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Seven Computer Science Jobs that Aren’t Coding Heavy!

April 19, 2022
                 Do not worry, you’re not alone! Graphic drawn by Devi Connor.

Are you majoring in computer science or already have a degree in computer science, but find yourself not passionate about coding 8 hours a day for the rest of your career? Do you crave more other than software coding, such as using your people skills or artistic skills? Well, look no further! I have researched about 7 different jobs that you, as a major in computer science, can apply for that are not coding-heavy. This is definitely not an exhaustive list of all the jobs you can apply to as a computer science major that aren’t coding heavy, but it’s just enough to get you thinking of all the endless options out there! Tell your other computer science friends who also feel the same way as you do! Your options are never limited. Your skills are so versatile, take a look at some of the jobs below.

1) Project Manager

Do you like planning ahead and prefer to be a leader in group projects? A project manager job might just be what you’re looking for! Project managers plan, execute, and complete projects by overseeing team members (such as a group of computer scientists) to make sure that they are performing their duties sufficiently. Project managers formulate benchmarks and deadlines for their team to complete, and work to meet regulatory and company standards.

  • Average Annual Salary in U.S: $88,907

 

2) IT Support Specialist 

IT Support Specialists are responsible for responding to technical issues and they work with IT departments, computer manufacturers (such as Dell or Apple), and retail stores. They assist customers and employees with software, network, and hardware issues.

  • Average Annual Salary in U.S: $58,332

 

3) Graphic Designer

If you have a talent or passion for visual design, graphic designing might be for you! Graphic designers are responsible for using design software such as (e.g: Adobe Photoshop or GIMP) to create logos, images, and other visual representations for clients and various companies. You may be responsible for creating web page designs, logos, and flyers.

  • Average Annual Salary in U.S: $47,334

 

4) Technical Recruiter

Technical recruiters utilize their people skills to recruit, interview, and present potential employees for IT positions. Technical recruiters have strong communication and interviewing skills, and are knowledgeable of the fundamental responsibilities of certain IT positions that candidates may apply for. 

  • Average Annual Salary in U.S: $56,747

 

5) Data Analyst

Are you a detail-oriented person? Well, a data analyst might be the job for you! Data analysts analyze data sets and identify departmental trends. They use data software along with market research and statistic to report their findings and predications to shareholders and executives to help guide their decision-making.

  • Average Annual Salary in U.S: $70,353

 

6) Technical Writers

If you like writing, this job is suited for you! Technical writers are responsible for writing technical materials such as resource guides, user manuals, and publications. Technical writers must possess a deep knowledge of their industry, and must know certain writing guidelines, grammar, and spelling.

  • Average Annual Salary in U.S: $68,555

 

7) Software Testing Engineer

As a software test engineer, you will be responsible for creating the methodologies used for testing software programs and conducting automated and manual tests to find bugs and determine software output. You will run reports and analyze the testing results to make recommendations for improvements on the software.

  • Average Annual Salary in U.S: $ 87,110

 

Sources:

https://www.glassdoor.com/Salaries/index.htm 

https://www.indeed.com/career-advice/finding-a-job/computer-science-jobs-without-coding 

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About Author

Devi is an undergraduate at Georgia Tech studying computer science. In her free time, she enjoys jogging, cooking and baking new things, jamming to vinyls and music, and gazing at the stars. Devi also loves cats!! (meow)

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  Timothy Durham was convicted of rape when a test showed his DNA genotype matched the DNA recovered from the crime scene. Eye witnesses testified that Durham was in a different state at the time of the incident, but he wasn’t exonerated from his 3000 year sentence until follow up DNA analysis showed that the preliminary forensic analysis used in the trial was misinterpreted (Thomas et al.). DNA testing serves an invaluable part in our criminal justice system, but it comes with complications and bias that can lead to wrongful convictions and misinformed juries.  

Background:

Forensic genetics aims to identify the origin of a biological sample. It can be traced back to Karl Landsteiner who discovered human ABO blood groups. With blood groups, suspects could be eliminated based on their blood type. For example, if a suspect had type A blood and there was type B blood left at the scene by the perpetrator, the investigators could rule out the suspect. Unfortunately, the reverse didn’t work. If the suspect and perpetrator both had the same blood type, it can’t be concluded that the suspect was guilty. Blood identifiers evolved into 8 distinct categories which improved the ability to eliminate suspects, but could only conclude that (if the system matched) it might be the perpetrator or this person might be related to the perpetrator (Jobling and Gill). The field evolved in the 1980s with the discovery of Minisatellites, lines of repetitive DNA that don’t encode proteins. Minisatellite patterns in DNA create what we know today as the DNA fingerprint, and it has a much higher accuracy rate than previous methods (Augustyn et al.). Around the same time, a breakthrough for victims of sexual assault was discovered. In the case of assault, bodily fluids of both the victim and the assaulter can get mixed. A process called differential lysis was created which could selectively increase the concentration of sperm in a fluid sample thereby allowing for accurate DNA testing (Jobling and Gill). Forensics evolved to use single-locus probe testing after several years because of its ease of interpretation. It looked at specific minisatellites that had high variability between subjects. This was the first DNA-based method to be used in a criminal investigation. Two murder homicides from several years apart were connected using DNA evidence. The evidence was then used to exonerate an innocent man, and the guilty party was found when he tried to evade mandatory DNA testing for all men in the small town. His guilt was then reinforced when the DNA profiles matched, and he gave a confession (Jobling and Gill). Finding DNA at a crime scene doesn’t guarantee that you’ll find a murderer. There are issues with non-pure samples and limited samples that make DNA work difficult, and accreditation of facilities and new techniques and take time (Jobling and Gill).  

Subjectivity of DNA Interpretation:

In a 2009 publication, the National Academy of Science discusses the possibility for subjective influence in lab based forensics. The interpretation bias happens in complex cases where, for example, there could be a mixture of DNA. A study published in 2011 looked specifically at this issue. A gang rape happened in Georgia, and the mixture in DNA from the case was used to prosecute one suspect after another suspect took a plea bargain to testify against him. With DNA mixtures, there are three possible conclusions: suspect excluded, suspect cannot be excluded, and inconclusive. If the suspect is excluded, it means that there’s little to no possibility that his DNA is part of the sample. If the suspect cannot be excluded, it means that there’s a possibility that his DNA is part of the sample. In the trial, the DNA examiners who knew the context of the case concluded that the suspect being prosecuted could not be excluded. The 2011 study gave the DNA evidence from this case and the DNA profiles of the suspects to 17 forensic examiners. These examiners had no context of the case, and they all worked independently. Of the 17, only 1 came to the same conclusion (suspect cannot be excluded) as the examiners who knew the context of the case. Twelve of the 17 concluded that the suspect who was prosecuted and convicted had no relation to the DNA found in the sample. Not only does this show that subjectivity occurs due to the context of the case, but the 17 examiners came up with different findings based on their past experiences meaning that not all DNA analysis is objective. Some organizations like the Scientific Working Group on DNA Analysis Methods say that quantitative statistical evidence should be presented with every categorical conclusion, but the US doesn’t require this (Dror and Hampikian).

False Positives:

There are two categories that cause wrongful identification in a DNA report: coincidental matches and false positives. Coincidental matches are DNA matches between two unrelated people. The probability of these matches is about 1 to 500,000 for white individuals and about 1 to 350,000 for black individuals. This small probability of coincidental matches makes people feel confident in DNA evidence. False positives, on the other hand, are matches between DNA and test DNA that result from issues in the forensics lab. There have been multiple cases of forensics labs accidentally switching victim and suspected rapist DNA which lead to convictions that were later overturned when the lab realized the error. There are also cases of misreading or misunderstanding a sample. In a case from 1993, a man was convicted of rape because someone interpreted a mixed sample of victim and perpetrator DNA as an individual sample. The conviction was again overturned. To prevent false convictions, retesting DNA is allowed, but it’s not an efficient solution. Many people are pushing for statistical information on the probability of false positives, so that the jurors can make accurate judgements based on DNA evidence (Thomas et al.).  

Conclusion:

DNA analysis is a useful tool for the criminal justice system, but subjectivity can be an issue in forensic testing. To combat wrongful convictions, there are organizations like The Innocence Project who use DNA testing to free the innocent, but reformed forensic testing standards should be put in place to prevent subjectivity and to ensure juries are correctly informed during trials. https://www.innocenceproject.org/    

Citation:

Augustyn , Adam, et al. “DNA Finerprinting.” Encyclopedia Britannica , 26 Apr. 2018, www.britannica.com/science/DNA-fingerprinting#ref153186. Dror, Itiel E., and Greg Hampikian. “Subjectivity and Bias in Forensic DNA Mixture Interpretation.” Science & Justice, vol. 51, no. 4, 2011, pp. 204–208., doi:10.1016/j.scijus.2011.08.004. Jobling, Mark A., and Peter Gill. “Encoded Evidence: DNA in Forensic Analysis.” Nature Reviews: Genetics, vol. 5, no. 10, Oct. 2004, pp. 739–751., doi:10.1038/nrg1455. Thompson, William C, et al. “How the Probability of a False Positive Affects the Value of DNA Evidence.” Journal of Forensic Sciences, vol. 48, no. 1, Jan. 2003, pp. 1–8., doi:10.1520/jfs2001171.