Insight Data Science's blog is written by fellows and staff of a training program for moving PhDs into data science and AI roles.
HN commenters recommend the Insight fellowship to academics moving into industry, and link its post on starting with a simple baseline model.
It's even worse. Their iOS instructors are equally questionable.
Paul Solt - No industry iOS experience. At Apple for 3 months writing test code back in 2008. Internship at Microsoft in 2009 doing testing as well. Taught a college course on iOS/ObjC in 2012. Indie/freelance dev since then and now teaching iOS at Lambda. https://www.linkedin.com/in/paulsolt/
Andrew Madsen - "Director of Curriculum R&D" at Lambda now but originally came in to start up their iOS course and I hear he still steps in to lectures from time to time. Worked at a small Mac & iOS dev shop, https://mixedinkey.com 2011-2016, until he left to teach an iOS track at another bootcamp DevMountain. Independent developer since 2006-present.
https://www.linkedin.com/in/armadsen/
Ben Gohlke - Seems like he started iOS dev since 2010 and has hopped around between a couple tech companies all in the Florida area since then until 2015 when he went to teach iOS at another coding bootcamp, The Iron Yard. Now an iOS instructor at Lambda.
https://www.linkedin.com/in/bgohlke/
Spencer Curtis - No formal education (that's okay) or industry experience! His own sole experience comes from attending another bootcamp, DevMountain, for iOS back in 2016. He mentored for that same bootcamp until leaving to be an iOS instructor at Lambda.
https://www.linkedin.com/in/spencercolecurtis/
Dave DeLong - The sole notable iOS instructor they've had, who's left since then, is this guy. Worked iOS at Apple since 2010-17. Left to do iOS…
I would recommend first of all that you get familiar with the names and versioning. Hadoop is a mess, and its basically a family of big data projects.
You have two/three main components in hadoop:
- Data nodes that constitute HDFS. HDFS is Hadoop's distributed file system, which is basically a replicated fs that stores a bunch of bytes. You can have really dumb data (lets say a bunch of bytes), compressed data (which saves space but depending on the codec you may need to uncompress the whole file just to read a segment), arrange data in columns, etc. HDFS is agnostic of this. This is where you hear names like gzip, snappy, lza, parquet, ORC, etc.
- Compute nodes which run tasks, jobs, etc depending on the framework. Normally you submit a job which is composed of tasks that run on compute nodes that get data from hdfs nodes. A compute node can also be an hdfs node. There are alot of frameworks on top of hadoop, what is important is that you know the stack (ex: https://zekeriyabesiroglu.files.wordpress.com/2015/04/ekran-resmi-2015-04-23-08-34-00.png). So you have HDFS, and on top of that you (now) have YARN which handles resource negotiation within a cluster
- Scheduler/Job runner. This is kinda what YARN does (please someone correct me). Actually its a little more complicated https://hadoop.apache.org/docs/r2.7.2/hadoop-yarn/hadoop-yarn-site/YARN.html
Since hadoop jobs are normally a JAR, there are several ways of creating a jar ready to be submitted to an hadoop…
Founder of Insight here (YC W11). Since 2012 we've been running free fellowships to help PhDs transition to roles in data science [1] and more recently health data [2]. Similarly, since 2014, we have been helping professional software engineers learn and move into data engineering roles [3]. Over 750 Insight alums now work as data scientists & engineers at 200+ companies.
This past year, we've seen more highly specialized applied AI / deep learning roles emerge in the industry. We're also increasingly receiving applications from scientists and engineers who have some machine learning experience and are learning to build out sophisticated deep learning models during their time at Insight. The new Insight AI [4] program will focus on allowing Fellows with these backgrounds implement the latest ML techniques from research or contribute to open source projects under the guidance of industry leaders, then join AI teams in Silicon Valley and New York after the program. Insight AI will accept both software engineers and quantitative scientists (no PhD required).
[1] Data Science: http://insightdatascience.com
[2] Health Data: http://insighthealthdata.com
[3] Data Engineering: http://insightdataengineering.com
[4] Artificial Intelligence: http://insightdata.ai
I don't know if you know Jake from Insight Data Science, but he calls his students fellows and his program a fellowship: http://insightdatascience.com/
Per the point you were making in your essay, though, he does this because most of his students are post-docs and (1) they understand what a "fellowship" is and (2) do not necessarily want to be a "student" again. To a postdoc, especially, student implies having to pay for it.
The other folks upthread are sort of missing the point, honestly, because when it comes to a word like "student," it touches on issues of identity and association. When someone (e.g., a friend) asks your students, "What are you doing?" they're going to respond with, "Oh, I'm an X at Hacker School."
You want X to be something the students feel comfortable saying to friends, families, and co-workers. Whether "fellow" is too pretentious or not depends on who your students are and who they'll be saying "I'm an X" to.
What kind of job(s) do you think you're interested in?
I moved from academia to a data science job, and it wasn't as hard as I thought it would be.
My background: Math PhD from a top school, though my research was very abstract - not really related to machine learning, algorithms, or statistics. I had some coding experience in undergrad but none in grad school.
My path: I did the Insight Data Science bootcamp (http://insightdatascience.com/), which was very effective for me.
There are really two things I got out of the bootcamp that made it effective:
1) Connections
2) Impressive project(s) to show off & get your foot in the door.
You may be able to do these things without the help of a bootcamp, and I think especially if you're more interested in coding, there are a ton of good bootcamps that can provide you with the connections and last little bit of training to land a good job.
I don't think a lot of people know about the Insight Fellows program yet, but it's highly relevant here: http://insightdatascience.com/
They're taking people in STEM fields who are over-qualified and under paid, and helping them transition into new careers as data scientists at top technology companies (Google, Facebook, Square, LinkedIn, etc.). It's a really interesting model because they're filling a big hole that universities have right now in that there's no degree for data science. Close to 100% of their Fellows make the transition successfully and I think the idea is something that others are going to try to copy in the near future because it's clear there's a supply-demand mismatch right now.
Fwiw, the company is a YC alumnus (a hard pivot from their original idea).