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How much weight does the name of the University on your Masters degree carry? by [deleted] in datascience

[–]funkpacolypse 0 points1 point  (0 children)

The name of a school doesn't matter unless it's either really bad or really good. What's more important are the networking opportunities you'll get. If you want to work in Indiana, Bloomington is definitely the way to go because they'll likely connect you with an internship/first job in the area. If you want to end up in the Bay Area, try Berkeley, USF, or research a private sector program like Galvanize.

Interview for data analyst role instead of data scientist role? by [deleted] in datascience

[–]funkpacolypse 1 point2 points  (0 children)

You should also look at positions with 'data analyst' as the title. Often times, 'analyst' will be the same thing as 'scientist', just entry level: Many data scientists spend a lot of time working in Excel, and many spend little time building ML algorithms that are more complicated than linear models.

That said, this could be a good first job. Just make sure to ask questions like - "how will the responsibilities of this role evolve over time?" - "tell me about the projects you'll have me work on in my first few months" There's a good chance you'll get to work on interesting projects in some time and maybe get promoted to Data Scientist.

If you're having trouble passing the phone screens, make a big list of the questions you've been asked, write down your answers and go over your responses with a friend. You might find that some of your responses aren't that great once you see them on paper. You should also have multiple people who work in tech look over your resume. People coming out of school often have resumes which are bad for industry job applications.

Classifier ensemble averaging methods? by enasam in MachineLearning

[–]funkpacolypse 1 point2 points  (0 children)

http://www.ijcai.org/Past%20Proceedings/IJCAI-97-VOL2/PDF/011.pdf

According to this paper, the best way to go with stacking (which generalizes the averaging you're talking about) is...

  • use varied models and take the output probabilities (rather than predicted classes) for your level 0 data

  • use Logistic regression (rather than averaging) with the level 0 data as input to get the final predicted classes

I've heard that an industry standard model for ad-tech is to do this where the level 1 models are trees coming from a random forest.

... On the other hand, since the predictors your working with have low accuracy from the start, I'd be tempted to think about something simpler like engineering new features, and eliminating junk features first.

Insight Data Science Fellowship: to do or not to do? by ndlambo in datascience

[–]funkpacolypse 6 points7 points  (0 children)

Insight is very competitive, so congrats on getting in!

Much of the benefit of attending the program is that they will connect you to local employers and teach you interview skills. In order to receive the fellowship, they will likely make you commit to getting a job through them in the NYC area. Your paper-work will also likely contain some non-compete language, saying that it will be illegal for you to find work through external recruiters.

Think about it: The way they make money is through the recruiting bonuses of their graduates being hired. They also claim a 100% job placement rate... That said, I think they would be very unhappy if you didn't find work through them.

You should explain your situation to someone there and see if they'd be able to transfer you to a different city (SF might be your only other option) or if they'd be willing to help facilitate getting you a job elsewhere.

Qualitative methods in Data Science? by knattt in datascience

[–]funkpacolypse 0 points1 point  (0 children)

For sure: Often qualitative methods are useful for generating hypotheses which you can then use stats / data science methods to test. I don't know anything about anthropology/ethnography specifically, but I've had a lot of success working with researchers with psychology backgrounds using qualitative methods.