Insight Data Science Blog

A friend recently recommended the 7-week tuition-free Data Science intensive program at Insight Data Science (http://insightdatascience.com/). The program is specifically for PhDs who are in your position, aka moving from academia to industry. I believe the course is available in Boston, New York and remotely (not 100% sure of the latter). I'm in a similar boat. I studied Economics in undergrad, but I only have a Masters (in an unrelated field) so I didn't meet the critieria.

I recommend the Insight Data Science Fellowship. http://insightdatascience.com/ You can read Ethan Rosenthal's blog about the transition from physics to industry. http://blog.ethanrosenthal.com/

I would join this program. http://insightdatascience.com/ Note, I am not affiliated with the program, I just know that they do exactly what you're asking, and have produced amazing results.

1. There are programs to help scientists become data scientists:, e.g. https://www.insightdatascience.com/ 2. You don't feel like you're a good bet. But companies may disagree, if you present yourself with confidence.

Agreed, we've also written about why the best use of your time is to start with a baseline (even in Computer Vision!) https://blog.insightdatascience.com/always-start-with-a-stupid-model-n…

For data science: http://insightdatascience.com/ only accepts those who have completed a PhD

Data Engineer/Program Director | NYC | Fulltime, Onsite Insight Data Engineering is looking for a full-time program director/data engineer to help lead a fellowship program. If you want a job that leverages your software development experience but allows you to do more, this could be a great role for you. See http://insightdatascience.com/jobs?gh_jid=248141 for more details.

There's also http://insightdatascience.com/ . This is a little bit different; it's a program aimed at training postdocs in various fields to be data scientists.

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).

Actually there are several efforts to increase the number of programmers, e.g. http://hackerschool.com, http://insightdatascience.com, etc.

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

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 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… 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/YAR… Since hadoop jobs are normally a JAR, there are several ways of creating a jar ready to be submitted to an hadoop…

If you have a PhD check out http://insightdatascience.com/

Apply to http://insightdatascience.com/

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.

Your CV must include links to ML project(s) you've completed. In fact, various datascience/ML fellowships exist for this very reason - to help people like you to complete a project and get hired. You can see who was hired where after completing a project [1], and example projects [2]. Insight Fellowship is just one of many [3]. Just don't confuse these programs with AI research fellowships at FAANG labs (those are pretty hard to get into, and typically require publications in top conferences to be considered). [1] https://www.insightdatascience.com/fellows [2] http://xyz.insightdatascience.com/blog/ [3] https://www.google.com/search?q=ai+fellowship Good luck!

Masters and PhD are required for particular employers. Others not so much but there are caveats. High probability of being the “data” guy who is then made to be the database admin. Better summary at: https://blog.insightdatascience.com/from-phd-to-data-scientist-17209c6…

A lot of STEM PhD's are transitioning into data science in industry. It seems you have a lot of CS background as well, so "data engineer" might also be a role you should look into. Also, you say you have no experience working with a team. So no collaborations? No co-authors (surely you must have some students working for you)? I see from another comment that you had to go through the tenure process. I'm sure you had some deadlines to meet there and some important requirements to meet. As far as tips, I have found this article helpful: https://blog.insightdatascience.com/preparing-for-the-transition-to-da…

Blog post on how this was built: https://blog.insightdatascience.com/elastik-nearest-neighbors-4b1f6821…

I see tons of them. If you're interested in ML, you're probably more looking towards data science. Data engineering (in general) is more about getting the data in a state where it can be used (extracted, cleaned, moved, transformed, etc.) at least from what i've commonly seen in the industry. A decent breakdown is here: https://blog.insightdatascience.com/data-science-vs-data-engineering-6…

Here's a complete tutorial--from concepts to step-by-step command-line scripting. Eg, you provision EC2 instances, deploy a Hadoop cluster, and build a simple data processing pipeline on the cluster. this is intended primarily for data scientists; two mid-level devs in my shop worked through it, and said it was solid. https://blog.insightdatascience.com/introducing-pegasus-one-does-not-s…

Well, if you believe Insight's white paper (http://insightdatascience.com/Insight_White_Paper_2013.pdf), the answer is "Yes".

PhDs interested in data science job may find the Insight Data Science Fellowship a good opportunity: http://insightdatascience.com/fellowship.html It's a paid six week post-doctoral training fellowship in SV that ends with interviews with companies looking for data scientists.

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…