We talk with Michel Tricot, who is the Founder and CEO of Airbyte, which is an open source data integration Y Combinator startup. It has raised over $30M in capital and has been growing quite fast. It was a great conversation and I think you wi

26: Building Data Engineering Pipelines at Scale (with Data Warehouse, Spark and Airflow)

Aug 18th, 2021 39m 30s

Imagine you are at a beach and you are hanging out and seeing all the waves come and go and all the shells on the beach. And you get an idea. How about you collect these shells and make necklaces to sell? Well how would you go about doing this?

25: Talking Data Privacy with Jeff Bermant

Aug 4th, 2021 28m 11s

In this episode, I'm excited to be talking with Jeff Bermant, who is the founder and CEO of Cocoon Mydata Rewards browser. It is a browser based off Chrome and it pays people to use it! ✨ In this episode we talk about data ethics and privacy,

24: Promoting Women in Tech - With Rupal Gupta

Oct 8th, 2020 15m 5s

In this episode, we are talking about women in tech with Rupal Gupta. Rupal, a recent graduate from Online MS in CS from Georgia Tech, is a data engineer in the industry and is passionate to help promote women in tech. She also has some great t

23: Let’s Talk AWS SageMaker for ML Model Deployment

Jun 17th, 2020 19m 46s

In this episode, we talk about Amazon SageMaker and how it can help with ML model development including model building, training and deployment. We cover 3 advantages in each of these 3 areas. We cover points such as:1. Host ML endpoints for

22: Transfer Learning for NLP - With Paul Azunre

Apr 13th, 2020 46m 46s

In this episode, we are talking with Paul Azunre. Paul is one of the world’s experts in the area of Transfer Learning for NLP and is also an author of the upcoming book Transfer Learning for NLP published by Manning Publications. In this episod

21: Why Scikit-Learn and Keras are Awesome for ML

Jan 26th, 2020 19m 54s

In this episode, we talk about why the two libraries Scikit-Learn and Keras are great for machine learning. These two libraries combined with Pandas form the 3 core libraries in Python for a data scientist today. We cover things like:1) Data

20: Yogi's Guide to Analytics - An Interview with Akshay Kanade

Dec 1st, 2019 35m 44s

In this episode, we talk with Akshay Kanade. He is a business analyst working in New York City who likes taking a big view of data, and has very interesting spiritual views on data analytics and life in general, he is also a handwriting expert-

19: Statistics and Data Science- An Interview with Patrick McClory

Nov 22nd, 2019 56m 20s

In this podcast episode, we do an interview! We talk with Patrick McClory, who is the founder and CEO of IntrospectData. He is an expert working in areas of data science consulting, large machine learning projects, math, statistics and more.I

18: 5 Things to Consider for Master of Science (MS) in US

Nov 15th, 2019 19m

What should you consider for pursuing MS in US? There might be several questions in your mind as you explore this question. In this episode we cover some of the main things to consider before you make the decision. I also go into details about

17: Why Pandas is the new Excel

Oct 25th, 2019 16m 37s

The Data Life Podcast is a podcast where we talk all-about real life experiences with data and data science science tools, techniques, models and personalities. In this episode, we will talk about how Pandas is becoming a tool of choice for ma

16: Getting Started with Natural Language Processing

Oct 5th, 2019 19m 31s

So many tweets and news articles and unstructured text surrounds us. How do we make sense of all of these? Natural language processing or NLP can help. NLP refers to algorithms that process, understand and generate aspects of natural language e

15: Using Flask, REST API and Vue.js to build a Single Page Web Application

Sep 16th, 2019 20m 39s

As a data scientist, you will work on machine learning models that are deployed on websites - usually wrapped around a REST API, these days they also call this approach a “micro-service”. It is for this reason it is important to know how backen

14: Building a Character-Based Text Classifier

Aug 7th, 2019 23m 20s

Ever wonder how to automatically detect language from a script? How does Google do it? Ever wonder how Amazon knows whether you are searching for a product or a SKU on its search bar? We look into character-based text classifiers in this epis

13: Statistics of A/B Testing

Jul 17th, 2019 21m 22s

You and your team might spend a lot of time building a new feature. But how do you know if this feature will be liked by the users? One of the ways to statistically prove this is by using A/B testing. Listen to this episode to get tips, tricks

12: Your Users Don't Care How Smart You Are

Jun 25th, 2019 5m 4s

In this episode, we will talk about the importance of business impact in data science. "Your users don't care how smart you are" was a quote I read that got me started in thinking about this. The right way to do data science is to think of us

11: The Ten Essential Machine Learning Questions

Jun 21st, 2019 19m 8s

This episode covers the ten essential machine learning questions. Disclaimer: Baseline answers have been provided in the episode for guidance. For complete accuracy, please refer to textbooks or to courses by Andrew Ng on Coursera. If this con

Mining Twitter Data for Sentiment Analysis of Events

Jun 1st, 2019 18m 43s

Twitter is a rich source of live information. Is it possible to run sentiment analysis on what the world is thinking as an event unfolds over time? Could we track Twitter data and see if it correlates to news that affects stock market movements

Don't Be Shy To Pursue Your Interest

May 19th, 2019 5m

In this episode, we will talk about things like Maslow's Hierarchy of Needs, and focussing on higher level needs such as satisfaction and achieving full potential. In the area of tech, data science and software development, admitting your inter

Review of Udacity Nanodegrees - are they worth it?

May 3rd, 2019 13m 3s

Udacity has become a popular platform for learning about various things in data science, machine learning and programming in general. In this episode, we will discuss the good, bad and ugly of the Udacity nanodegrees. I will also cover my exper

6 Steps to Transition to Data Science from non-CS background

Apr 20th, 2019 15m 58s

In this episode we will talk all about the various steps to transition to data science from non computer science backgrounds.One of the main difficulties people face from non-CS backgrounds is how overwhelming it can be to transition to data s

The Top 5 Data Science Podcasts

Apr 10th, 2019 8m 20s

Welcome! In this episode, we will cover some of the top data science podcasts, that have helped me a lot in my own journey, and hopefully will be helpful to you as well. The top 5 podcasts are (linked to my favorite episodes):1) AI in Industr

What I learnt building a data science course

Mar 30th, 2019 18m 16s

Have you ever thought about building a video course? Have you wanted to share your expertise with other people via a video course on different platforms like Udemy? Have you wondered what are the economics and revenue details of building a cour

Overview of Netflix and Spotify like recommendation engines

Mar 22nd, 2019 13m 28s

In this episode, we cover the two main types of recommendation engines used at companies like Netflix and Spotify.1) Content based recommendation systems use the genres or tags of each product to find other similar products to recommend to us

3 Mistakes to Avoid in a Machine Learning Project

Mar 15th, 2019 10m 18s

You and your team might spend weeks or even months building a model. These are the 3 mistakes to avoid in your next machine learning project! This can save you a lot of time and effort in your next project. These tips have been learnt from ex