Super Data Science: ML & AI Podcast with Jon Krohn

385: Advanced Data Topics and People-Centered Data Science

Jul 22, 2020
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1
Introduction
00:00 • 3min
2
Scott Glendaniel: A Lifetime of Travel
02:53 • 2min
3
The Differences in Personality in the Islands
04:49 • 2min
4
Advanced Machine Learning Questions
06:41 • 2min
5
The Life and Times of a Data Scientist at Morgan Stanley
08:24 • 2min
6
Franklin Templeton: A Data Scientist's Guide to Investment Management
10:02 • 5min
7
The Future of AI
14:59 • 2min
8
The Global Fraud Problem
17:03 • 2min
9
The Limits of Anomaly Detection
18:51 • 2min
10
Ben Ford's Law for Anomaly Detection
20:22 • 3min
11
How to Overcome Fraud Detection Problems
23:20 • 2min
12
The Drawbacks of Using Random Functions in Data Mining
25:43 • 3min
13
How to Build a Fraud Prevention Model
28:25 • 2min
14
Target Mean Encoding: A Data Prep Technique
30:17 • 2min
15
How to Monitor a Production AI or Machine Learning Model
32:05 • 3min
16
Why Models Decline Over Time
34:56 • 2min
17
How to Optimize Your Model for Success
36:35 • 2min
18
How to Check Stability and Consistency in the Data Collection Process Before Using the Data Generated for Model Building
39:00 • 3min
19
How to Cross Validate a Data Model
42:28 • 3min
20
The Complexity of Machine Learning Models
45:07 • 4min
21
How to Design a Validation Strategy for Your Model Building Process
49:27 • 2min
22
Building a Model for Daily Pizza Sales
51:23 • 2min
23
The Difference Between a Data Scientist and a Lead Data Scientist
53:47 • 2min
24
How to Be a Successful Data Scientist
56:00 • 3min
25
How to Be a Successful Data Scientist
58:37 • 2min
26
The Future of AI
01:00:12 • 2min
27
The Fear That AI Could Replace Humans in Their Jobs
01:01:49 • 2min
28
AI Replacing Data Scientists
01:03:35 • 2min
29
How to Structure Your Data Science Division
01:05:54 • 3min
30
How to Integrate Individual Data Scientists in an Organization
01:08:33 • 3min
31
How to Be a Better Statistician
01:12:00 • 2min
32
How to Be a Successful Data Scientist
01:13:47 • 4min