Hello all.


About me:

-12 year poker pro. I've done alright, but won't be set up for retirement any time soon and I don't like the direction poker is headed.

-33yo, live in USA

-B.A. in economics from a good public university (2012). pretty blank resume besides that. my last non-poker job was in high school.

-passion for data science and coding in general

I have spent the past month or so taking free online courses (SQL, Python), working through a data science e-book, practicing on codewars.com, and generally messing around importing, cleaning, and plotting data.

I have the foundations I need for statistics and linear algebra, but there's a lot of learning to do. Strong understanding of statistics is what separates good data scientists from not-good data scientists.

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I would like to eventually land an entry-level data scientist position somewhere, but I know I'm not there yet. Here are some paths I'm considering:

A. Continue learning on my own. Do my own little projects. Put them on Github and use that as a 'portfolio'. Try some Kaggle competitions. Get my hard skills to a place where I could hopefully impress in an interview.

B. Enroll in a data science bootcamp or something like Lambda School or Launch School. I read mixed reviews about these online. Anyone have experience?

C. Start off aiming for a data analyst (Excel, Tableau, etc.) role to get my feet wet and get used to being in the workforce. Keep studying on the side. Perhaps an employer will help pay for a Masters.

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I could see any combination of the 3 of these being a reasonable approach.

Lastly, if anyone has experience hiring, I'd love to know what the most important criteria are for getting my resume seen and getting an interview.

Hi,

I will address in sections - feel free to reach out to me privately if you need further suggestions

What:
1) is a handicap but 2) is a great opportunity given covid times - so you need not worry

why:

use your past experience + current = map the future with target org

data science + poker - try to land up a position in one of the poker clients, solver, HUD companies - or coaching companies like upswing, rio etc

How:
Referrals, referrals, referrals - esp in covid times - see if you can get networked via 2+2 based on your activity here

Things to do:
data science:
Pay and Enroll in Johns Hopkins data science specialization in Coursera (https://www.coursera.org/specializat...u-data-science)
academics love R, industry loves Python - See if you can bridge both

without fail read these books:
https://www.amazon.com/Data-Science-.../dp/1449361323

https://www.amazon.com/Storytelling-...s=books&sr=1-1

resume/career change:
get this book - https://www.amazon.com/What-Color-Yo.../dp/198485657X
Check Liz Ryan's blog posts - https://www.forbes.com/sites/lizryan.../#3b290237677e
https://matchexecutive.com.au/ten-wa...away-liz-ryan/

thanks for the quick reply. I'll check out some of those books.

If I move back to my hometown area next spring I'll have a good network for potential referrals. My current location is a big market, but I don't have as many connections.

START NOW! DO NOT WAIT

https://www.forbes.com/sites/lizryan.../#71925a2f79a1

https://www.job-hunt.org/job-search-...-network.shtml

https://www.job-hunt.org/job-search-...-network.shtml

Hey I was in a pretty similar spot, I would recommend A+B w/ one of the more selective bootcamps (i.e., a bootcamp that requires some amount of programming experience to begin with, not lambda school) if I could do it over again.

Feel free to PM me if you wanna chat more.

Hey few months later any update OP?

I think were roughly the same ago, I've bounced back and forth a bunch of times via poker/programmer as income over last 10 years interested how things have played out.

Recently made career transition to data science. More details in this thread: https://forumserver.twoplustwo.com/1...areer-1553642/

Feel free to PM

I signed an offer for a data engineer position a couple weeks ago and I'll probably start in the next few weeks. I'm lucky I have some connections from poker -- one of them is taking a chance on me doing data stuff for his IT company.


Things I've started picking up so far: Linux, Python, MySQL, SQLite, Docker, AWS VMs, version control, web scraping.

I want to try handling some bigger data soon with Pyspark, Kubernetes, steaming data sources.

What has really struck me is how in the big picture of using data to improve business, building and tuning models is an insanely small part of the work. Data science is the sexy buzzword career, but software development, operations, data engineering, cloud solutions, UX and more all need more attention in the grand scheme of things.

It's easy to program in Python and SQL, but the bulk of my time has been spent trying to understand how and why everything fits together.

correction...it's not easy to program well in Python and SQL, but it has been easy for me to get to the point where I can get things done.

I recommend leetcode -- I try to do problems every day.

update

still waiting on secret security clearance 6 months after submitting my eQIP. recently a friend told me his wife's took a year, hopefully that doesn't happen.

in the meantime I've been coding, watching youtube vids, listening to podcasts. it's been about 9 months since I started my first beginner Python course. here's where I'd put my skills:

Python, SQL - Intermediate
Unix/Bash, AWS, Docker, Airflow - Advanced beginner I guess

things are getting pretty fun. I can now do stuff like...make a web scraper and put it on a schedule, dockerize it, run containers on EC2 instances, dump results into a database and do queries/viz on it.

still much work to be done

Congrats on the transition from poker. It is not easy for sure! You've already made the important realization that engineering and execution than developing pie in the sky models.

Long term look to transition from data engineering to general software engineering by slowing widening your skill set on the systems level. Since SE is more general and has higher pay and growth prospects long term than being a specialist.

I hope it's ok to post this here, I couldn't see anywhere else. I'm in somewhat the same universe as OP but am a bit closer to applying for Junior positions.

Is there somewhere here I can post requesting a tutor for ASP. Net framework? Learning it on my own has been a bit of a grind.

Thanks and I appreciate the advice.

I started working (data engineer) about a month ago. I'm just now getting fully set up with the necessary accesses and workspace, so starting now I will get some hands-on experience deploying pipelines to clusters and things of that nature.

I'm still in the batter's box in the top of the first in terms of career, but I think at this point I can give an important tip to people looking to transition into any programming related field...


Do cool projects and show them to people. Blog, tweet, get involved in communities and sub-communities, talk to friends about what you're doing. Immerse yourself if possible.

What I've found is that in the professional world, especially for junior positions, it's not very meritocratic. You won't be making key decisions right off the bat, so if you have a reputation as a curious, conscientious hard worker, you'll look better than an awkward / difficult person with more refined skills.

Also, it's an order of magnitude or two easier to get an interview with a reference than without.

Yeah that is good Advice especially for people who have the time and passion in early career to dedicate side projects. Side projects during collage and in the first few years of my career really made a difference in expanding my skill set and being able to take on bigger things later on. I work with server engineers who have 10 years of industry experience and never had to design a database schema themselves.

I'm a former 'data scientist' and a current product manager at a major tech company in the US (and rec poker player ofc )

data scientist in quotes because I did more product work than deploy models etc

in our company, some of the top skills for DS include some subset of

* stats know how
* python experience
* linux skills
* cloud computing
* DevOps/ CICD / productionizing a solution
* on prem systems
* biz knowledge / industry chops (understanding common problems in an industry, using that to help other customers solve problems more quickly)

engineers might know more about

* deploying applications
* docker, containerization
* DevOps/ MLOps, CICD
* cluster management
* linux skills
* backend or frontend development frameworks, especially Javascript, Java/Scala, and C++
* cloud deployment patterns
* on prem systems


Overall there are many roles across data science, data engineering, and software engineering catering to many diverse skillsets. The key is to get your foot in the door, that's the major hurdle imo.

Also don't do bootcamps.