With my recent (Oct 2016) layoff from Dell Technologies, I’ve had some time to work on additional IT topics. According to several industry groups, Data Science is one of the top fields in the IT industry right now. So, I decided to learn what I could about it, and perhaps move into the field. I set up some goals and thoughts on what it might take to get me up to speed on the topic, short of going back to college and getting a masters degree.
Some of my goals and thoughts included:
- Find any online schooling (free preferred) that might be of use.
- Find a local “bootcamp” school that I could attend. This would get me integrated into the field quickly.
- Read, read, read… Buy books on Data Science and any supporting languages.
- Find a mentor or a Meetup group in the local area.
- Build a portfolio of programs, examples, etc. as a resume item.
- Put code and findings on my website: SQLSmarty.com
To date, I’ve done most of the six items listed above. I did enroll in online training through Coursera, an online site set up by a couple Stanford University professors. I looked into bootcamps, and found a local one, but the tuition was cost prohibitive. I ordered a whole library of books on Data Science [that I could understand] and the Python programming language. I will attend a Meetup group here in Austin this month. I have some code examples using Python, NumPy, and Pandas, but have not compiled and edited them to post on this site yet.
What I found through my reading and poking around on the Internet was that Python had some of the best API’s for Data Science [DS], and that it’s fairly easy to learn. Armed with that information, I logged on to Amazon and started buying books on DS and Python at an alarming rate. I also enrolled in a Python course through Coursera that is sponsored and taught by professors at the University of Michigan. These courses are certificate only, but it shows that you’ve done the work at least.
I installed Python (Anacoda) on my MacBook Pro, and off I went. First off, Python is a very cool language. Developed in the 1980’s, it is very much industrial strength now. It is an interpreted language, but it’s so easy to use and very powerful since it is optimized to do specific operations very quickly. It is Object-Oriented, and everything in Python is an object – no primatives. I started using Python when my first [of many] book arrived from Amazon. Within a couple days, I was enrolled in a Python course at the University of Michigan (Coursera) and was banging out code! Writing programs that would take two pages of C# code in about 10 lines of Python.
So, as I’ve moved into the DS portion of Python, I’ll be adding coding examples that will include a look at NumPy, and Pandas. Pandas is one of the most used data science API’s, and it’s free. Just import it and start using it! Plotting and charting the findings is also an important feature of data science. I’ll share some examples of Matplotlib to illustrate how easy it is to create a graphic of the results of your work. Again, with very few, but intuitive, lines of Python code.
So, check back soon as I will begin posting code examples to the new Python blog!
Knowledge is Power!