his team members at STATS' new downtown Chicago office.
How do you stay up-to-date in the quickly evolving field?
I constantly read Medium's "Towards Data Science" section, partly because people post interesting articles, but more importantly, they always provide step-by-step instruction of their projects as well as sometimes sharing their source code. I can always be sure to learn a new topic while reading through the articles or learning a new way to apply a topic I learned in the past. I also became active in the research community at the University of Chicago and read work by my professors and other colleagues at the university on ResearchGate.
Thank you again for reading, and we look forward to seeing you in one of our upcoming courses!
Watch an on-demand sample class taught by Gordon Dri and Roberto Reif here – and if interested, you can learn more and enroll in their upcoming Live Online Beginner Python & Math for Data Science course, which starts April 23rd.