Q&A with Beginner Python & Math for Data Science instructor, Gordon Dri

This blog post has been updated and was originally authored by Jerod Rubalcava, Metis.

 

Gordon Dri’s a Data Scientist at Oracle and the co-designer/co-instructor of the Metis live online Beginner Python & Math for Data Science. In this blog post, Gordon is interviewed by Jerod Rubalcava, Executive Director of Professional Development at Metis.

 

 

When I created the concept for the live online Beginner Python & Math for Data Science short course, I wanted the focus to be on true beginners. Data science is intimidating, and it can be hard to know where to begin your journey, so that’s exactly why this course was built.

Over the last few years, ‘data scientist’ has been recognised as one of the best and most popular jobs in the market. However, the gap from beginner to professional is large. We need to focus on the fundamentals to make people excited about data science, while also showing them there’s no reason to be intimidated. As experts and educators within the industry, we need to make bridging the 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 at Oracle) and Robert Reif (Senior Data Scientist at Metis) came into play. They’re both exceptional data scientists who’ve worked in academia for years. We worked diligently as a team to create a course that anyone can take no matter where they’re located, with no worries about limitations on their experience or hefty prerequisites to hold them back. Everything about this short 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.

 

Interested in starting your journey in data science, but have no idea where to begin? Perhaps you don’t really know what data science is and want to learn more?

The Beginner Python and Math for Data Science course is for you.

This Q&A I conducted with Gordon Driexplores the development of the Beginner Python and Math for Data Science 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 in a new language. When you first decide to pick up a new language, the most common first step is to learn the alphabet. This becomes the foundation for words, which leads to the sentence, which leads to paragraphs and conversations.”

 

What makes you interested and excited about teaching a course like this? What led to its development?

There are two main things that excite me about teaching a short course like this. Firstly, the course is tailored to complete beginners and is designed in a way to cater to this specific audience. This means there are absolutely no barriers to someone enrolling in the short course. This motivates me because it opens the door to people who typically may get turned away or intimidated by more advanced courses. I’m always an advocate for inclusivity rather than exclusivity. Secondly, the course teaches the underlying programming and mathematical principles that serve all data science endeavours. 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’s happening and why it’s necessary. The prospect of students learning from first principles excites me because I truly think it’s necessary to become successful in this field.

Both ideas above are what led to the development of the course. We saw a need to provide training to absolute beginners, plus we saw too many students jumping to more advanced topics without having an understanding and appreciation for the basics.

 

If someone asked you about the short course, what highlights would you identify? 

I’d tell them this course is the equivalent of learning the alphabet in a new language. When you first decide to pick up a new language, the most common first step is to learn the alphabet. This becomes the foundation for words, which leads to the sentence, which leads to paragraphs and conversations. This course teaches the ‘data science alphabet’, so 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 briefly mentioned why this short course is good for beginners. Can you go into more detail? 

In my opinion, all data science journeys should start with first principles. An individual who has never programmed or taken advanced mathematics can’t enter the data science field at the same level as someone with graduate degrees … and they shouldn’t try to. Unfortunately, the hype around data science is encouraging people to fast-track their path to becoming data scientists by skipping the foundation material, which I believe sets them up for failure. To be successful in data science, you must deeply understand the basics, including how to program efficiently and explain principles in mathematics.

At Metis, we believe this course isn’t just the best way to start your data science journey; it’s the only way.

 

How did you get started on your own data science journey?

 I first became interested in data science when interning at a commercial real estate company during university. I was tasked with the very open-ended request to analyse the building’s data; including electricity, water consumption, building performance, etc.. I then had to report back on where we were doing well and what we could improve. At the time, I didn’t have many tools to perform this analysis, but the more I researched and learned, the more 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 skill set. It was at this time I decided to pursue the field more formally through a masters degree. I recently completed my Master of Science in Analytics from the University of Chicago. Although I’d say I’m well on my way through my own data science journey, I always look to raise the bar for myself and learn more as the field continues to change.

 

Whose work inspires you? 

I’m 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 the last three Sloan conferences (which is very impressive given the number of research papers competing each year). Patrick sat on a panel and was joined by Sam Hinkie (ex-General Manager of the Philadelphia 76ers) and others, where 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. People post interesting articles and 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 the articles or find a new way to apply a topic I learned in the past.

 

Looking for a data science short course for absolute beginners? Beginner Python and Math for Data Science offers an introduction to the basic building blocks essential to developing data science skills in a live online format. It’s also suitable for those who need a refresher with the fundamental Python programming or mathematical concepts necessary for career progression.