From the course: Introduction to Career Skills in Data Analytics

Defining data analysis and roles in data analysis

From the course: Introduction to Career Skills in Data Analytics

Defining data analysis and roles in data analysis

“

- One of the challenges we face as we decide to pursue data as a career choice is the fact that there are many different paths and specializations. Let's define some of those roles, and then discuss the common skills that are shared among all. The most universal role is that of the data worker. This person consumes data regularly. Works with data often. Performs some data manipulation, and presents that data as a part of their everyday work. Let's take Sally for an example. She works in the business unit, and not necessarily the IT department, and each week she prepares a report for her manager. She'll then prepare the data for the reports, and her reports are the same as last week, the only difference is new data. You see most data workers have limited access to all the different systems through the backend. They likely receive data from people who have access to the databases. Data workers like Sally may even export the data out of a system into CSV or Excel files, and the process of their data work begins. A data analyst goes further. Generally, they might have a little more access to data, model the data, and have connected to the data, so they can just refresh the reports, and begin the analysis, and the presentation of the data. The data analyst will handle a lot of ad hoc requests, and especially if they're efficient. They will likely have more than just Excel to work with and are likely considered a guru or a wizard in their department. The data worker and the data analyst are what I consider the largest amount of roles available. Most people are some form of data worker, and they dive into data analysis more than they even know. The common skills of all data professionals are gathering data. manipulating it to me requirements, and then reporting the outcomes in some way. Data engineers take a special skill of being able to build and design data sets. Whereas a data worker, and the data analysts will work with what is already built, and model the data as needed. You will find a lot of people in crossover roles where sometimes they play data engineers, and sometimes they act as a data analyst. One could argue on the top of the hierarchy of data roles are the data architect, and the data scientist. A data architect is a creator of architecture, no different than an architect that designs a building. The data architect designs data systems. The importance of the architecture really can't be understated as all roles including the data scientist needs this architecture. What most see as the literal top of the hierarchy is the data scientist. And I believe this is likely due to the fact that most companies have their data architecture in place, and now it's time to take that data, and put it to use. This is where the data scientist comes into play. A data scientist will likely have all the common skills of the data analyst, the data engineer and the data architect. They'll also have deeper skills in coding, statistics and math. It's okay to not know where you'll end on your journey, but I think it's important to start. You can begin either as a data worker or recognizing you already are one. You can increase your skills as a data analyst, and then as you grow deeper in your experience with data, you'll discover where you want to be. In all roles, you'll gain a deeper understanding of data, and it's okay to find a place and stay there.

Contents