The Data Scientist Show - Daliana Liu

From physics PhD to data science leader, unexpected challenges in survey data, Python vs R, EDA best practices, building MLOps toolkit - Julia Silge - The Data Scientist Show #087

Mar 30, 2024

Julia Silge, former astrophysicist turned data science leader, discusses challenges in survey data, Python vs. R, EDA best practices, and building MLOps tools. Topics include text analysis, balancing data science and engineering, and her journey from physics PhD to engineering manager at Posit PBC.

46:18

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Episode guests

Julia Silge

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Episode notes

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Quick takeaways

Transitioning from astrophysics to data science requires self-study and blogging to showcase skills.

Balancing data analysis and tool development is crucial for Julia's fulfillment in her leadership role.

MLOps tools focus on enhancing data science tasks, emphasizing model development and operationalization.

Deep dives

Julia's Transition to Data Science

Julia shares her journey transitioning from astrophysics to data science, emphasizing the importance of self-study and blogging to showcase her skills. She highlights her experience starting in the non-profit sector, leveraging data analysis skills to secure her first data science title, and eventually transitioning to a leadership role at Positive PBC.

Introduction

4min

Transition from Data Scientist to Engineering Manager

13min

Navigating Challenges in Survey Data Analysis

8min

Navigating Data Science Ecosystem Challenges

6min

Exploring Text Data and Data Visualization

7min

Optimizing Tools for Data Science Efficiency

9min

Julia Silge is an engineering manager at Posit PBC, formerly know as R-studio, where she leads a team of developers building open source software MLOps. Before Posit, she finished a PhD in astrophysics, worked for several years in the nonprofit space, and was a data scientist at Stack Overflow where some of her most public work involved the annual developer survey. We talked about MLOps tools, challenges in survey data, text analysis, and balancing her interests in data science and engineering.

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(00:00:00) Introduction

(00:00:56) Getting into data science

(00:04:50) Transition from data centers to engineering manager

(00:14:04) Common challenges in tool development

(00:17:38) Challenges with survey data

(00:26:47) Engineering skills for data scientists

(00:28:59) Balancing roles

(00:34:49) Developing skills in Exploratory Data Analysis (EDA)

(00:39:19) Python vs. R for data analysis