Busting Common Data Science Job Myths

Breakdown of what a data science job looks like in reality and busting the myths that exist

Being a data scientist is one of the most-coveted jobs right now. With so much data being generated in the world right at this very moment, the need for trained data scientists who can analyse and interpret this data to make informed decisions about the future is at an all-time high! This has seen aspirants from different domains take a detour and try to make a career in this field. And why not? A data scientist gets to do really innovative work, implement the latest tools and cutting edge tech, and help businesses solve problems.

But aspirants have many wrong notions about the job profile of a data scientist. Often these misleading claims come from coaching institutes and “mentors” who want to make the profession sound extremely attractive, easily achievable to make more and more people take up their courses. The reality is often very different from this. 

Lets breakdown common misconceptions about data science jobs:

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Work on cutting edge tech from the get-go

While anyone who wants to make a career in data science would want to work on all the latest tech such as AI, ML from the very beginning, in practicality, it is not so. These mechanisms can only be deployed after considerable experience in the field.

Freshers and young professionals often carry the notion that once they bag a job even remotely related to data science, they will be implementing complex methods in business problems. What actually happens is that people need to unlearn different things studied at colleges and re-train to understand real-world situations and case studies. And all of this needs time.

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Dipesh Lakhotia, Director – Practice Head CPG Analytics at BRIDGEi2i Analytics Solutions, says, “We are seeing a rush in the data science field with a huge influx of talent who want to tap on this opportunity. Due to this, we have seen two kinds of people in this space. Some get associated with the statistics and mathematics behind data science, while some get excited by the programming aspect of it. 

“As soon as you get trained in one area, the mindset becomes that you have now become skilled enough to start solving business problems. When someone is entering the data science space and getting trained in algorithms (whatever it might be), the mindset should be to understand the business problem and how to deploy the algorithm to solve the problem”, Lakhotia adds.

Coding will sail me through; business acumen can wait

R, Python, PowerBI, and SQL are some of the popular programming languages used by data scientists. A common notion exists among aspirants and young professionals that being very skillful in these languages would easily get them through the corporate ladder. Being skilled in these surely helps. But it is not a roadblock to realise your data science dreams. Data scientists can come from a variety of fields like mathematics, statistics, physics, mechanical engineering, and chemical engineering, among others.

Taanya Gupta, who works as a data analyst at BRIDGEi2i Analytics and comes with a background in applied economics, says, “Getting business knowledge of various industries and keeping us up to date with what’s happening around the world is going to be one key factor in driving the transition. You can always complete what is given, but the task at hand is to explore and provide solutions to the clients to inculcate more data-driven solutions into it. Just knowing how to code is not going to be the way forward for sure. It must come with a lot of interdisciplinary knowledge of things.”

Have a PhD in computer science, mathematics or statistics or fall behind

Though we can’t deny that being a good data scientist needs good mathematical and statistical skills along with great logical and critical thinking capabilities, not having advanced degrees in mathematics or computer science is not really a hindrance to moving forward in a career. After moving into a data science role, one needs to constantly learn, undertake different projects, and keep the desire to innovate fueled up. These factors and performance in real-world projects will help a data scientist grow, despite not having advanced degrees in his kitty.

Venkat Raman, Co-founder, Aryma Labs, says, “It is not true that one needs to have a PhD in computer science, maths or statistics to succeed in data science. However, it is ideal if one at least has a bachelor’s degree in maths, statistics or computer science. If not a bachelor’s degree, he/she at least must have thorough training in these disciplines. Good knowledge of statistics and mathematics is essential to have a good career in data science.”

Data analysts, data engineers, and data scientists do the same thing

Though the names might seem very similar, they hold different values in an organisation. While a data scientist builds and tests hypotheses and learns from the data for the future, a data analyst focuses more on analysing and visualising data. A data engineer, on the other hand, works on building pipelines that change raw data into formats that data scientists can pick up for hypothesis testing. They aim to make data more accessible for the business.

While the hype around data science continues to grow every day, a professional entering this field must come with the passion and hunger to learn continuously. As it is a very dynamic field, constant learning of business concepts, statistical understanding and good programming skills will help succeed in this space.

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by Vijayalakshmi Anandan

The Deep Learning Curve is a technology-based podcast hosted by Vijayalakshmi Anandan - Video Presenter and Podcaster at Analytics India Magazine. This podcast is the narrator's journey of curiosity and discovery in the world of technology.

Sreejani Bhattacharyya
I am a technology journalist at AIM. What gets me excited is deep-diving into new-age technologies and analysing how they impact us for the greater good. Reach me at sreejani.bhattacharyya@analyticsindiamag.com

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