Top 10 Myths Regarding Data Scientist’s Roles

In recent times, the demand for data scientists has increased due to the transition of the business to an online mode and its dependency on data. Data scientists play a diverse role, from formulating strategies to providing insights. A data scientist's job role is so dynamic that there are no chances of having a monotonous work-life. This is what makes data scientists a highly-paid & in-demand profession.

Data Science is such a diverse field, with people from all walks of life working in almost every domain imaginable. There has been so much hype surrounding data science, so there have also been a lot of myths about data science. Let’s learn about the top 10 myths about data scientists and make this profession free from myths

1. AI to Replace Data Science Soon

Artificial Intelligence is indeed likely to take over some of the tasks of data science like data cleaning and data preparation. However, it is a fact that AI cannot do all the work alone. An AI system needs human instructions to work. AI/ML can predict market trends, but they cannot make the right decisions and identify its benefits in the context of real-world issues. As a result, a machine needs human instructions to carry out complex operations. 

Interestingly, AI is itself a vast field that provides various job opportunities such as Data Engineer, IoT Specialist, Statistician, Domain Expert, Project Manager, and many more. 

2. Data Scientists Predict a Model Building

This is one of the most common myths that need to be debunked. People usually believe that the role of a data scientist is to predict the possible future outcomes of any business. However, predictive modeling involves numerous steps such as Data Collection, Wrangling, Analyzing Data, Training the Algorithm, Building a Model, Testing the Model, and Deployment. 

3. Data Scientists Work with Tools

The role of a data scientist is dynamic and just learning about different tools will not help in getting a job. To put it simply, apart from learning about tools one should also focus on mastering skills and the right application of tools and languages. Tools do help in dealing with complex data, but companies nowadays hire skill-oriented data scientists who not only know the nuances of tools and languages but also have problem-solving skills and an ability to communicate with the team effectively. 

4. A Coding or Computer Science Background is Mandatory

People coming from coding or Computer Science backgrounds might have good knowledge of coding, but it is not mandatory. Any enthusiastic learner can become a data scientist, one just needs to learn everything from scratch. At the initial stage of learning, things will not be easy but dedication and commitment to achieving goals will take you to your goal. Numerous online platforms provide data science-related courses that are designed for absolute beginners. Moreover, these days, non-tech people are switching their careers to lucrative data science fields and becoming successful data scientists.

5. It is Quite Easy to Collect Data

Data collection is a stressful task. Around 2.5 Quintillion bytes of data are generated per day. Hence, building a correct pipeline for data collection is a crucial step. Also, one needs to understand that data can be sourced in various ways but keeping up its quality matters more. Apart from this, a data security mechanism is very important to prevent the breach of any data.

6. Data Scientist Needs to be a Ph.D. Holder

A degree in Ph.D. is a great accomplishment. But it is a misconception that to become a data scientist one should have a Ph.D. in data science. If someone wants to get into a research job, then Ph.D. is a must; it involves writing well-researched scientific papers. But people who are interested in working with algorithms and understanding their application do not need a Ph.D. degree. Hence, data scientists related job roles require different levels of experience and skills. 

7. Domain/Business Knowledge is not Required

In a tech world where market competition is increasing in every business, domain knowledge is very important for a data scientist. In the data science industry, individuals with business knowledge can explore different things that will help them to understand the business goals and grow their business at an amazing pace. 

8. Soft Skills are not so Important

The aspiring data scientist often fails to notice the importance of soft skills. Companies hiring data scientists look for soft skills like critical thinking, communication skills, adaptability, problem-solver, team player, etc. Soft Skills are an invaluable asset to the skillset. Developing these skills will help in every stage of their job; right from thinking critically, and solving roadblocks to communicating with the team to arrive at the right decisions. 

9. Data Science is Best for Large Organizations

Most people are under the delusion that data science is only meant for larger organizations that require investment in software and experts to use the software. The fact is a data scientist needs essential skills and knowledge to guarantee the success of an organization. With minimum resources and a smart move, you can take your small size organization to a higher level. 

10. More Data Means More Accuracy 

One of the biggest myths about data scientists' role is that they just need to collect more and more data. If data scientists have more and more data then their work becomes easier or in fact, some believe just collecting loads of data is their job. It is not true at all, because having the correct and optimum data is the right approach to solving data-related problems. Data scientists know how much data is sufficient to run out a data model and find out actionable insights.

Conclusion 

There are plenty of misconceptions about the role of data scientists. With the rising demand for AI and machine learning, my career as a data scientist has become very rewarding. A career in data science is not only rewarding with respect to a high paid salary but also with the variety of work one gets to do in their day-to-day life. 

Having mentioned the myth, students must read and gain more knowledge about this profession so that they know exactly what kind of work it is and whether their interests coincide with such type of work.