#333: State of Data Science in 2021

1

Introduction

00:00 • 2min

2

How Did You Become a Programmer?

01:33 • 2min

3

Aniconda, Python and Data Science

03:38 • 2min

4

The Challenges of the Data Science World

05:24 • 2min

5

Can Anniconda Work on a Raspberry Pi?

07:47 • 2min

6

Python - A Scientist's Guide to the Apple Silicon Transition

09:35 • 2min

7

Is There a Way to Automate Development on Macous?

11:52 • 2min

8

Is There a Workflow?

13:24 • 5min

9

Open Source Apples - Risk v or Five?

18:09 • 2min

10

Getting More Power in the Data Center

20:15 • 2min

11

Is There a Way to Accelerate the Apple Neural Engine?

22:08 • 2min

12

Is There a Way to Scale Up the M One?

23:47 • 2min

13

Data Scientists, What Do You Think?

25:28 • 2min

14

The State of Data Science 20 21

27:35 • 2min

15

Data Science

29:43 • 2min

16

Are You Seeing More Data Science?

31:23 • 5min

17

Data Science and Supply Chain Logistics

36:04 • 2min

18

Are Data Scientists Embedded Within Little Groups?

38:18 • 2min

19

How Do You Spend Your Time?

40:23 • 2min

20

Is Python OK for Production?

42:13 • 2min

21

Is Your Employer Encouraging You to Contribute to Open Source?

44:18 • 2min

22

Open Source and Soft War - What's Hot?

46:06 • 2min

23

The Conda Anda Condiversion

47:58 • 2min

24

Is Python Getting More Popular?

49:58 • 2min

25

Python and Python in the Lifestream

51:58 • 2min

26

Anacon Distribution Packages

53:39 • 2min

27

Piston Is Not a Rewriting Python in Rust. It's a Performance in Python

55:50 • 2min

28

Open Source

57:29 • 2min

29

C U P I - The Easiest Way to Get Started in G P Computing

59:40 • 2min

30

Talk Python - What You Need to Know

01:01:36 • 2min