Hi all - I am a recent Industrial Engineering grad (~1 year removed) from West Virginia University. While in school, I always found myself fascinated with the statistics courses..but ended up getting an entry level role in manufacturing. That being said, I have mainly focused on data collection, mining & analysis (intermediate level in Excel & Access/SQL), as well as some traditional IE process improvement work.

For the most part, I have been disenchanted with what I have been learning, and am looking to transition myself into something where I can have a more rewarding career and use the skills that align mainly with my passions.

I have taken the time to learn (at a beginner level) how to code with R and am learning Python at the moment. Outside of linear/logistic regression, decision trees, confidence intervals, and more traditional statistics, what can I do to better position myself? Does it make sense to go back to school??

I tried starting by reviewing the data science cheat sheet, but I found myself leaving with more questions than answers. My end goal is to build analytics company with a few of my friends from school. My problem is that as of now, I have a high level idea of what it is I want to accomplish, but feel that I may lack the technical means to do so.

Thanks for your time.

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i will start the AI or mining but it will take extra time to learn new things in it.

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Hi, although 5 years have passed, I think that you still created a company with your friends from school. But if I returned to this forum in 2016, I would advise you if you want to study python such tips. There are many free online courses on the Internet (including video tutorials) that promise to learn Python from scratch. They allow you to get acquainted with the structure of language, master grammar, and understand its logic. But writing serious development in Python requires systematic training. This means reading thick (and expensive) programming books, technical documentation, or attending classes.
I deal with software for large business clients at the engineering marketplace Engre.co and I personally advise you not to forget about constant practice. An important step in learning Python is to use it in self-written applications and solve emerging applications, rather than copying from the screen the tasks suggested by the teacher.
But difficulties should not stop you, you really want to get a good profession with decent pay. After all, the specialty of Python developer, or application developer, is in demand in the labor market and its scope is growing.

In addition to R and Python, I'd also get familiar with visualization tools if you're not already. A lot of companies have free versions of their tools. Tableau Public is a popular one, another good one is visualize free.


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