Protip: job market data scientists & ML experts is on fire. If you find these topics interesting you should get into it now.
For undergrads this means CS, applied math, stats, etc. PhD can help but not required. If you have any quantitative PhD (e.g., engineering, even chemistry) there are "bootcamps" to bring you up to speed and place you in a data science role. Some are good, especially the more selective programs you don't have to pay for.
Source: I am hiring for two ML spots on the west coast and can't find anyone for <$100k and that's after offering lots of equity. As an extreme example, Google brain team is more like $450k/yr plus $4M in options over 4 years.
Anyone have any questions about this? Will answer.
>>7951004
studying to find a job is the most plebbiest thing you can do.
>>7951011
Studying to do something you find interesting AND that others highly value is out of reach for most people. This is one field where the demand so far exceeds the supply that you can solve really interesting problems and make a lot of money doing so.
Pretty much the opposite of plebeian.
I have a bachelor's in math, and I recently started to learn ML from Andrew Ng's videos. I really enjoy ML. It might have been enough for me to major in CS if I had learned about it 6 years ago. Where do I go from here? Internship, more CS and/or stats, job experience or what. How long will this kind of market last?
>>7951004
Will graduate in two years, filling up that position almost perfectly well.
Will the job market still be viable then?
For data science see pic. For ML see link.
https://www.metacademy.org/roadmaps/
>>7951077
lel I almost perfectly match the skill set needed for data science, and I'm undergrad.
>Bayesian learning
>Stat 1,2 and multivariate
>ML course
>Shitton of programming
>Dynamic system
>Differential geometry
>Concurrent and parallel programming
>All the analysis skills the world could ever dream of
Wheres my job?!
>>7951082
Also: signal processing and linear systems
How do I best prepare data for Decision Trees and/or Rule induction to be applied a text mining problem? I basically have two text fields, one something like a title and the other a description. Amount of words differs but can be normalized / discarded and used with a different model. I use Naive Bayes right now but would like something more graphical.
What kind of projects are you looking for in applicants? And how do you find a project to work on? Compsci student here, I took my school's data mining and machine learning courses, as well as some extra stats classes.
My city has a few open data sets that are interesting. I would classify the data and after that I just don't know what to do. Even after I plot the data, what do I do?
Thanks in advance.
>>7951004
>west coast
><$100k
YOU CHEAP ASSHOLE
>>7951082
>Wheres my job?!
whenever you stop being an entitled fag and get your lazy ass on the job market
>>7951045
Start applying for jobs with what you have. Get comfy with HR at interviewing companies, ask them what they want and they'll tell you what they're looking for. Ask them if you should get a masters in CS.
As for the market, its hard to tell. CS/Math/Tech fields are booming now. Hard to tell where demand will be in a couple years from now. Could be bust or boom, although most people are optimistic.
>>7951077
Let me confirm, is this pic saying that being self taught is enough to find a job in this field? Do I really not need a degree?
I'm graduating soon with a masters in financial mathematics, would that be enough background for a basic machine learning job? I haven't taken any actual classes in it, I've really only taken basic CS classes but I have an undergrad+grad major in math, I've taken a ton of stats and done a lot of scientific programming in C++/matlab/python. Like, I've never made a GUI, but I can program a numerical integrator or some complicated econometrics models.
I'm pretty sure I could self-study and learn the basics of ML in a couple months but would that be enough or would I actually need some kind of CS degree?
>>7952472
Should be good. I'm doing an internship right now working on text mining / document classification basically teaching myself the methods and approaches as well as using them in tools which offer them.
Some math goes over my head but in general it's a lot of rather simple equations; linear algebra and probability for a huge part.
Due to time constraints I am not implementing any algorithms myself but use a GUI based workflow to chain operators together alongside ETL via MySQL to ready data for further use.
>>7951082
You're also gonna need to learn about SQL/NoSQL databases, data warehousing, business intelligence (which isn't too difficult), and more importantly you're gonna need to get experience actually doing data science, since it's results driven; unlike your undergrad.
Hey OP you got a few more questions to answer here
I'm still finishing a cs/math degree, but would like to make some income in an entry position in this field. How legitimate do you think a coursera specialization is? - would you consider hiring me?
https://www.coursera.org/specializations/machine-learning?utm_medium=email&utm_source=marketing&utm_campaign=Tn2tcPE4EeWKKUvh_iZFtg
>>7951004
I don't understand why someone with a CS degree can't do this job.
>>7951082
>one course on ML
So a master in survey statistics or survey methodology would be a good thing?
>>7951004
Protip: don't let your AI learn on twitter
>>7951004
What's in it for me?
Not letting you go that easy, OP
>>7952454
I doubt it. If you want to go beyond canned software, the math for statistical learning gets pretty fucking wild.
Even if you want to stick with canned software, i'm convinced that you'd need at least an undergraduate background in linear algebra, calculus, and signal processing to know how to use it.
i have an unrelated bachelors degrees and dropped out while pursuing a master's in statistics.
im doing programming directly now, trying to create a portfolio.
did i fuck up?
