Colleen Spiegel has an extensive background in engineering and data science, and is now the Head of Curriculum Development at K2 Data Science’s online data science bootcamp. Colleen joined the team because she was excited to help people advance themselves and change careers, without quitting their jobs. Colleen explains how the K2 Data Science curriculum is going to keep students engaged online, and why they chose to teach Python and R.
Tell us about your background and experience as a data scientist before joining K2.
Having worked in this industry for many years, I’ve done a lot of mathematical modeling, statistics, and programming on various projects. I’ve worked for myself as a freelancer and a consultant, and also in both pharmaceuticals and semiconductors.
What’s your role at K2? How are you helping to teach data science to beginners?
I’m the Head of Curriculum Development. Right now we’re in the process of developing the curriculum for K2. Curriculum-writing is not new to me; I’ve authored two textbooks published by McGraw-Hill and Elsevier Science. The K2 team is working together to develop the best possible data science curriculum. We offer a self-paced model, so the curriculum we are developing is in smaller blocks. Unlike other data science bootcamps, K2 doesn’t require a full-time commitment from students, so you can stay in your city and even keep your full-time job. We’re extremely excited about the program.
How did you transition into data science from engineering in your career?
In my case it was learning the new technology as I went. I’ve worked as a traditional engineer, but part of being an engineer is data collection and data analysis. I’ve done a lot of mathematical modeling, and not only model development from a scientific viewpoint, but also regression, and predictive modeling. For example, in the pharmaceutical industry a drug is often developed at one location, and you want to start manufacturing it in another facility, and so you have to prove to the FDA that this drug has the same properties, statistically speaking.
Throughout my career I’ve used Matlab, Visual Basic, statistical software like Minitab and Sass Jump, depending on what I was working on. More recently I’ve been able to work on some freelance projects using Python and R. R is a lot like Matlab, and Python is a very good universal language that has tremendous application and it’s one of the top languages used by data scientists in the industry.
With your background in engineering and your formal education, did you need to be convinced of the effectiveness of on online data science bootcamp?
No. Since I first started working as an engineer in industry in the late 90s, the landscape has really changed. Back then, an advanced degree (a master’s degree, or Ph.D.) was enough to not only get you in the door, but also get you pretty far in your career.
Today, employers want practical experience. So a bootcamp makes a lot of sense for Ph.D.s and master’s grads directly out of school. They start learning from ground zero when they enter the industry, so the bootcamp gives you a lot of practical experiences, and immerses you in a project-based environment rather than taking exams. A bootcamp allows you develop a portfolio, and that’s what employers are interested in. They want somebody who can not only program, but who also understands how to make good decisions based upon data.
What made you excited to work at K2 Data Science in particular? What are they doing that’s different from other data science bootcamps?
Besides how nice the folks are there, what they’re offering is an experience suitable for working professionals, and to me that meant a lot because I did all of my graduate school while I worked in industry full time. So I understand what it’s like to have a full-time job, have family responsibilities, but still want to further advance yourself. If you look at the landscape of the data science bootcamp industry, it just doesn’t seem to allow for that right now. It requires a full-time commitment, and if you’re an adult with a family, the last thing you want to do is quit your full-time job and do a bootcamp for 12 weeks or longer. That really appealed to me.
You mentioned you have experience in writing curriculum and teaching. What will be different about teaching at an online data science bootcamp, compared to teaching in person?
With an online bootcamp, you have to make sure what you’re presenting really connects with people because it’s too easy for people to not want to go to class because of other responsibilities in an online environment, especially a part-time environment. So K2 will have to continuously work to make sure the curriculum is the best, make sure our students are engaged, and that all our content is interactive. Our mentors are making sure our students are learning appropriately and properly, so they not only stay engaged but they get the outcome they desire, which is a new career.
When you designed the curriculum, how do you decide what to include in a data science bootcamp?
We looked at other universities and bootcamps, and developed a landscape of what current curriculums cover. We also looked at what employers are looking for, and along with our own practical and industry experience, we created a curriculum that combines theory and practical application. Theory without practice (and vice versa) is no good, especially for a bootcamp.
Tell us about that curriculum! What technologies do you cover at K2 Data Science and why?
Our main goal is to prepare students to become data scientists or data engineers, so our curriculum covers topics such as exploratory data analysis, regression analysis, classification methods, big data, ensemble methods, predictive learning, clustering, and a lot of machine learning in classical data science concepts. It combines the theoretical aspects with the programming aspect which is primarily going to be Python. However, there will be some work in R as well. We will continue to monitor the landscape to make sure our curriculum stays up to date, and continuously work with employers to get that feedback.
What’s the teaching style? How do you deliver data science lectures and lessons online?
The lectures are all going to be pre-recorded, which I think is great and it creates a unique learning experience. The format is primarily in PowerPoints, which have examples for students. Then in our portal there are also written lessons and coding examples. That will be combined with coding projects at the end of every unit.
Are these lessons something you might introduce in a lecture and then the students can go back and look at it afterwards?
Yes. The lectures will consist of slides so they will be going through every topic first in theoretical depth and then we go over specific real-world applications and examples. We then take those examples and show the actual code that’s used and the plots that would be generated from that code. It’s a very interactive and immersive environment.
Along with these individual lesson plans and coding examples, there is an interactive chat box for students which in the lower right-hand corner of the screen. You can go ahead and send messages there anytime during the course of the lecture or during the majority of the week excluding Friday evenings and weekends. So anytime a student needs assistance or has questions, they can use that and get answers very quickly. This portal also includes a calendar of events and complete assignment solutions and projects once it’s finished being created.
How and where do students complete assignments or projects?
They are going to work through Github, the repo, so they’ll be working on assignments offline, using Git to post code and share it, then upload it to this platform for instructors and classmates to review.
Where will you conduct lectures or lessons? Is it through this platform?
That will be recorded through a different platform and then be uploaded to this portal once they have been recorded.
How do students interact with each other?
We will be using Slack. There will be a channel for each cohort of students as well as a general channel for all students.
Will students be completing assignments in groups? Or individually?
There will be a combination of both individual and group assignments. It’s good to do both because working with others in industry is very important, so we want to incorporate collaboration and working with others. But individual projects are great too because they allow you to work by yourself, do a deep dive, and figure out how a certain concept works, and how it’s applied, and make your own mistakes.
On the learning platform is there a way for students to see their progress and how far they’ve got through the learning material?
We don’t have that yet, but I’m sure there is going to be a way of doing that. The online platform is very basic because the majority of the lectures from K2 are recordings. Interactive features will gradually be added over time, but it’s going to be very basic to begin with, and will primarily consist of lectures, projects, the curriculum, and examples. A lot of it will be the K2 instructors interacting with the students for knowledge sharing and development of each student’s knowledge base.
Is the job hunt integrated into the learning platform? What kind of career prep do you give students?
It won’t be on the online platform itself, but K2 is taking a lot of steps to prepare students for their career development. As part of the curriculum, K2 will hold workshops to help build a professional resume and online networks using LinkedIn and Github which will stand out to employers. We’re also going to have mock interviews with data scientists to help strengthen students’ communication skills and provide feedback.
Students will also have access to a database of technical interview questions and a job board. I’m sure those will be integrated into the portal over time. After students graduate they’ll join our Alumni Channel, so they can keep networking with recent graduates for advice.
How many students do you expect to be teaching at any one time?
Our goal for the initial classes is no more than 10 students. In the future we’d probably allow as many as 20, but we’re looking to have a small group learning environment, so we can pay a lot of individual attention to the students.
How many instructors, TAs, and/or mentors do you have? Is there an ideal student:teacher ratio?
Right now it looks like we’re going to have five total. So it’s a pretty good student to teacher ratio. We have two or three instructors, and at least three teaching assistants.
How many hours a week do you expect your students to commit to K2?
The lectures will be approximately nine hours a week. There’s going to be at least that much time again for student projects and studying. It would be great if students can commit more hours, but it is a part-time program, so we’re not going to expect any more from them than that.
In what time zone is the program taught? How will you cater for students in different time zones?
The self-paced model allows us to be timezone agnostic.
How do you assess student progress? Do you give assessments or tests at the bootcamp?
No, there are no tests in this bootcamp, it’s purely project based. Students learn the theory and programming by example and then have projects to work on. We’ll be looking at student projects from different industry viewpoints and providing lot of feedback on how to improve them. So the projects will be interactive. There’s not going to be a harsh rating system like a university.
Can you tell us about the ideal student for the bootcamp? What sort of background or qualifications would someone need to be successful at K2?
Our ideal candidate will have one to three years of work experience as a data analyst or software engineer, or some other technically related field. We are also interested in new graduates with a STEM-related master’s or Ph.D. degree
What’s the goal for a student that completes the bootcamp? What sort of jobs will they be prepared for?
It would be a number of different jobs – data scientist, data engineer, and some different variations from those.
For our readers who are beginners, what resources or online forums do you recommend for aspiring data scientists?
There is a lot out there on data science. Any general google search will lead to a myriad of results to help someone learn what data science is. I think it’s an evolving field, a new really exciting field. It is different from employer to employer.
KDnuggets is a website that aggregates major news articles about data mining, analytics, big data and data science.
Is there anything else that you want to make sure our readers know about the K2 online bootcamp?
Our first class starts on January 9th. My advice would be to get your application in because the cut off will be a couple of weeks before the January start date. I think it’s going to be a great bootcamp. It’s an online classroom with continuous engagement and project interaction throughout.