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The last few months have been an adjustment learning about data analytics. Data analytics is the science of reviewing raw data and making conclusions from the information. There are 3 programming languages often at the heart of data science: Python, R, and a SQL variant. By combining these three elements together, a person creates data visuals that can tell the story from raw data. I am in the process of becoming a data analyst.
Data analysts and statisticians fall into the realm of data science. However there are strong differences between the two professions. A data analyst is a jack of all trades, whereas the statistician is a dedicated mathematical specialist. A data analyst helps develop the possible hypothesis, and the statistician confirms the hypothesis.
I enrolled in a local college and took a Python course. I passed it as a requirement for graduate school enrollment. I used an openSUSE laptop and passed the class. openSUSE did a great job of supporting my Python programming and learning.
Shortly thereafter I started graduate school and began learning R. The graduate class was poorly taught, and I eventually left it. Some of the difficulties included learning a new language, and the fact that R is more Debian friendly. Many times, the support package libraries for R under openSUSE were tedious to install.
Yet I wanted to continue on my path in data analytics. I enrolled in the Google Data Analytics Course. This process is entirely online. It allows me to continue my new professional development, while I find a better graduate school. I nuked my laptop and installed Ubuntu MATE onto it. And many of the issues I had with R under openSUSE were erased with Ubuntu MATE.
There are various tools utilized in the Google Data Analytics Course. Most of these tools are Windows or Mac friendly, and a few are cloud based. However I do not see many open source GUI based applications for data science. The two most popular options are Microsoft Power BI and Tableau. I have seen KST as being an option.
So exactly what is the point of this article? To find open source versions for data analytics.