I have found the semantics in the information technology field to be quite confusing. When I was in college, my university had several computing majors like computer science, information technology, information systems, and human-computer interaction among others. However, when people talk about career paths for students in "IT" (Information Technology), they are often not just referring to people who specifically majored in "information technology," but rather the greater realm of "computing" majors like the ones mentioned above and many others.
Within the information technology field, many articles and people are saying that "big data" skills are in high demand right now. There are a few different positions that I have read about that relate to a career with "big data": data scientist, data engineer, data architect, and some (but definitely not all) software engineering roles.
How does anyone choose a specific path? What are the differences between the different roles?
I came across an article in Mashable this morning that I thought helped clear up the difference between data science and data engineering, but then I got confused again.
The article, if you have not read it, discusses five lucrative tech careers to pursue this year. Numbers one and two are data scientist and data engineer, respectively. Since they are listed explicitly separately in this article, I thought the descriptions would shed some light on the differences between the two roles.
The descriptions did help me understand the differences, or so I thought. It seems that data scientists look to find meaning from data while data engineers look to format data so other people can analyze it. The data scientist position mentioned tools like Python, R, and SQL which aligned with some past job descriptions I had read. The data engineering position mentioned Hadoop, MapReduce, and Storm. I have seen Hadoop and MapReduce mentioned in some jobs that relate to big data so I had hoped that these buzzwords could help me distinguish between data engineering and data science roles in the future.
Unfortunately, I got confused again when I clicked on the infographic link mentioned in the data scientist description. This infographic explains how to become a data scientist, but mentions Hadoop and MapReduce in the fifth step of becoming a data scientist.
I have heard from many people that there are some areas of overlap and some things that are connected throughout the different areas of information technology so I understand that the skills are relevant to both positions, but this certainly does not make the semantics any easier!
If semantics confuse you, you are not alone!
Thanks for reading!
Within the information technology field, many articles and people are saying that "big data" skills are in high demand right now. There are a few different positions that I have read about that relate to a career with "big data": data scientist, data engineer, data architect, and some (but definitely not all) software engineering roles.
How does anyone choose a specific path? What are the differences between the different roles?
I came across an article in Mashable this morning that I thought helped clear up the difference between data science and data engineering, but then I got confused again.
The article, if you have not read it, discusses five lucrative tech careers to pursue this year. Numbers one and two are data scientist and data engineer, respectively. Since they are listed explicitly separately in this article, I thought the descriptions would shed some light on the differences between the two roles.
The descriptions did help me understand the differences, or so I thought. It seems that data scientists look to find meaning from data while data engineers look to format data so other people can analyze it. The data scientist position mentioned tools like Python, R, and SQL which aligned with some past job descriptions I had read. The data engineering position mentioned Hadoop, MapReduce, and Storm. I have seen Hadoop and MapReduce mentioned in some jobs that relate to big data so I had hoped that these buzzwords could help me distinguish between data engineering and data science roles in the future.
Unfortunately, I got confused again when I clicked on the infographic link mentioned in the data scientist description. This infographic explains how to become a data scientist, but mentions Hadoop and MapReduce in the fifth step of becoming a data scientist.
I have heard from many people that there are some areas of overlap and some things that are connected throughout the different areas of information technology so I understand that the skills are relevant to both positions, but this certainly does not make the semantics any easier!
If semantics confuse you, you are not alone!
Thanks for reading!
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