Python, every mention
12 scenes (2017, on the MAD Podcast) · ← back to Python
Latent Space 324
the MAD Podcast 232
the a16z Podcast 67
the Startup Ideas Podcast 42
the Y Combinator Startup Podcast 41
No Priors 41
TBPN 40
Top Founders 3828 more shows
every year 2017 every show Latent Space 324 the MAD Podcast 232 the a16z Podcast 67 the Startup Ideas Podcast 42 the Y Combinator Startup Podcast 41 No Priors 41 TBPN 40 Top Founders 38 
Lenny's Podcast 37 
20VC 33 
Startups For the Rest of Us 19 
My First Million 17 
the Neon Show 17 
Big Technology 14 
In Depth 13 
WTF is with Nikhil Kamath 10 
A Product Market Fit Show 10 
Capital Allocators 10 
Acquired 8 
the Official SaaStr Podcast 8 
Sourcery 8 
the Knowledge Project 7 
Cheeky Pint 6 
Catalyst 6 
Mixergy 5 
the Green Blueprint 5 
All-In 5 
BG2 Pod 3 
American Optimist 3 
Invest Like the Best 3 
How I Built This 3 
Founder's Journal 2 
Startup Acquisition Stories 2 
We Live to Build 2 
Innovators & Investors 1 
David Senra 1
Verbatim, from the transcripts: passages where Python comes up on Latent Space, the MAD Podcast, the a16z Podcast, the Startup Ideas Podcast, the Y Combinator Startup Podcast
A Fresh Approach to Technical Computing // Viral Shah & Stefan Karpinski, Julia Computing
- ▶ 3:59 Stefan Karpinski This is, this is, I'll talk about how we unify this, but this dichotomy, you know, that he observed is actually, has led to a pretty standard compromise in systems, and it's actually, it's actually very pragmatic, so for convenience, you… 4 times in the scene
- ▶ 10:50 Viral Shah Yeah, 800 people, there are 1600 contributed Julia packages, but people keep coming up to us and be like, oh, but, you know, R has 6000, or, you know, JavaScript has a bazillion or something, and, um, you know, the thing is that you could…
- ▶ 19:33 unnamed speaker I'm curious to know, people, ah, are using Python a lot because the libraries are so feature rich, it, ah, lets you write with less code, so how far will Julia go to that extent? 13 times in the scene
Three Loops of Analytics Efficiency // Sean Kandel, Trifacta (FirstMark's Data Driven)
- ▶ 6:41 Sean Kandel Uh, so probably still today the most common is using kind of hand coding tools, um, so programming languages, Python, Spark,
AI, Big Data, and Data Governance // Stan Christiaens, Collibra (FirstMark's Data Driven)
- ▶ 3:23 Stan Christiaens Uh, ideally, this translates all the way through, not into the technicalities of how to make a Python script, but actually into how does data get into an MBA program, for example, right?
- ▶ 12:24 Stan Christiaens Typing in Python commands in these notebooks and then immediately seeing a classifier or a visualization.
- ▶ 14:01 Stan Christiaens Right, and if you need to write a Python script or hack into the database of your own internal company, just do it.
Big Data as a Service // Prat Moghe, Cazena (FirstMark's Data Driven)
- ▶ 4:52 Prat Moghe It's Hadoop, it's Spark, it's Python, it's R, and it's like every three months there's a new open source project around it.
- ▶ 10:18 Prat Moghe You're running Python.
The Power of GPU Analytics // Todd Mostak, MapD (FirstMark's Data Driven)
- ▶ 12:46 Todd Mostak So one of the great things is that behind the scenes, MapD is ultimately a very, very fast database, so if I wanted to, I could create a, um, Python notebook out of this, um,
- ▶ 20:51 Todd Mostak Probably first written in CUDA, but later maybe in Python, right on top of the database.
Project Jupyter // Jason Grout & Sylvain Corlay, Bloomberg (FirstMark's Data Driven)
- ▶ 5:21 Jason Grout Now, it started with Python, but the point is, if you have a language or a computer system that's 4 times in the scene