Super Data Science: ML & AI Podcast with Jon Krohn

563: How to Rock at Data Science — with Tina Huang

Apr 5, 2022

Superstar data science YouTuber Tina Huang talks about key areas in data science, her five steps for consistency, day-to-day life at a tech company, computer science background, software languages used, SQL course benefits, traditional banking vs big tech, pharmacology impact on data science, and preparing for data science interviews

01:04:33

Episode guests

Tina Huang

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Episode notes

Podcast summary created with Snipd AI

Quick takeaways

Focus on coding, math, and commercial acumen to start in data science.

Tina outlines five steps for consistency: setting goals, having support, and maintaining commitment.

Data scientists need versatility and business understanding in large tech companies.

Deep dives

Key Areas for Getting Started in Data Science

Focusing on coding, math, and commercial acumen are key areas to start in data science. Tina highlights the importance of these aspects to build a strong foundation in the field.

Intro

4min

Exploring Data Science on YouTube and Keys to Success

11min

Motivation, Accountability, and Consistency in Achieving Goals

3min

Journey of a Data Scientist at a Major Tech Company

12min

Exploring Computer Science Tricks and Contrasting Cultures in Finance and Tech

2min

Challenges and Influences in Adopting Data Science in Finance Companies

15min

Mastering Data Science Interviews and Productivity Tips

13min

Insights on Data Science and Career Growth

3min

In this episode, superstar data science YouTuber Tina Huang joins us to discuss what it's like to work at one of the world's largest tech companies, her strategies for efficient learning, and how best to prepare for a career in data science from scratch.

In this episode you will learn:

The key areas to focus on when getting started in data science [6:01]
Tina’s five steps to consistently doing anything [11:55]
Tina's day-to-day life as a data scientist at one of the world’s largest tech companies [20:02]
How Tina's computer science background helps her work [26:20]
Traditional banking culture vs big tech [32:12]
How Tina's background in pharmacology impacts her work in data science [36:15]
The software languages that Tina uses daily in her work [45:30]
How Tina’s SQL course practically prepares you for data science interviews [47:24]

Additional materials: www.superdatascience.com/563