Interview with Coding Dojo Data Science Plus+ Instructor Isaac Faber

Data is the driver of the digital landscape. Metrics and statistics have never been easier to obtain and have significantly decreased a good part of the guesswork in business decision-making. Data is a necessary element of web development, but its behind-the-curtain nature–as well as the massive amount of data available–can intimidate the rookie coder. That’s why data analysis is such an in-demand and potential lucrative field.

Isaac Faber has spent a decade developing products that make data accessible and powerful for businesses and the public sector. As a data scientist and professor, Isaac is heavily involved in developing groundbreaking products and applications that maximize digital intelligence, helping users access and interpret data to make better decisions.

Now Isaac is sharing his expert knowledge with the Coding Dojo bootcamp, helping to make data science accessible, understandable, and maybe even a little addictive for students. We spoke with Isaac about his experience, and the dynamic he brings to Coding Dojo as a data science instructor.

Can you tell us how you got into data science?

Isaac: I was in a master’s program at the University of Washington, was working on my thesis about stock options. At the time the only tool that I had to do data analysis was Excel. Someone suggested to me, “Hey, you should try this new statistical language called R,” and I tried it. It was amazing how much faster and better it was. I was hooked, and so from there things sort of took off. 

I served as the lead data scientist at the army cyberwarfare unit called ARCYBER. The last few years I was working on my PhD at Stanford. I helped co-found a data science startup, MatrixDS, a pretty fantastic way to get people immersed in using real tools. I’ve programmed in different languages, primarily R and Python, with a bunch of experience across lots of different projects from national security to business applications.

What makes data science so powerful and makes you excited about teaching it?

Isaac: Statistics was borne out of the problem of not having enough data. A lot of the early questions around statistics were about using as little resources as possible, or collecting as little data as possible. Data science flips that on its head. 

Now I’ve got a huge amount of information at my disposal. We look at the data first and then we form hypotheses, and that’s a fundamental paradigm shift. We’ve been able to do more and more applications and automate more processes. We’re on the edge of building systems competitive with human cognitive thought in many areas. With the confluence of these resources, now is the time to get into the discipline.

Do you have any examples of everyday things where you’re seeing data science impacting our lives and the way we use technology, and how that’s impacting the growing field?

Isaac: Almost every product–certainly every tech product–that you interface with on has some data science component built into it. Text completion on Gmail, scheduling a meeting on your calendar application, Alexa and Siri–all of those things are the result of data science processes. Imagine the Uber and Lyfts of the world. When you’re hailing your ride, they probably run several dozen machine-learning applications during that process. And you won’t even see it. It all happens behind the scenes, but you get to benefit.

I run across many problems where I think to myself, “Man, this is a data science product waiting to happen.” Like, traffic or water usage in your house –if you were collecting data on them, you could potentially make much better decisions than you do in the low-information environments that we have right now. 

How does that connect to how this course works?

Isaac: The purpose of this course is to get people started. You need a solid foundation that you can build on. We picked Python because right now it’s very popular in industry, and if you learn it you can expand skills out to different development circles. You’ll understand how the data science process works, how machine learning works, how you might build it into applications to answer specific questions. You’ll have this complete breadth of knowledge by the time you finish the course.

What would you say is different about our bootcamp approach with this course, compared with traditional universities or online courses?

Isaac: The big thing that makes us different is our multi-pronged approach. You’re going to be reading and watching online videos, and we do live lectures. We also do one-on-one sessions at least once in the course between me and each of the students. So you’re going to get a lot of hands-on learning. You’ll have access to me throughout the course, via messenger application.

By the time you’re done you’ll have all the work that you’ve completed, so you can use it as a reference later on to launch into your data science career or deeper learning after the course. That’s significantly different. You’re going to start scripting literally the first night of the course. You’ll be using the real tools that you will use in real life, writing code and solving problems related to things that you will run into in the real world.

What do you enjoy about teaching?

Isaac: Those types of interactions are my favorite part. One of the neat things about watching an industry grow is spotting people who would be good fits for the industry, who have the out-of-the-box dimension. The most rewarding thing is watching folks I know are going to do really well in the industry have those light bulbs go off when they see the type of problems that they can solve.

If you’re interested in getting a modern and comprehensive introduction to data science and machine learning in Python, you can learn more about our Data Science Plus+ program or get in touch with a Coding Dojo representative today. If you’re interested in watching the full interview with Isaac, please watch the video below

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