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.cssail
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.

naren

colinraghav

 

 

 

 

 

 

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.

What AM Research told you in 2015 – the data science way?

As the year came to an end, we looked back on what we shared with the world in 2015. As data nerds, we pushed all our blog articles in to an NLP engine to cluster them to identify key themes. Given the small sample size and challenges to find semantic similarity in our specialized area, we waded through millions of unsupervised samples through deep learning with a Bayesian framework, ran it on a cluster of GPUs for a month…yada yada. Well, for some problems it is just that humans can do things easily and efficiently; so that is what we actually did.

The key themes were:

Grading of programs – 4 posts

We need to grade programs better to be able to give automated feedback to learners and help companies hire more efficiently and expand the pool considered for hiring. We at AM dream to have an automated teaching assistant – we think it is possible and will be disruptive. Thus we dedicated 4 of our posts on telling you about automatically grading programs and its impact.

The tree of program difficulty – We found that we could determine the empirical difficulty of a programming problem based on the data structures it uses, the control structures used and its return type, among other parameters. We used these features in a nice decision tree to predict how many test takers would answer the question correctly, and we predicted with a correlation of 0.81! This tells us about human cognition, helps improve pedagogy and also helps generate the right questions to have a balanced test. And this is just the tip of the iceberg. Second, we approached the same by looking at the difficulty of test-cases and their inter correlation. We understood what conceptual mistakes people make and also got a recipe to make better test cases for programs and had insights on how to score them. For instance, we found that a trailing comma in a test case can make it unnecessarily difficult!

Finding super good programmers – Given these thoughts on how to construct a programming test and score it, we showed you how all this intelligence put together with our super semantic machine learning algorithm, we can spot 16% good programmers missed by test case based measures. Additionally, we also found automatically the super good ones writing efficient and maintainable code. So please say a BIG NO to test case based programming assessment tools!

venn

Reproduced from “AI can help you spot the right programmers”. It shows a test case metric misses 16% good programmers. Furthermore AI can help spot 20% super good coders

Pre-reqs to learn programming - Stepping back, we tried determining who could learn programming through a short duration course. We found that it was a function of a person’s logical ability and English but not did not depend on her/his quantitative skills. Interestingly, we found that a basic exposure to programming language could compensate for lower logical ability in predicting a successful student who could learn programming. A data way to find course prerequisites!

Building a machine learning ecosystem – 3 posts

Catching them young! We designed a cognitively manageable hands-on supervised learning exercise for 5th-9th graders. We helped kids, in three workshops spread across different cities, make fairly accurate friend predictors with great success! We think data science is going to become a horizontal skill across job roles and want to find ways to get it into schools, universities and informal education.

“Exams. I would take my exam results, from the report card of every year. And then I will make it on excel and then I will remember the grades and the one I get more grades I will take a gift” [sic.]

flashcard

Reproduced from datasciencekids.org. Whom will you befriend? Can machine learning models devised by high school kids predict this?

The ML India ecosystem – Our next victims were those in universities. We launched ml-india.org to catalyse the Indian machine learning ecosystem. Given India’s very low research output in machine learning, we have put together a resource center and a mail list to promote machine learning. We also declared ourselves as self-styled evaluators of machine learning research in India and promise to share monthly updates.

Employment outcome data release – We recently launched AMEO, our employability outcome data set at CODS. This unique data set has assessment details, education and demographic details of close to 6000 students together with their employment outcomes – first job designation and salary. This can tell us so much about the labor market to guide students and also identify gaps – to guide policy makers. We are keenly looking forward to what wonderful insights we get from the crowd! Come, contribute!

Pat our back! – 3 posts 

blog4-image

Reproduced from “Work on spoken English grading gets accepted at ACL, AM-R&D going to Beijing!”. We describe our system that mixes machine learning with crowdsourcing to do spontaneous speech evaluation

We told you about our KDD and ACL papers on automatic spoken English evaluation – the first semi-automated automated grading of free speech. We loved mixing crowdsourcing with machine learning – a cross between peer and machine grading – to do super reliable automated evaluation.

And then our ICML workshop paper talked about how to build models of ‘employability’ – interpretable, theoretically plausible yet non-linear models which could predict outcome based on grades. More than 200 organizations have benefited by using this model in recruiting talent and they do way better than linear models!

Other posts

On the posts off these three clusters, we told you about –
Why we exist – why we need data science to promote labor market meritoracy

– The state of the art and goals for assessment research for the next decade (See ASSESS 2015)

Our work on classifying with 80-80 accuracy for 1500+ classes

It has been an interesting year at AM, learning from all our peers and contributing our bit to research, while using it to build super products. We promise to treat you with a lot more interesting stuff in open-response grading, labor market standardizing and understanding next year. Stay tuned to this space!

Varun

Data science camp for kids!

It is an open secret that data science is becoming pervasive. What was once the preserve of statisticians and computer scientists – deft at trudging through mountains of data – has found its tools and techniques percolating into every industry and every level. Peer into the crystal ball and you don’t need to suspend reality too much to imagine a future in which a factory manager looks at production data to predict what machine might break-down soon. A cab-operator analyzes his Uber receipts to figure out where he should drive to make the most money. A sales manager looks at what kinds of customers his sales agents are most successful with to ascertain who to deploy where. Decidedly, the future belongs to the data scientist. Where will these data scientists come from? Who is going to train them?

The very nature of the subject eschews traditional learning modes. The data scientist must have the ability to learn quickly the context of the dataData science camp!, build hypotheses, have the ability to use techniques to confirm his suspicions and then construct predictors or automated systems. It marries technology with knowledge; intuition with scientific rigor. Our education systems will be slow to adapt – they will have to devise new methodologies, develop syllabi and learn to simultaneously involve multiple teachers. In the meanwhile, a whole generation of students might graduate who do not have the skills that industry expects from them in a data rich environment.

At Aspiring Minds, we’re passionate about helping students reach their full potential. We plan to pursue a series of initiatives to help advance data science education in India and around the world. As a first step, we held a data science camp for elementary school students! The participants continuously surprised us – with their knowledge, their understanding and even their wit. Two things became clear quickly – a. kids seldom confront open-ended problems and it took some getting used-to the idea of there being no one correct, pre-decided answer and b. with some guidance, they learn astonishingly quickly.

Read more about our exciting and rewarding weekend here!

At the end of the camp, the participating kids blogged about their experiences and the plots/analysis that they came up with. Read about them here.

Our team got enthusiastically involved in mentoring the students through the exercise and ended up learning more about their own teaching styles in the process.

We’ve also put out the exercises and resources we used for the camp for you to replicate it in your school/university/workplace. If the thought of indulging high schoolers in data-science seems absurd to you, snap out of it! It is possible; we tried it and the kids had a fun time picking up these concepts.

Let us know what you thought of our data camp. Please do write to us if you go ahead and try this out with students around you. We’ll eagerly look forward to that!

Samarth Singal
Research Intern, Aspiring Minds
Class of 2017, Computer science, Harvard.