The story of how a five-membered team used empirical data to do good for a village tucked away in a rural corner of Northern India

Using Data Science to make common lives better

NOTE: All terminology and theory that has been the backbone of our team’s studies on this project are attributed to the preparatory workshop conducted by the Live in Labs Team at Amrita Vishwa Vidyapeetham, India.

In the summer of 2018, I had an opportunity to be a part of a team that visited a highly backward village in Northern India. This visit was a part of our curriculum at the college — The Live in Labs Program(Read more about it here) and our task for the program was as follows :

Data for Change

Saving animal lives with data

Photo by Dorothea OLDANI on Unsplash

In my last article, there was a theory-driven discussion on animal shelter analytics. The article focused on the need for analytics at shelters, the challenges and possible solutions to these challenges. One of the main challenges of analyzing data at the shelter was the reduced quality of data collected. In this post, I intend to shed light on the data quality problems at the shelter and how I dealt with them.

The data says “Hi! Nice to meet you!”

The data collected at the shelter had about 9–13 columns — I can’t exactly localize it as the number of columns varied every year as the shelter began adding…


An Introduction to Animal Shelter Analytics

Photo by Lewis Roberts on Unsplash

It is currently estimated that there are about 5,000 independently-run animal shelters in the world. I personally feel there might be more. The efficiency of operations at these shelters depend largely on their ability to satisfy their main objective, usually to optimize select metrics. And in order to calculate these metrics, there is a requirement to collect, maintain and analyze data.

But, how many times have we come across the use of data in animal shelters? I never did till I made the decision to approach a local animal shelter with a speculative application to be hired as a data…

8 Actionable Tips to Turn Heads With Your Work


The most important part of building one’s career in a field is to be known by one’s work. When it comes to data analytics, having impressive projects to showcase your knowledge and expertise trumps all other methods, including certifications and courses.

So, how does one build an impressive project? Even more important, what makes an analytics project impressive?

In this article, I present to you 8 tips that have helped me become a Kaggle Notebooks Expert by building narratives across different datasets. So, without further ado let’s get started.

Tip 1: Choose the goal of your analysis over the tools you want to use


Organizing a 20-day campaign revolved around learning and teaching Data Science

Being a beginner in data science does pose obvious challenges such as being overwhelmed by the sheer expanse of topics to cover, the ever-persistent impostor syndrome, spurious knowledge and confusion induced by online articles.

A good way to tackle these challenges could be to start learning together as a community, guided by steps figured out by fellow beginners and experts alike. This was our focus when we designed the 20 day #DataDecember initiative at the London School of Economics and Political Science Data Science Society.

The #DataDecember Initiative was a month long social media campaign to provide data science beginners…

Traditional literacy won’t help children navigate the age of social media

Photo by Annie Spratt on Unsplash

When Tim Berners-Lee invented the World Wide Web in 1990, he had also laid out the foundations for modern age social media. Some of you might like to call it the “Age of Information”, for obvious reasons. But, I see this as the “Age of Misinformation”.

The concept of propagation of knowledge through social media is priceless. But what makes this a less-attractive affair is the ability of the internet to loosen up the constraints of information sharing. People often succumb to the tendency to propagate news online without verifying its associated validity.

Enough articles have been written about how…

In order to improve as a student of the data world, realise that everybody around you is an able mentor

You have finished working on your first data science project. It’s simple, but works. You are amazed by how much you have learnt while working on it. Now, you wish to share your work to the rest of the world. So, you begin writing a blog. Soon, you realise that your project is not decorated enough to be “blog-worthy”.

You could have always written a “How to do X” or a “Build X with Y” article. But, you prefer to not add onto the redundancy of certain content on the internet. You think about posting your project on LinkedIn. …

Promoting mental health — more than a social obligation

Photo by Sydney Sims on Unsplash

It is ironic how we realize the preciousness of life only when it ceases to exist. Likewise, we fail to identify with the concept of mental health until somebody takes their own life.

A suicide is a horrible event, and I can be sure of this having seen one at close quarters in the last year. It devastates the deceased’s family and friends, leaving indelible scars of mental pain. And these scars often take an entire lifetime to heal.

In the aftermath of a suicide, people let emotions take over their rationale and, more often than not, are driven by…

What kills your ideas the moment you try to implement them?

Image by Ian Lindsay from Pixabay

This is my 3rd attempt at writing this story. The first 2 times ended up with me deleting my initial drafts after writing no less than 300–400 words. Now, your question might be, “Why did he delete those drafts? Could he have not come back to writing it at a later stage?”.

Well, I did come back to it at a later stage and that is when I deleted those drafts. I deleted those drafts because I was embarrassed with them. I felt that those drafts were not good enough.

Pro Tip: Stop submitting low quality projects at college

Author’s Note: This article is a take on how you can learn core concepts by working on quality projects at college. While a lot of the discussed points are experiential , it is my idea that they hold good for most CSE undergrads.


“Companies will want to see the work you have done, other than what you did as a part of the curriculum”. These were the words of a “placement teacher” at my institution. This was his subtle way of letting us know that the projects done for the grades didn’t mean much to an employer. …

Ramshankar Yadhunath

MSc Applied Social Data Science at LSE || Portfolio:

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