Post #7- Data Analytics

     Welcome to this published post and please enjoy and respond to any questions that may come up in your head? Data analytics is newer than some of the other IT positions you may see on job boards. It is gaining popularity and the pay scale is high enough to motivate yourself to learn. Data analytics is the process of analyzing raw data to find trends and answer questions. It has a broad scope across the field. This process includes many different techniques and goals that can shift from industry to industry (Master's in Data Science). 

     This tech topic correlates to the class at UAGC called TEC101 for the large scope of reading material the class provides. A testout book regarding IT fundamentals and a comprehensive, 11-chapter, Zybooks digital textbook that covers everything from the history of hardware and software to ip addresses and the way to ping them using a command prompt. The class material covers some data as well that can be associated with data analytics. However, data analytics is a form of data science and is more more detailed than what is shown in the class.

     In order to use data analysis, you will need a PC or laptop with recommended processors. If you are using a Mac, shoot for an m1 or m2 processor, but Windows and Linux for advanced users can also manipulate data as a data analyst. Windows is the preferred choice if you want to take a boot camp or learn via Coursera or other MOOC due to feedback from current or formers students and analysts in the field.

     Data analytics uses a few programming languages. One of the most important skills for a data analyst is proficiency in a programming language. Data analysts use SQL (Structured Query Language) to communicate with databases, but when it comes to cleaning, manipulating, analyzing, and visualizing data, you’re looking at either Python or R (Coursera staff). It is about choosing what is the most efficient program to use when analyzing and manipulating data. They are both fairly easy languages to learn. 

     Database management systems organize and optimize data storage and can be used for meaningful insights. It is similar to what you do when using the programming languages mentioned above. And this plays a critical role in maintaining integrity and security. Similar to the AAA that the Tec101 class Zybooks discussed in the security chapter.

     If I were to get into the field and may end up doing it on top of other IT fields. The first thing I would do is immerse myself in research the topic much more than I already have. Then, I would complete the 9-course IBM data analytics Coursera course and post that to Linkedin and include it on my resume(s). Much of what was learned in network infrastructure and security is going to spill out in Data analytics. It is not uncommon to secure the network and R and Python before analyzing any company data. 

References:

Master's in Data Science. What is Data Analytics. https://www.mastersindatascience.org/learning/what-is-data-analytics/

Coursera Staff. April 03, 2024. Python or R for Data Analysis: Which Should I Learn? Coursera.org. https://www.coursera.org/articles/python-or-r-for-data-analysis