Top 5 Data Science Podcasts For Beginners

With the technology changing at a meteoric pace, tech enthusiasts are usually one step behind when it comes to upskilling and keeping themselves updated. With rising popularity, buzzwords regarding data science have started to surface and have swarmed over the foundational concepts making them obscure for beginners.  



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Here we list few subject-specific podcast episodes that can help you get an idea on what to expect from this field and what tools you require to become an expert


Is Data Science Something For You

It is always wise to have a cursory view of a new field before diving deep. In this episode of the podcast titled The Effective Statistician, industry experts will help you gain some insights on starting out in this field. They discuss how much of statistics, a data scientist needs to know and the areas where data science has had an early impact.


Basic Concepts Of Statistics

If you have made it through the previous podcast and are curious to know more about data science then you can start here. This episode is a part of The Science of Everything podcast and this episode will introduce you to key concepts like types of statistical data, sampling methods, the difference between descriptive and inferential statistics, statistical significance, and p-values. And briefly about, important statistical tests like chi-square test, t-test, and linear regression.

Being Bayesian

It is almost impossible to do a course on statistics without coming across Bayes’ theorem. Bayesian inference assists statisticians with optimal decision making. This episode explores the fundamental concept of what it is to be Bayesian — describing knowledge of a system probabilistically, having an appropriate prior probability, know how to weigh new evidence, and following Bayes’s rule to compute the revised distribution.

Poisson Distribution

The Poisson distribution is a probability distribution function used for events that happen in time or space. This episode by Linear Digressions, introduces you to the distribution and then explores some wide variety of applications using the Poisson distribution ranging from supernovas to a study on army deaths from horse kicks.

Maximum Likelihood Estimation In Statistical Environments

Designing machine learning models in statistical environments needs knowledge of frequencies at which events occur in the real world.  In the real world, the data is large, random and inconsistent. This episode is a part of the series A Gentle Introduction to AI and ML, by Learning Machines 101 and might help you develop some intuition regarding rudimentary statistical designs and, help one prepare for next level of machine learning.


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Ram Sagar
I have a master's degree in Robotics and I write about machine learning advancements.

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