Why Data Science May NOT be the Right Career Option for You
“Big data is like teenage sex; everyone talks about it, nobody knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.” — Dan Ariely
Yes, the field of Data Science does have a lot of potential for the future, promises a fat salary, and has been often referred to as the “sexiest job of the 21st century” in certain circles, but that does not automatically mean it is the best choice you can go for without giving it any thought whatsoever.
While it is true that Data Science constructs an environment that facilitates learning and performance hand in hand (you can learn while on the job), most of its takers are allured into the field of work by its glamour factor and the financial benefits that it advertises. That, in turn, contributes to the widening gap between personnel quantity and appropriate quality. In other words, most employees lack the suitable skills for the work they are hired for.
Data Science being a relatively new field of study encourages the absorption of skills running parallel to performance for practitioners. But that leeway gives way to its very own loophole. A Data Scientist is expected to possess a specific set of skills, and while one with a limited number of them can acquire the rest on the job, the lack of filtering during employment increases the risk of hiring someone without any of them. For better elaboration, the complete set of said skills are as follows:
- 1) High-Level Mathematics
- 2) Advanced Statistics
- 3) SQL / NoSQL
- 4) Social Media Mining
- 5) Coding in R
- 6) Coding in Python
- 7) Coding in C++
- 8) Coding in Java
- 9) Natural Language Processing
- 10) Machine Learning algorithms
- 11) Data Visualization
- 12) Microsoft Excel
- 13) Teamwork
Of course, the above drawbacks do not apply to you if you had Data Science set as your target or destination from the get-go. Because, in that case, you would already be gifted with most if not all of the aforementioned skills. But even if you do, you must be prepared to acknowledge the fact that everyone who applies for it cannot become a Data Scientist or Analyst. You may be burdened with less exciting tasks such as writing, documenting, reporting, and delivering presentations, or stuck with repeatedly explaining the basics of your models or techniques, administrative overhead, project management, etc. to the stakeholders.
Even if you go technical, it may feel like the time frame provided for performing research is perpetually short.
While that may be true, not conforming to it would mean you are unsuitable for this field. That said, it is not just individual factors that determine your compatibility with Data Science (or the lack thereof). There are quite a few external factors that come into play as well. Life often surprises us, and not in a good way.
1. Fiction does not complement facts, expectation often disappoints reality.
One of the biggest issues that arise with Data Scientists coming in with an expectation is when all their hopes are dashed. Since Data Science is a vast subject, preparing yourself to excel at one specific task often backfires. For example, the Data Scientist who arrives with the clear intention of writing smart machine learning algorithms for driving insight would no doubt be extremely disappointed when asked to create analytic reports or sort out the data infrastructure. The lack of filters at the time of hiring implies there are high chances the ultimate goals of the employee may not align with that of the company. That eventually drives the former to look for a different job, resulting in a dip in the latter’s efficiency.
2. The isolation paradox: Is collaboration counter-productive?
Given the proper exposure, each Data Scientist grows to become an industry expert in their given area. One might excel at using Hadoop while another may be brilliant at writing machine learning algorithms using Neo4J. Everyone’s work has its signature. Since some companies focus more on overall efficiency than individual contributions, they team Data Scientists up and assign a task to the group. While that may result in brilliance at times, it is not a default result. This simply means as a Data Scientist looking for employment your primary priority should be sorting companies by their workflow and goals and finding the one that goes best with you.
3. Fasten your seatbelts, since workplace politics may not agree with you.
As mentioned before, Data Science is a relatively new field of study. There is a good chance even your superiors may not be knowledgeable on the subject. You may excel at what you do, but recognition would not be easy to come by without a little social bribing. To climb the career ladder, you have to make sure that those with the highest amount of clout perceive you as worthy. In other words, as frustrating as it may seem, you have to speak their language i.e. do simple projects, get numbers from a database to give to the right people at the right time, etc.
As nice as the term “Data Scientist” may sound, not a lot of people can wrap their heads around what it entails. From queries on databases to analytics reporting, you will be the one everyone approaches. As if that wasn’t enough, you will be expected to have the solutions as well.
This means you will need to keep a good rapport with your team members, not only to meet the expectations you are burdened with but also because one of them will no doubt have the answers to the above queries.
Message from the Author
If the above is not true for you and you want to pursue a career in Data Science, we at Learners Point Academy offer a handful of Data Science courses in Dubai that include Data Science Foundation, Python for Data Science, Data Science for Managers, and Data Science with R.
Learners Point Academy is a KHDA and ISO 9001:2015 accredited exam and training centre in Dubai, UAE.
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