It was very interesting to note that several students thought of big data as a viable career option - Mentor experience by Lavanya Marla

Lavanya Marla Assistant Professor, Industrial and Enterprise Systems Engineering University of Illinois at Urbana-Champaign

The data science workshop was a very exciting platform to explore the interests and exposure of middle and high schoolers in 'big data'. It was very interesting to note that several students thought of big data as a viable career option, and were keen to learn more about what fields big data could be related to. There was considerable eagerness to learn the underlying mathematical concepts and a lot of discussion as the students thought about how problems could be solved. Students were slow to open up and discuss, but once they did, the workshop had a lively atmosphere. One or two of the students had significantly more knowledge to statistical methods, but the other students matched them in eagerness to learn. The students who came with little to no knowledge also expressed that they learned a lot from the workshop and especially in a manner that the concepts were intuitive. The grad student instructors Naren, Colin and Raghav did an exceptional job of teaching, managing and conveying excitement about the subject during the four hours of the workshop. Multiple future sessions will be needed for a more detailed study of foundational concepts, but the success of this workshop gives me a lot of encouragement to build future workshops.

Data Science For Kids Goes International

We successfully organised our first international data science workshop for kids at the University of Illinois as a part of SAIL, a one-day event to learn more about life on campus by attending classes taught by current students. 

The workshop aimed towards introducing the idea of machine learning and data-driven techniques to middle-to-high-school kids. Participants went through a fun exercise to understand the complete data science pipeline starting from problem formulation to prediction and analysis.
 

                                                                

                         
Special mention and thanks to the mentors, Narender Gupta, Colin Graber and Raghav Batta, students at the university who helped us execute the academic and peripheral logistics of the workshop efficiently and making the experience engaging and interesting for the attendees.

                                                 Narender Gupta                                                                    Colin Graber                                                                         Raghav Batta

To read the mentor experiences click here.
Visit sail.cs.illinois.edu for more information on the event or workshop.

I was amazed to see such curiosity and intuition among high-school students - Mentor Experience by Narender Gupta (Illinois Camp)

The event was good, I liked it for multiple reasons. We talked about ideas that people have not yet been exposed to before they go to college and even then a lot of them were interested. Also, the turnout of roughly 20 students, which was almost double than expected, is a good sign.
Some of the students demanded more features and some who were a step ahead wanted to try combination of features. We would have liked a concrete web source to give out to students after the workshop where they could have access to data and basic machine learning concepts and models. It could also have a compiled list of related website links which could help students get a better understanding of ML and data science. Basically giving the curious students an easy access to this information to follow up with. Overall, it was a really good learning experience for me and I wasn’t expecting students to know as much as they actually did. When I talked about the John Snow cholera problem, some of them actually came up with the idea of putting the data on the map in a geo location manner and visualize the problem, which was really great. In fact, one of them came up with a question on overfitting and underfitting, which totally amazed me. I was surprised that they were even aware of these terms and could think in that tangent.

I discussed how we were analysing the data of just 20 people and facebook needs to analyse data of billions of people. It was amazing to see that they could appreciate the scale of the problem and the need of machine learning to solve it. It was a really humbling experience and would like to be a part of such endeavours in the future.

Without realizing it, and they understood all of it! - Mentor Experience by Colin Graber (Illinois Camp)

Running the class was a blast! Much enthusiasm for the topic was present, indicated by the fact that we had more than double the turnout we expected. The students were engaged deeply in the topic; not only did we not have problems getting people to volunteer answers, but we also answered questions on more advanced topics ranging from overfitting to learning feature weights.

What I find most striking about the workshop is how intuitive the concepts involved are when you strip away the technical details and jargon. In a way, the students learned about many machine learning and data science concepts - feature engineering, weight learning, experimentation - without realizing it, and they understood all of it! An interesting extension to the session we ran would be one where, after completing the activities we did, we would go back through everything done and discuss some of the more technical details related to them (this, of course, would be most appropriate for students in the second half of high school).

It sparked an interest in them - Mentor Experience by Raghav Batta (Illinois Camp)

The event went really well. The students were very interested in the workshop. Every student who wants to go to a good grad school is talking about Machine Learning.
We had a registration of about 8 people, but the turnout was about 20, which was really awesome.
I think the depth we went into, at their level, was alright. However, I feel it could be more structured. Probably a series of 3-4 lectures could achieve that. It would get more people interested as opposed to people coming in for just one lecture and leaving. People their age, coming in for just one session, might not get involved to a serious degree and they won’t care much about it.
With a series of sessions, only the people who are really interested would stay and gain deeper insights. So we could gradually talk about more complex concepts and come up with demonstrations using R or Python to make it illustrative.
We were 3 mentors for 20 people, which was a decent ratio. Also, the university was supportive with laptops and other resources which made it easy for the students to get the exercises done hands-on. The students were very interested and asked a lot of questions. Some of them already had a background and some were just curious, which is a good thing. A student came to me after the workshop seeking suggestions on how he could work around with the data in his project using machine learning. Another one asked my opinion on the statistics side of it. I feel if the students are staying back after the workshop to ask questions, it means they liked it. It showed that it had sparked a bit on interest in them about the topic.