Top 10 Data Scientists Myths Regarding Roles in India

Data Science has now been the most trending fields in the current market. Its applications are growing exponentially and so the demand for Data Scientists. The role of a Data Scientist is so dynamic that every day they get a new job to work on.

Who is a Data Scientist?

Even though there are numerous definitions available for the term Data Scientists, we may define it as a Professional who practices Data Science as an art. It solves complex data problems using scientific disciplines. It is being a specialist in different fields like text analysis (NLP), speech, image and video processing, material solution and medicine, etc.,

Data Scientist Reality Vs Myth

No Ph.D. needed for Data Science

A Ph.D. is a reputed position in the field of research and it takes a lot of hard work for building one’s career. But the requirement of a Ph.D. for becoming a data scientist isn’t necessary and it may completely dependent on what job the data scientist go for. If the field is Applied Data Science Role dealing with Existing algorithms and understanding them, then you do not need a Ph.D.

For research, you might need a Ph.D. degree.

AI will be replaced by Data Scientists soon

A bunch of Data Scientists can never replace an AI because the complexity of the AI project in terms of practicality is far beyond human capabilities. AI is attached to different roles like:

  • Data Engineer
  • Statistician
  • Domain Expert
  • IoT Specialists
  • Project Managers

Data Scientists cannot alone solve everything and even for AI, it’s not possible.

Large Data provides Accuracy

There is a misconception that Higher the data bundles result in higher accuracy which is a complete lie. The small amount of data builds in higher accuracy and better quality. The thing matters are the understanding and usability of the Data. Quality deserves less quantity.

Deep Learning is only for a large organization

It is a general belief that good hardware is needed to run Deep learning tasks. But in reality, it can be implemented on the local machine or Google Colab (GPU+CPU). It might take a longer time than expected in terms of training your machine.

Data Collection isn’t easy

Data is generated at an enormous rate of about 2.5 Quintillion Bytes per day. The major task is to collect the right data in the right format. Sources are numerous to get the data. Maintaining the integrity in the data collection and the pipeline for the project is something that needs to be careful about.

There are many other misconceptions that were planted into the minds of engineers. So discussing all of them may take months of time and solving each of the issues isn’t practically working. There are various fields that need the services of Data Scientists and the growth is taking a linear progression.