Data Analytics
In the Fall of 2013, I took a Data Analytics course with Professor Zhewei Zhang. In this class, we learned about various concepts such as exploring various analytical approaches and applying these approaches to business scenarios, using tools such as My SQL and SAS Enterprise Miner. Using pivot tables in Microsoft Excel and performing extract, transform, and load (ETL) functions are the concepts that I found to be useful. Pivot tables are a data summarization tool that can be created and easily altered by dragging and dropping fields into rows, columns, filters and values. Pivot tables are very useful for aggregating large data sets into an easy-to-read format, while calculating multiple functions at a time. Pivot Tables are useful in business because it provides you with summarizations of data, so you are able to simply communicate the important information with others, regardless of the size of the data set. The other concept from this class that I found to be very useful is learning basic Excel extract, transform, and load (ETL) functions. This process involves extracting data from an operational data store, transforming the data into an analysis-ready format and loading it into an analytical data store. The “transforming” step in the ETL process is very important because often when information from several different transactional databases are combined, many problems can arise, such as redundant data. The solution for this is creating a “single view” of the data, which means that the entire organization understands a unit of data in the same way, which is both a business goal and a technological goal. This is applicable in a business setting considering data is often a company’s most valuable asset, and if the data isn’t formatted uniformally, you won’t be able to analyze it correctly.