In the fast-moving field of data science, staying up-to-date with the latest trends, techniques, and insights is difficult. While numerous ways exist to develop your knowledge, podcasts offer a unique and accessible medium for continuous learning. They allow you to gain information on the go, whether commuting, exercising, or cooking.
Podcasts are an excellent resource for data scientists for several reasons. First and foremost, they offer accessibility – you can listen to them anywhere, anytime, without being tied to a computer or desk. Many podcasts feature interviews with experts, providing diverse perspectives on various topics. They often cover recent developments and news in the field, keeping you up-to-date with the latest trends.
This post explores four podcasts to help you become a better data practitioner. It covers two podcasts directly related to data science and two that, while not explicitly focused on the field, offer valuable insights for your growth as a data practitioner.
Direct Data Science Link Podcasts
Let’s start with two podcasts focused on data science, offering in-depth discussions on methodologies, tools, and industry trends.
Data Skeptic
Data Skeptic was previously discussed in the Data Column in Meg Malone‘s 2019 post. Since that post, the podcast has consistently published quality episodes on interpretability, time series, k-means clustering, surveys, and more.
Data Skeptic is a podcast that takes a deep dive into different data science processes throughout a season. It’s an excellent resource for both beginners and experienced practitioners.
Why you should listen:
- In-depth exploration of data science topics
- Expert interviews providing diverse perspectives
- A mix of technical content and accessible discussions
Recommended first episode: Non-Response Bias
This episode is helpful for many data scientists who don’t typically conduct survey design. It offers perspectives on data exploration and highlights how data collection methods can significantly impact the information we derive from our datasets. It is helpful even if you’re not doing survey design because non-response bias is something you need to think about outside of surveys.
SuperDataScience
SuperDataScience is a podcast that keeps you updated on the latest data science news, focusing on the evolving field of Large Language Models (LLMs).
Why you should listen:
- Coverage of data science news and trends
- Interviews with data scientists at various career stages
- Practical insights into data science tools and best practices
Recommended first episode: Pandas for Data Analysis and Visualization
This episode is excellent for both beginners and experienced data scientists. Despite Pandas being one of the most popular data science libraries, you’ll likely learn new best practices and advanced techniques. The episode also delves into visualization tools and offers insights into working as a data scientist.
Helpful Non-Data-Centric Podcasts
At the IAA, we are learning about the importance of learning a combination of many skills to improve ourselves as well-rounded data practitioners. Here are two podcasts that, while not directly about data science, offer valuable insights for your professional growth.
Hidden Brain
Hidden Brain is a podcast that explores human behavior and relationships. It’s an invaluable resource for developing the soft skills crucial for success in any professional field, including data science.
Why you should listen:
- Explores human behavior and social dynamics
- Encourages reflection on interpersonal relationships
- Aligns with the soft skills development needed in data science careers
Recommended first episode: The Secret to Great Teams
This episode has relatable anecdotes that spark reflection on team dynamics. It discusses successful teams’ characteristics and explores why some teams fail. As data scientists often work in collaborative environments, understanding these dynamics can significantly improve your effectiveness.
Talk Python to Me
Talk Python to Me is a good resource for those looking to strengthen their Python programming skills. While programming-focused, many episodes are accessible even to those new to Python.
Why you should listen:
- Focuses on Python programming, a crucial skill for many data scientists
- Offers both beginner-friendly and advanced content
- Includes some data science-specific episodes
Recommended first episode: Clean Code in Python
This episode addresses an often-overlooked aspect of data science education: writing clean, readable code. Coming from a computer science background, I understand the importance of clean code, but it’s a skill that’s sometimes underemphasized. This episode provides an excellent introduction for data scientists looking to improve the quality and readability of their code.
Conclusion
Podcasts offer a fantastic way to supplement your data science education and stay current with industry trends. Whether you’re looking for in-depth discussions on data science methodologies, career insights, or ways to improve your soft skills, there’s a podcast for you.
You don’t need to limit yourself to data science-specific podcasts to improve as a data practitioner. Podcasts on relationship building (Hidden Brain) or general coding practices (Talk Python to Me) can be equally valuable in rounding out your skill set.
The key is to approach all content with a data science mindset. By doing so, you’ll find valuable insights and applicable skills in unexpected places, continually expanding your data science toolkit.
Columnist: Charlie Jubera