Pros & Cons Of Choosing A Career In Data Science

Pros and cons of data science career

The internet may be saturated with articles on why data science is the ‘sexiest job of the 21st century’, but little has been spoken about its possible cons. The draw of a career in this field is undeniable — it is in demand, pays well, and has a right mix of technology, statistics, and business. However, this field is massive and paradoxically, that comes with its own share of limitations.

This article seeks to provide you with the necessary insights about data science that will help you run a self-assessment and take the right course.

Pros Of Being A Data Scientist

In Demand

With 56% YoY job growth, data scientists have taken the top spot in LinkedIn’s analysis on the most promising jobs of our time. A study conducted by us had estimated that even within the larger analytics ecosystem, 70% of the job postings are for data scientists with less than five years of work experience. Also, with very few people having the required skill sets to become successful in this field, prospective job seekers have numerous opportunities. 

High pay

Data science is a highly lucrative career option, and this is attested by Glassdoor. According to the company, data scientists make an average of $113,309 a year. One of the reasons may be on account of the prestige attached to this role. Since it allows companies to make smarter, informed decisions, it gives them an important place in the company.


Data science is sector-agnostic and has numerous applications in various industries, including healthcare, banking, e-commerce, and marketing, among others. Therefore, you will not be tied to a specific business or function and can work in any field that uses data to drive decisions. For instance, the advent of machine learning (ML) signalled significant improvements in the healthcare sector, with one of the most significant applications being in detecting early-stage tumours.

Challenging Work

Data science combines a string of disciplines, including mathematics, statistics, computer programming, and strategy. And since technology is constantly evolving, it demands that you continuously learn new skills. Moreover, there is no one template that you can replicate from project to project. The problems data science addresses are quite varied, each of which demands new skills to solve.

Cons Of Being A Data Scientist

Vague Job Role

While it has become a buzzword over time, data science does not have a clear-cut definition. It is essentially the study of data, and that can involve the extraction, analysis, visualisation, etc. to create insights to help drive business decisions. Furthermore, it would also depend on the field that the company is specialising in. But what is certain is that all data scientists have to deal with a lot of raw data, which can be time-consuming. What is more, companies often provide data that is arbitrary, and this may not yield expected results.

Difficult To Master

As indicated above, data scientists have to work on large amounts of data to solve problems in businesses. This entails expertise in a long list of skills, including computer programming and software applications, statistics, data analysis, and data visualisation – and these are just the technical skills. Thus, it is far from possible to master each field and be equally proficient in all of them. While many online courses have been trying to offset this skill gap, it is still challenging given the immensity of the field. Which takes us to the next point.

1.Simplifying Technical Concepts

For all the skills that you have acquired to accomplish your work, it is all for nought if you cannot communicate your findings to stakeholders in a manner that is comprehensible to them. Explaining technical concepts to a non-technical audience is a key challenge for most data scientists who find it difficult to take a step back from something they have been immersed in for a long time. This means that in addition to a long list of technical skills, you also need to acquire some communication skills. And this is not all.

2. Cross-Department Expertise

In addition to the technical and non-technical skills mentioned above, a data scientist must have a good understanding and adequate knowledge of the sector they are operating in. Without a good grasp on domain knowledge, data scientists cannot make calculated decisions in order to assist the company. This also makes it challenging for them to migrate from one industry to another.

The Problem Of Data Privacy

As with anything that involves gathering and using data, you will have to constantly be in the thick of ethical issues around online privacy. Data scientists help companies make data-driven decisions, but in the process, they may be involved in breaching users’ privacy due to lapse in security.

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Anu Thomas
Anu is a writer who stews in existential angst and actively seeks what’s broken. Lover of avant-garde films and BoJack Horseman fan theories, she has previously worked for Economic Times. Contact:

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