Don’t Believe These 5 Myths About Data Science Career

The use of the term data science has become very common nowadays, but what does it exactly mean? What is the future of data scientists? What is the scope of work for transitioning to a data scientist? Is everything rumored around the glamour of becoming a data scientist true? Let us get insights into these questions eventually.

First, let us see what data science is. As the world entered the era of big data, more and more organizations have started processing and analyzing the data at their disposal to derive meaningful insights. The discovery of hidden patterns from raw data is achieved with a blend of various tools, algorithms, and machine learning principles. This entire process of deriving meaningful insights from raw data is called Data Science. This work is done by data scientists, and as more and more industries are changing to data-driven results, the value of data scientists is also increasing as they are the ones who know how to tease actionable insights out of gigabytes of data.

It is becoming more common by the day that there is considerable value in processing and analyzing data – and that is the reason data scientists are in the spotlight. We often hear how science is a cool industry, and how data scientists are like modern-day superheroes, but most of us are still unaware of the value a data scientist holds in an organization.

Various organizations have already put data science in action. It is helping them provide personalized customer experience by understanding the audiences at a granular level. It is helping them find when and where their products sell best. It is helping them in product and business model innovations. And it is also helping them in mitigating risks and frauds.

As more and more organizations are shifting to data-driven results. The shift in the outlook of organizations has created a huge demand for data scientists, and hence several students and professionals want to shift into a data science career.

As with anything new, several myths are bubbling around making a career in data science. Let us see if the transition to becoming a data scientist is smooth or filled with hurdles.

Myth 1: You need a Ph. D. degree to become a Data Scientist

It is a common belief that it is mandatory to have a Ph.D. to become a data scientist, or a full-time data science degree is a must for making the transition to data scientist. With the amount of interest, data science started developing in the past few years. People assumed that the best way to distinguish themselves as better suited for data science roles is by getting these degrees advertised by institutes. This is a myth created by the educational institutes to run their business. In the vast and complex area like data science, practical experience is the key. There are numerous problems out there that can be picked up and solved by various data science techniques. There is plenty of online material available for study, which can help you transition to becoming a data scientist.

Myth 2: Previous experience will automatically translate to data science role

This is a myth because there are two aspects to this story. Firstly, if you are changing your role entirely from, say, being a developer or analyst to a data scientist role, then your experience is counted as zero. When given real-world data for that role, you don’t have any experience of how it works and leads to failure.

Secondly, if you have some domain knowledge and switch to data science, then you already know about the nuisances of that domain. That will be factored in as an experience and help achieve a better role and salary as a data scientist.

Myth 3: Data Science is for Geeks with a degree in Computer Science, Statistics, or Mathematics

Most of the folks that you come across as data scientists will either be from Mathematics, statistics, or programming background, but that does not mean that people coming from different backgrounds cannot be a data scientist. The only difference is a person from a background other than the ones above might have to work harder during the initial phase of career and learn everything from scratch. Organizations now are also warming up to the concept of Citizen Data Scientists where domain experts are provided with easy to use tools and are recognized as data scientists delivering business insights.

Myth 4: Developing expertise with a specific technology is enough to become a successful data scientist

The myth is that being able to write code using NumPy, sci-kit-learn, etc. should be enough to call yourself an expert. That is not true as data science needs a combination of multiple skills, and programming is just one of them. They should have other skills like problem-solving, structured thinking, communicating the right problem, and understanding the right data.

Myth 5: Participating in various hackathons and contests (and not working on real-world projects) is enough to be a good data scientist

Data science competitions and hackathons are excellent starting points for the career journey. Hackathons and competitions in data science have increased multi-fold in the last few years, and individuals who are aspiring for data science roles are putting these in their resume considering them as good as working in real-life projects.  This concept is a myth, and recruiters have started paying lesser attention to this aspect of the resume. This is because working with real data science projects is way different than working on hackathon projects. For example, in competitions, you will work with spotless data, whereas; in real-world projects, you will have to deal with unclean and unstructured raw data that needs to be processed to make it usable.

Anyone can become a data scientist with the right guidance, training, and technology expertise. It is important to not go after the make-belief myths around it. Data Science has a long way to go, and it is good to see that educational institutions around the world are setting up data science Center of Excellence to help students venture into their journey in this field.