Data science Myths you should stop following


Data Science has become one of the most demanded jobs of the 21st century. It has become a buzzword that almost everyone talks about these days. But what is Data Science? In this article, we will demystify the myths surrounding Data Science, the role of a Data Scientist and have a look at the tools required to master Data Science.

You Need to have a Computer Science Background Most Data Scientists may be well versed at coding and might be having Experience in Computer Science, or Maths or Statistics. That does not mean that people from other backgrounds can’t pursue a career as a Data Scientist. One thing to keep in mind is that these people from these backgrounds may have an edge, but that’s only in the initial stages. You just need to keep up the dedication and hard work and soon it will be easy for you as well. Data Scientists would be replaced by AI soon.

Data Scientists alone cannot solve everything and it’s not possible for AI to do that either. So, if you’re one of those who fears this, DONT. AI is not capable of doing things like that yet, you need a vast amount of knowledge of the different domains. 

Data Science is all about analytical tools and coding.

Data Science is not equivalent to learning and working on analytical tools like SAS or Kafka. It is also not all about always coding in languages like R and Python. Data Science projects go through similar life cycles as a normal software project. One needs to have business acumen, interpretation skills, problem-solving mindset, ability to be creative with data, presentation skills and communication skills. Working on tools and coding is a part of a data science role. A data scientist should be able to understand the business problem and think of innovative ways to solve it. A data scientist should also be capable of presenting findings and observations to non-experts through simple graphs and plain English. Data Scientists always work on developing predictive models

Due to the romanticism of the word data scientist, we might think that a data scientist’s job is to create complex predictive models only. But its only half the truth of the story. Building models is the least time consuming and the easiest task in the job list of a data scientist. A major chunk of a data scientist’s time goes towards gathering relevant data and cleaning it, processing and feature engineering. Data collection is the first and most challenging task in the data science project. One needs to figure out the data that should be analyzed for the stated problem. The data then needs to be processed to make sense of it. Choosing the right variables for a given problem can be very daunting and frustrating. That’s where domain knowledge helps. Hence, it’s not all about machine learning algorithm only and always. 


While Data Science is a vast subject, being an aggregate of several technologies and disciplines, it is possible to acquire these skills with the right approach. In the end, Data Science is a very robust field that best fits people who have a knack for experimentation and problem-solving.

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