A/B testing and growth analytics at Airbnb, building data science tools and metrics store with Nick Handel, the data scientist show#037

1

Introduction

00:00 • 3min

2

What's the Day to Day Like as a Data Analyst in Black Rock?

03:06 • 3min

3

How Did You Get to AirBeand Be?

05:48 • 3min

4

Data Science Tools, the Experimentation Framework?

08:57 • 3min

5

What Are Common Mistakes People Make With Product Experiments?

11:56 • 3min

6

Cannibalization

14:40 • 2min

7

The Power of Product Experiments

16:55 • 2min

8

Is the Unit of Experimentation Standardized?

19:07 • 2min

9

Do We Really Need to Log People Out After 40 Hours?

21:34 • 4min

10

Is There a Framework for Data Science?

25:28 • 3min

11

Data Science - How to Avoid Random Hypotheses That Are Meaningless?

28:30 • 2min

12

How to Build Trust in the Data Science Industry

30:06 • 5min

13

Yen, You Don't Read It All.

35:17 • 2min

14

Logging Experiments Are Impacting Customer Service Tickets

37:34 • 3min

15

Do You Coordinate?

40:17 • 2min

16

The Biggest Surprising Thing About Ab Testing?

42:05 • 4min

17

Is There a Good Culture of Experimentation?

46:16 • 2min

18

You'll Learn Something From It

48:27 • 5min

19

Using Event Logging to Analyze Data

53:17 • 4min

20

Machine Learning at Arbanby - What's the Story?

57:07 • 3min

21

Machine Learning and Events - A Great Case Study

59:40 • 4min

22

Machine Learning Models - How Do You Know if You're Not Missing Something?

01:03:58 • 6min

23

Arn Beand B - How to Scale for Different Use Cases

01:09:57 • 2min

24

Using Python in Machine Learning Models?

01:12:12 • 1min

25

Building Tooling Is Really Hard, Is It?

01:13:41 • 2min

26

Scaling a Business

01:15:33 • 2min

27

Build a Tool - What Are Some Other Important Decisions You Made?

01:17:05 • 2min

28

Machine Learning - What's the Future Store?

01:19:05 • 3min

29

A Data Scientist's Perspective on Machine Learning and Machine Learning

01:21:50 • 2min

30

Data Engineering - The Hard Part Was Generalization, Right?

01:23:55 • 5min

31

Transform Is a Metric That Is the Language for Data.

01:28:47 • 2min

32

Using a Semantic Layer for Machine Learning?

01:30:30 • 3min

33

Are You Building a Reporting Tool?

01:33:41 • 4min

34

Is There a Future for Data Science Tools?

01:37:59 • 5min

35

Machine Learning

01:42:45 • 2min

36

Metric Flow

01:44:30 • 3min

37

Is It Really Important to Generate Metrics for Product Experiments?

01:47:21 • 3min

38

What Are Some of the Lessons You Learned From Data Science?

01:50:24 • 4min

39

Don't Follow the Formula, Do Sandwich?

01:53:54 • 3min

40

Are You Making These Mistakes as a Data Scientist?

01:56:36 • 3min

41

The One Example of the Industry Is Missing

02:00:00 • 2min

42

What Do You Learn From Your Brother?

02:02:08 • 2min

43

I've Learned a Lot From My Co-Fender

02:03:47 • 3min

44

Are You Planning a Wedding Already?

02:06:49 • 3min