Data Chatter

The phrase “using data to tell stories” is so commonly used nowadays that it runs the risk of becoming a cliche, if it hasn’t become one already. This episode’s guest flips this logic around - instead of using data to tell stories, he uses stories to teach data science!

Arvind Venkatadri is a faculty member at Srishti Manipal School of Art, Design and Technology. His research/teaching interests include TRIZ, Computation in R, Design using Open Source Electronics Hardware, and Complexity Science. He is part of the School of Foundation Studies at SMI.

This is a very wide ranging conversation. We talk about, among other things, The Three Musketeers, Lawrence of Arabia and Legally Blonde. We talk about how Arvind leverages all of these to teach his students data science and logic and game theory.

At a time when the field of data science is rife with “pile stirring”, where a large section of practitioners treat it as an extension of software engineering, Arvind’s approach, centred on stories and the human experience, is really refreshing. His approach also gives a pointer on how to widen the base in terms of attracting people into data science. 

I must apologise for one thing - this conversation was recorded during Deepavali in November 2021, so you can occasionally hear the sound of firecrackers in the background. I really hope you can get past that and listen to Arvind’s stories.

Show Notes

00:03:00: Arvind’s journey into teaching Data Science in an art school

00:05:45: Teaching data science to art students

00:15:45: Teaching statistics through art and stories. Wassily Kandinsky

00:23:00: Teaching coding through art

00:31:00: Shapes and colours and emotions

00:44:00: Lawrence of Arabia (can’t say more here in the description!)

00:50:00: Data science and the human experience

Links:

Arvind’s homepage

Arvind on Twitter

Arvind’s course on R for artists and designers

An intro to Wassily Kandinsky's work

Data Chatter is a podcast on all things data. It is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".

The podcast is hosted by Karthik Shashidhar. He is a blogger, newspaper columnist, book author and a former data and strategy consultant. Karthik currently heads Analytics and Business Intelligence for Delhivery, one of India’s largest logistics companies.

You can follow him on twitter at @karthiks, and read his blog at noenthuda.com

There is a conception, or misconception, that journalists are not good at maths. It is rather common to see newspaper headlines and graphics that make basic mathematical and logical errors.

On the other hand, in the last decade or so, we have seen a massive rise in “data journalism”. With more and more data being available, journalists are able to write stories exclusively based on data.

How do these two square off?

To answer this, we have Sukumar Ranganathan, editor in chief of the Hindustan Times. He was previously editor of Mint, of which he was one of the founding editors. It was while he was at Mint that he gave a big push to the then nascent field of “data journalism”, inviting writers such as HowIndiaLives, Rukmini S and myself to write data-backed pieces for Mint. He has previously worked in editorial leadership roles at The Hindu Businessline and Business Today.

Sukumar has degrees in chemical engineering, maths, and business administration, and is interested in mathematics, science and technology, the history of business, new media, and data-based political journalism. He reads and collects comic books and is an amateur birder. He tweets under the ID @HT_ed

Show Notes:

00:03:15: Are journalists really bad at maths?

Data Chatter is a podcast on all things data. It is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".

The podcast is hosted by Karthik Shashidhar. He is a blogger, newspaper columnist, book author and a former data and strategy consultant. Karthik currently heads Analytics and Business Intelligence for Delhivery, one of India’s largest logistics companies.

You can follow him on twitter at @karthiks, and read his blog at noenthuda.com

There are two dominant programming languages used for data science nowadays - R and Python, each having its own set of loyal users. Both have their own strengths and weaknesses. In this episode, we look at what each langauge is good and bad at, what kind of people are more likely to use each, and how being able to program in both and switch seamlessly can indeed be a superpower.

Today’s guest is Abdul Majed Raja RS, a Data Scientist at Atlassian. Abdul Majed likes to call himself an Analytics Consultant with over a decade of experience helping organisations solve their business problems. He's also a Content Creator trying to help newcomers navigate the Data Science space easily and learn continuously. You can find him on Twitter and on Youtube at 1littlecoder. 

Show Notes: 

00:03:00: How Abdul got into analytics

I don't like Notebooks - Joel Grus  - 

Interface between R and Python - reticulate.

Julia Silge Youtube Channel for latest Tidymodels tutorials

Advantages of Using R Notebooks For Data Analysis Instead of Jupyter Notebooks - Max Woolf

Data Chatter is a podcast on all things data. It is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".

The podcast is hosted by Karthik Shashidhar. He is a blogger, newspaper columnist, book author and a former data and strategy consultant. Karthik currently heads Analytics and Business Intelligence for Delhivery, one of India’s largest logistics companies.

You can follow him on twitter at @karthiks, and read his blog at noenthuda.com

Over the last decade, we have seen tremendous advances in big data, data science, artificial intelligence and machine learning. Every compnay wants to be a tech-first comapny now, and wants to “do data science". Companies can probably double their valuation by just adding a  “.ai" to their names. Companies that actually use artificial intelligence and machine learning maybe have an even higher premium on their valuations.

However, is Data Science worth the hype? Is AI going to take over the world?  And why is data science being eaten by computer science? What happned to classical analytics, operations resarch and statistics?

This week’s guest is someone who did data science even before the phrase had b een invented.

Amaresh Tripathy is SVP and Analytics Business Leader at Genpact. Till recently he was a Partner with PWC, leading the firm’s Data & Analytics Consulting, and helped build a $500mm business. Previously, Amaresh founded and co-led the Information and Analytics Practice for Diamond Management & Technology Consultants, and also serves as Adjunct Professor of Data Science and Business Analytics at the University of North Carolina, Charlotte.

Amaresh has helped Fortune 500 companies in multiple industries (healthcare, retail & consumer, communications) to help define and implement their analytics and AI strategies and institutionalize data enabled decision making.  He has led organizations to help embed analytics in their front, middle and back office functions and manage the change process.

Show Notes:

00:03:00: Definitions - data science, artificial intelligence, machine learning, etc.

Links:

Thomas Davenport and DJ Patil on Data Science as the “sexiest job of the 21st century” (2012 article)

Hal Varian on statistics as a “sexy job”

Data Chatter is a podcast on all things data. It is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".

The podcast is hosted by Karthik Shashidhar. He is a blogger, newspaper columnist, book author and a former data and strategy consultant. Karthik currently heads Analytics and Business Intelligence for Delhivery, one of India’s largest logistics companies. 

You can follow him on twitter at @karthiks, and read his blog at noenthuda.com/blog

In this edition of data chatter, we will talk about maps. Maps are excellent devices for telling stories. Think of the maps you see around election times that show which parties won seats where. in fact, the first ever scatter plot - Dr. John Snow’s figure of cholera cases in London, was essentially a map. Or think of the famous map of Napoleon’s invasion of Russia.

And telling stories through maps is an exercise in data science. Data overlaid on maps can help tell really powerful stories. And as we learn in today’s conversation, the process of mapping is no diferent from the process of data science.

Our guest is Raj Bhagat Palanichamy, or as he calls himself “mapper for life”. Raj works for the World Resources Insitute India, where he leads projects on urban development, water resources and transport.

In this conversation, Raj talks about his journey into mapping, how he makes his maps, and how WWE influences the way he tells his stories.

Highlights:

00:03:00: Raj's journey into the world of maps and mapmaking

Raj's 30 day map challenge in 2020

Data Chatter is a podcast on all things data. It is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".

The fundamental principle underlying all analytics and data science is Probability. And probability was first invented, or should I say discovered, to assess risk. So what is risk? Can we quantify and measure it? How do we handle risk in life? Is risk always bad?

Today’s guest on Data Chatter is Bala Vamsi Tatavarthy, who is co-founder and investment advisor at Aravali Asset Management, a global arbitrage fund.

Vamsi was my classmate at IIT Madras, where he studied computer science but spent most of his time gaming. He then went to IIM Ahmedabad, where he continued to game heavily and graduated with a gold medal. He now runs a hedge fund, and spends a lot of time gaming. 

Moreover, he was one of the last traders to trade on behalf of Lehman Brothers, on 15th September 2008. 

Risk, as you can imagine, is a vast subject, and so this is a long podcast. We talk about measuring risk, problems with too much measurement of risk, how risk can be managed, and all that. We also talk about movies, games, the differneces between poker and bridge and physics envy.

Show Notes

00:03:45: Defining Risk, and Lehman Brothers’ collapse

00:09:00: Can risk be created or destoryed? Is it conserved?

00:15:00: Risk, probability distributions and long tails

00:20:45: Uncertainty, volatility and risk

00:28:30: Hedging

00:35:00: Utility functions

00:42:30: Games and risk

00:54:00: Bridge and poker, and finite and infinite games

01:04:15: Ergodicity

01:07:30: VaR, Risk-metrics and Goodhart’s Law

01:14:30: Correlation

Links:

Finite and Infinite Games

“Risk once created cannot be destroyed”

The Wired article about Gaussian Copula, used to estimate correlations

Too Big To Fail, by Andrew Ross Sorkin

Ergodicity Economics 

Data Chatter is a podcast on all things data. It is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".

When I was graduating college in the mid 2000s, the word in job descriptions that most commonly appeared alongside “data” was “analytics”. However, around 2010, the phrase “data science” (HBR link) got coined, and took over the world in the next five years. Nowadays it seems everyone wants to be a “data scientist”

However, where is the science in data science? And why are so many people with PhDs in pure science moving to data science?

To understand this better, I bring back one of the old guests of Data Chatter. Dhanya P is an aerospace engineer turned neuroscientiest turned data scientist. She is co-founder of Messy Fractals and Kabaddi Adda, and a Senior Scientist at Sapien Labs. Dhanya talks about her journey from neuroscience to data science, why a PhD is good training for data science, and what the “science” in data science is all about. 

You can follow Dhanya on Twitter at d2a2d

Show Notes:

00:02:30: Dhanya’s journey from Aerospace Engineering to Neuroscience to Data Science

Data Chatter is a podcast on all things data. It is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".

Everyone wants to do “data science”. Companies want to introduce “machine learning” in their products. Most fund raises by startups nowadays are accompanied by a statement of intent to invest in data, and data science.

Back in 2006, mathematician Clive Humby, who was working for Tesco, made the statement that “data is the new oil” (to give context, we were in the middle of a massive bull run in oil prices then). And so companies are investing in data.

However, just investing in data capture and hiring data scientists is not enough for a company to get value. It is important to structure the relationship between data and business, and how the data team is managed, in the right way for the data team to be effective.

Today’s guest is Anuj Krishna. Over the last 14 years, Anuj has worked with multiple enterprises on both, the translation side as well as the execution side of analytics. He has helped create standard processes for analytical problem solving that are in use in multiple enterprises.

Anuj was an early employee of MuSigma, and then went on to co-found TheMathCompany. In his current role, Anuj is Head of Assets at TheMathCompany, and is also responsible for operations related to TheMathCompany.

Show Notes:

00:03:00: How business and data science currrently interact

00:06:30: Translating from analytics to business

00:13:00: Structuring a data science team

00:22:00: Data science versus business intelligence

00:29:00: How can a business person get best value out of a data team?

00:32:00: Why data science projects fail

00:38:30: Evolution of the data science industry over the last decade

Links:

Anuj Krishna

TheMathCompany (LinkedIn)

Data Chatter is a podcast on all things data. It is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".

There is an ongoing debate on when children should be taught to code. There is one group of people which insists that computer programming is a lifelong skill, and is best taught early. The opposing argument is that coding is possibly a fad, and that children will learn it when they have to.

But what about data science? The field itself is less than 15 years old. Does it make sense to introduce it to children at an early age?

According to Rahul Raghavan, the guest on this episode, the answer is an overwhelming “yes”.

Rahul is a Montessori educator and founder of pep School v2, a Montessori school in Bangalore. Before getting into education, he was in the corporate world, working in impact investing (with VentureEast) and then with Amazon.

You can follow him on Twitter at @rahulrg

Show Notes:

00:02:55 - Introduction to the Montessori method

Links:

Rahul on Twitter

Commentary on the viral video on “why should I learn maths?”

Florence Nightingale’s chart on causes of death in the Crimean War

Geometric intuition on how sqrt(2) + sqrt(3) is approximately equal to pi

Edward Tufte's books

Data Chatter is a podcast on all things data. It is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".

One of the first industries to extensively use advanced maths to do better was financial services. Ever since Fischer Black and Myron Scholes published their seminal paper on option pricing in 1973, Wall Street firms hired mathematicians and scientists by the droves, getting them to model asset prices in order to get an edge in the market. Even today, top hedge funds such as Renaissance, Citadel and Two Sigma prefer to hire scientists rather than finance professionals to manage their portfolios.

However, in the last decade or so, as Data Science and Artificial Intelligence have taken over the rest of the world, Wall Street has not maintained its leadership position in the use of maths to make money. How and why did this happen?

In order to understand this, we talk to Hari Balaji, co-founder of Romulus, an award winning unstructured data automation platform for Financial Services firms.

Prior to founding Romulus, Hari spent a decade in quant & data roles at Goldman Sachs across Hong Kong and Singapore. Hari is an alumnus of IIT Madras & IIM Ahmedabad.

Show Notes

00:03:15 - What is data science and what is artificial intelligence?

00:10:40 - What Hari’s company does

00:14:00 - Toolbox versus hammer-nail approaches

00:15:00 - This history of math in the financial services industry

00:28:45 - Wall Street is never a first mover but a great follower

00:33:30 - How Wall Street uses data science nowadays

00:41:00 - Why most innovations have happened at smaller firms

00:44:00 - Why the financial industry doesn’t behave like the Tech world

Romulus on Twitter

Romulus on LinkedIn

Data Chatter is a podcast on all things data. It is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".