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Jason Costello | Data Science vs Software, Academia vs Industry

Jason Costello | Data Science vs Software, Academia vs Industry

FromData & Science with Glen Wright Colopy


Jason Costello | Data Science vs Software, Academia vs Industry

FromData & Science with Glen Wright Colopy

ratings:
Length:
69 minutes
Released:
Jul 19, 2021
Format:
Podcast episode

Description

Interested in Data Science? Learn Data Science and Statistics from experts as they cover key topics in the field. The Data & Science podcast focusses on teaching data scientists how to think critically in order to solve data analysis problems across various scientific domains.
 
Jason Costello | Data Science vs Software, Academia vs Industry Jason Costello (Hypervector) describes his (non-trivial) transition from academic research into big tech and then the healthcare industry. He outlines a strategy to find the cool research problems that you get in academia while still delivering value to your company. We then talk about the interface of data science / machine learning and software.
 
0:00              Deploying Data Science into the Real World
8:24              Transitioning from Academic to Industrial Data Science
16:56            First step to delivering value to industry
21:38            Toy example of high value data science
25:28            Deep technical challenges are real and useful too!
29:59            Formalized logic in machine learning solutions
32:54            Data Science & Machine Learning Projects can fail.
38:50            Getting to the cool data science projects
47:21            Putting Machine Learning Models into Software
56:21            Software and Deduction, Machine Learning and Induction
1:06:06         Is Software A Deductive Complex System?
 
Released:
Jul 19, 2021
Format:
Podcast episode

Titles in the series (88)

Data and Science with Glen Wright Colopy is a podcast covering critical scientific reasoning, particularly from a data science / machine learning / statistics perspective. Episodes typically focus on understanding of how to be better scientists and critical thinkers for the practical purpose of being a better data scientists. Previously called: ”Pod of Asclepius”