Introduction: Welcome to Data Analytics

I would like to welcome you to Data Analytics, this blog is dedicated on the basics and "how to's" in the science of Data Analytics. Beforehand, several examples and exercises in this blog is analyzed using free software packages like R Language, Gitbash and GitHub as well as commercial softwares like Tableau, SPSS, SPSS Modeler, STATA and SAS. So I suggest you also download them.

So let's begin our introduction. What is Data Analytics? There are two words here first is data and next is analytics. Data is a type of information measured either as a quantitative variable (objective) or a qualitative statement (subjective). Analytics, on the otherhand, is the discovery of meaningful patterns. Therefore, Data Analytics is the discovery and communication of meaningful patterns in data.

Data Analytics rely on the the applications of statistics and computer programming as well as applied research to measure performance and improve processes, the result are then communicated in the form of reports and visualizations. Data Analytics can be applied in businesses, manufacturing, economics and quality control and almost all events in life where data is involved, even in cooking and going to school.

Say for example, business companies today often are faced with the challenges of solving and analyzing complex and massive sets of data, to add to the complications, these data are not constant and changes with time, these data also comes in very large volumes. To solve this problem some enterprises formed a separate Data Analytics team to address this issue, this team is an essential part in the transformation of the company into a more competitive business unit. Mathematical and statistical tools like price and promotion modeling, consumer brand loyalty modeling, sales force sizing and optimization, market mix modeling, marketing optimization, stock keeping optimization, store assortment and enterprise decision management,among others, have leverage company competitiveness by optimizing process units.

Other problems that are faced by business companies and other institutions like the governments and healthcare systems, is unstructured "messy" data. these are data that comes from a wide variety of formats that cannot be stored by traditional databases. Unstructured data includes emails, pdf, GIS data and a lot more. These type of data requires a complex type of analytical tool to perform full text search, conduct meta-analysis and even perform a comparative analysis of different data from different data sources.

Presented with these challenges Data Analytics is undoubtedly the most useful and innovative tool to solve issues regarding data. Data Analytics can help improve companies, optimize work and processes, expand innovation and lastly help us in our daily lives by providing a space where useful application can be extracted from seemingly complex data.