I love programming languages. I started a long time ago with BASIC for my Timex 1000. Then I moved on to Pascal, some Assembler, really began to take off in Java, and graduated with Python and Ruby. I have looked into different languages like C, Lisp, and Perl, but as my chores in the company I worked for kept me from learning more and dwelling deep, I started to leave than languages behind.
Late on 2014 I came back to code as my need to dwell on Data Analytics began to take off. I could do much of the Statistics in EXCEL, but I couldn’t pass the opportunity to add some geeky collaterals with code of my own. The analysis I started to make were difficult enough that I could explain IT why EXCEL wasn’t enough. First, I really needed to move big quantities of data, enough to render EXCEL useless. Second, I really needed to apply enough statistical formulas to make the IT people give up (most business programmers I know are terrible at math…)
That’s when I came across two new finds: the Python Pandas library and the R language. Pandas is big and frightening, but I found several R courses in www.udemy.com and decided for a free small seminar on R called R Basics – R Programming Language Introduction by Martin Heissenberger.
Martin is both a Scientist and a Biostatistician. Martin not only knows, he knows enough to make my data analysis for selling more shoes look like child play. Martin knows Statistics, and above all, he knows R from a Statistician point of view, not a programmer. And I say this because Mr. Martin gave me something precious: a programming language where the main challenge is not learning about control flow, classes and syntax, but rather about easily solving Statistic problems. It has been a first time for me. Dwelling into R opened a new panorama I had never explored before.
In case you never heard about R let me give you a quick intro blatantly copied from Wikipedia. R is a free software programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners for developing statistical software and data analysis.Polls and surveys of data miners are showing R’s popularity has increased substantially in recent years.
R is an implementation of the S programming language combined with lexical scoping semantics inspired by Scheme. S was created by John Chambers while at Bell Labs. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team, of which Chambers is a member. R is named partly after the first names of the first two R authors and partly as a play on the name of S.
R is a GNU project. The source code for the R software environment is written primarily in C, Fortran, and R. R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems. R uses a command line interface; however, several graphical user interfaces are available for use with R.
The course I would recommend lives here. Most free courses in ww.udemy.com are short versions of longer courses that you can breeze through in an hour to get a general feeling of the topic. This is not the case with R Basics. The course will not only get you started, but it is complete enough to put you behind R Visual Studio coding, solving data problems and generating graphs like a pro. The power of R is impressive. You can read a flat file with a million rows of data (something I can not do in Excel) and start analysing statistic formula in not time with simple commands. No need to highlight ranges. R knows intuitively what you want because the language operates like a mathematician would. You are analysing and solving data problems instead of fighting the language and reviewing where you misplaced a curly bracket (Java) or left a missing white space (Python). You are treating data frame structures and looking for null hypothesis instead of trying to get your collections to correctly iterate. If anything, you will be looking at your Statistics book, not your R manual.
The way Martin Heissenberger approaches teaching feels different. This is not a language teacher. He feels like a math teacher guiding you along this nifty tool called R to help you get much more done. After the 19 lessons you feel like you can accomplish a lot. Take a week to learn R with this tutorial and you could probably get some very sophisticated analysis done. Take a week to learn the powerful Pandas library and you will be wresting the Python language underneath. You can learn Python in a week, but you will not master Python in a week. Python is strong and well suited for math, but it is a broader language. Everything in R feels so much more focused.
I have now moved to a new course by Martin called R Level 1 – Data Analytics with R. This is an in-depth walkthrough of the language, with many hours of lecture and a bit more complicated. It goes beyond Statistics and into more general math topics, such as matrixes and the like. It also involves functions, control flow and other generalities of R. It’s the kind of web seminar you take over the course of several weekends, but so far I am having a blast getting a lot done and learning at a much faster pace than other languages. R is winning over my heart and Martin Heissenberger has a lot to do with it. He not only teaches a subject, he teaches purpose, and that has made R much more accessible and easy take on from the very beginning.
Do yourself a favour a visit Martin’s course at https://www.udemy.com/r-basics/#/
I guarantee you, you will not be disappointed.