randyzwitch

12 ms·

91.
My journey out of RSI (n=1)(jbendeaton.com)
1 points by randyzwitch 6y ago|0 comments
92.
Don't write documentation in Markdown(buttondown.email)
286 points by randyzwitch 7y ago|211 comments
93.
Distilleries All Across the Country Are Creating Their Own Hand Sanitizer(marthastewart.com)
1 points by randyzwitch 7y ago|0 comments
94.
by randyzwitch 7y ago
OP Definitely agree with you both. I chose Dash because I knew Python, but JavaScript is just something that never seems to click with me. They definitely make it easy for the pandas-using, wants-one-screen-and-a-few-dropdowns type of perso
95.
Plotly Dash and OmniSciDB for real-time data visualization(omnisci.com)
41 points by randyzwitch 7y ago|6 comments
96.
by randyzwitch 7y ago
Yes, that's my point. It's too simplistic to say "well, the data fits in RAM", you have to add parallelism to make the workload tolerable. In the past, some people have done that using MapReduce or Spark, GNU parallel or
97.
by randyzwitch 7y ago
No, but I frequently see people implying that you can do your data science in Python and R as long as you can fit the data in RAM. As you mention, it's not RAM that's the limiting factor for larger data volumes, it's finding
98.
by randyzwitch 7y ago
While pithy, the implication that you are going to process 12 TB of data in RAM using mostly single-threaded tools doesn't reflect reality.
99.
by randyzwitch 7y ago
Yes, that was generally my point. Even if available code already took advantage of multiple cores, my experience has shown that currently these chips bring out some latent assumptions on what hardware the code will be running on (whether th
100.
by randyzwitch 7y ago
I have the 3970X and use it with Ubuntu. It's fun to have a lot of cores, but in the beginning it's highlighted some areas where open-source programs aren't assuming you're going to use that many cores (similar to what t
101.
Static Site Generators – Full Stack Python(fullstackpython.com)
1 points by randyzwitch 7y ago|0 comments
102.
Accelerating Tensor Computations in Julia with the GPU – Katharine Hyatt(kshyatt.github.io)
15 points by randyzwitch 7y ago|3 comments
103.
A Beginner's Look at Benchmarktools.jl(randyzwitch.com)
1 points by randyzwitch 7y ago|0 comments
104.
by randyzwitch 7y ago
This article is a great example of how a series of small decisions can mean the difference between wild success and modest success (or even failure)
105.
by randyzwitch 7y ago
Collaborator here: these visualizations required 4 NVIDIA P40 GPUs (96GB GPU RAM) for full interactivity using a pre-release version of OmniSci 5.0
106.
OmniSci and Intel Collaborate to Bring Accelerated Analytics at Scale to CPUs(omnisci.com)
7 points by randyzwitch 7y ago|2 comments
107.
Dates, Times, Calendars– The Universal Source of Data Science Trauma(medium.com)
4 points by randyzwitch 7y ago|0 comments
108.
by randyzwitch 7y ago
OmniSciDB is useful when you need really high-performance from a relational database. Think <100ms query times over billions of records. This level of performance is useful for streaming use cases, operations, repeated drilling into subs
109.
by randyzwitch 7y ago
This is a really comprehensive tutorial, one of better uses of Jupyter Notebooks
110.
Freeing the data scientist mind from the curse of vectoRization(towardsdatascience.com)
3 points by randyzwitch 7y ago|0 comments
111.
by randyzwitch 7y ago
How is this fewer than 32,000 steps? Doesn't the act of running this gem on every file to clean it count as a step?
112.
by randyzwitch 7y ago
There are 10's of thousands of CS majors every year, and probably hundreds of thousands of people who think being a data scientist would be "fun". No company is going to weed through that pile to find the literal "best&q
113.
by randyzwitch 7y ago
Like any other job hunt, it all comes down to networking with people you hope to work with. Blindly learning techniques won't get you anywhere if you don't have anyone who will vouch for you/introduce you to other people
114.
by randyzwitch 7y ago
This is a good point. There are lots of interesting possibilities with Observable, especially using the OmniSciDB open-source project for analyses that people intentionally do want to share
115.
by randyzwitch 7y ago
Could you be more specific? Does which part scale?
116.
by randyzwitch 7y ago
It's certainly possible, I guess the bigger question is whether people would be comfortable having a database do an automatic data transfer into a public tool. In the case of this Jupyter integration, it's a Docker image that&#x27
117.
Persistent R Objects in C(blog.davisvaughan.com)
2 points by randyzwitch 7y ago|0 comments
118.
Stop Using Zip Codes for Geospatial Analysis – Carto Blog(carto.com)
6 points by randyzwitch 7y ago|0 comments
119.
Minute Quickstart with OmniSci’s Charting Libraries(omnisci.com)
1 points by randyzwitch 7y ago|0 comments
120.
How Geospatial Data Is Changing the Way We Manage the Environment Around Us(gislounge.com)
1 points by randyzwitch 7y ago|0 comments

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