4 Data Science Huge Myths v/s Factual Reality

Are you an engineer? Interestingly enough, EduBild has come across ultimate interns who are interested in the Data Science field but turned out to be unaware of the core of this specialization.

Every 10th engineering student in India is curious about data science as it’s a booming industry, high paid job lottery, well-off sector. But the reality is, that the path is not as smooth as one imagines as this field requires a specific skill-set one should develop continuously through learning and practice. In this blog post, we are introducing some myths that are common for those making the first steps in the data science industry.

Myth 1: Data scientists can predict anything.

Reality: Data scientists can point out new sources of revenue, predict the best product features as per your business requirements, give insights into your next productions through data science.

Myth 2: All data scientists get high salary.

Reality: Only good and qualified data scientists bring value for their companies and get highly paid for that. However, a fresher engineer who has not covered data science program, doesn’t master the required languages, can’t expect a high-pay unless the qualification matches with the employer’s expectation. The average salary of a data scientist fresher in India can be from 6 lack INR per annum. For a mid-level specialist salary would be 1,367,306 INR and if you work internationally then 70000US$ to 110000US$ per annum.

Myth 3: Data scientist’s role is just to collect data- it’s easy, anyone can do it

Reality: Data scientists collect and analyze large amount of structured and unstructured data. They use computer science, statistics, and mathematics to analyze, process, and package insightful data and serve as the bases for key decisions made for the organization.

Myth 4: I am a software engineer, I can be a data scientist

Reality: Most of the common tools a data scientist should master are:

  • Apache Spark
  • Code Quality
  • Data Modeling
  • Data Visualization
  • Data Wrangling
  • Machine Learning
  • Python
  • R
  • SQL
  • System Design
  • Technical Communication

In a nutshell, data scientists perform data collection and processing, while software engineers develop products, applications, and capabilities for users. It’s naïve to think that software engineers can easily be data scientists because even though both data science and software engineering require programming experience, data science is more about statistics and machine learning, while software engineering concentrates on coding languages.

If you are a kick-ass data science student interested in a live-project opportunity, hurry up, don’t miss out EduBild Experienships, get a project-based internship with top companies in India.

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