The Data Exchange with Ben Lorica

Data Science and AI in Context

Jan 26, 2023

47:40

Highlights

AI Chapters

Episode notes

Injecting Software Engineering Rigorousness Into Data Science?

01:58

Getting Students Excited About Data Science Topics

01:41

Is There a Future for Open Source Language Models?

01:47

Introduction

3min

What's the Origin Story Behind the Book on Data Science?

3min

Is This a Good Book for Product Managers?

2min

The Analysis Rubric for Data Science Projects

3min

The Best Way to Operationalize a Data Science Rubric?

5min

Injecting Software Engineering Rigorousness Into Data Science?

2min

Can We Get a Good Result?

2min

Do You Have the Right Objective Function?

3min

Is There a Rubric for Data and Analytics?

2min

10.

Getting Students Excited About Data Science Topics

2min

11.

Machine Learning - What's Next?

3min

12.

Is the Trend Towards Synthetic Data Also Getting Automated?

2min

13.

Is There a Risk in the Python Software Supply Chain?

4min

14.

Is There a Future for Open Source Language Models?

2min

15.

Is There a Tradeoff Between Convenience and Control?

2min

16.

What Do Data Scientists Need to Know in Five Years?

2min

17.

Is It Calculus?

4min

18.

How Much Coding Will Software Engineers Still Be Doing?

2min

19.

Data Science and Context - A Must Read Book

2min

Peter Norvig (of Google and Stanford) and Alfred Spector (of MIT) are part of the team of authors behind the must-read book Data Science in Context: Foundations, Challenges, Opportunities. We discussed their recent book and tool a deep dive into their Data Science Analysis Rubric, and we also talked about a trending topics in AI including looming regulations, synthetic data, and Large Language and Foundation Models.

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Detailed show notes can be found on The Data Exchange web site.