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how does one accurately make predictions without ability to build models?
Do you think the relationship between data scientist and statistician is like that between engineer and physicist, or programmer and computer scientist?
Also, would you prefer to hire someone who has a masters in Statistics over someone who has a masters in data science, even though you are a data science manager, and are trying to find people to fill the job title of "data scientist", or would you think the person with MS in statistics is over qualified?
a. I don't understand your first question. With limited data or no data, one can still make judgmental forecasts/predictions based on that. One can also do some sort of optimization to make predictions. IDK, just not sure what you are asking here
b. I'd say that in general, the distinction between the two is this.
DS: usually working on data warehousing and analytics, perhaps some statistical modeling (MOST ds folks are not doing ML, just number crunching and dashboard development.) these roles do tend to be a lot more technical and often require knowledge of Python
STAT: usually working on the application of statistics to solve problems, be it through hypothesis testing, clustering, regression, etc. They still use things like SQL and R/Python, but these roles are less technical
c. I am not a fan of MS programs in DS and have nothing good to say about them. WITH THAT SAID, I would note that grads from those programs are getting hired. Ultimately, I'd suggest a stats degree.
a. I don't understand your first question. With limited data or no data, one can still make judgmental forecasts/predictions based on that. One can also do some sort of optimization to make predictions. IDK, just not sure what you are asking here
b. I'd say that in general, the distinction between the two is this.
DS: usually working on data warehousing and analytics, perhaps some statistical modeling (MOST ds folks are not doing ML, just number crunching and dashboard development.) these roles do tend to be a lot more technical and often require knowledge of Python
STAT: usually working on the application of statistics to solve problems, be it through hypothesis testing, clustering, regression, etc. They still use things like SQL and R/Python, but these roles are less technical
c. I am not a fan of MS programs in DS and have nothing good to say about them. WITH THAT SAID, I would note that grads from those programs are getting hired. Ultimately, I'd suggest a stats degree.
thank you so much, Tonym! I really appreciate your help. if you don't mind, what was your education and work background before becoming data science manager? it seems like something i'd like to do when i grow up.
thank you so much, Tonym! I really appreciate your help. if you don't mind, what was your education and work background before becoming data science manager? it seems like something i'd like to do when i grow up.
I studied Econ and Poli Sci as an undergrad, and then did a masters in Quantitative Methods in Social Science program. Came out of school and worked as a statistician, then as a econometrician. Then moved to the Bay Area for my current role. Next role, which I'll be finalizing soon will be a Sr Manager of Business Insights or Manager of Data Science.
If you don't mind, in which year did you graduate? Was the uni you attended very prestigious? What was your job position after graduation?
My answer is neither. A much better choice would be a data mining or busines intelligence program offered by the College of Business. For example, U. of South Florida has a program for business intelligence offered by the College of Business. I saw that when I was still in FL. I am now in AZ. ASU has a similar program offered by the College of Business.
If you go into the statistics department your courses will be heavily mathematical and likely take you out of your comfort zone. You most likely will have many math majors in the courses. By contrast, the business college fits your undergraduate major nicely by offering BI topics in statistics with an economic context.
As for me, I have an MS in Software Engineering and work in data test integration for Data Warehousing. I used Informatica, SQL Server, MySQL, SAP ECC, and RESTful services written in node.js for business intelligence. The web app using BI increased site revenue by a huge amount - very huge. It's very profitable. So data mining is growing rapidly. Supply does not meet demand. Go for it.
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