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Is Data Science a Hype?

3 min read
·
31 January, 2021

You have seen a rising trend in Machine Learning or Artifical Intelligence and you may have came across people quoting 'Data Science is just a hype'. But is it true?

The straightforward answer is “No, There is soo much scope than you can comprehend. And there will be even more in the future.” Then what stopping companies to hire Data Scientists? Why it’s hard to land a job in Data Science? Why companies still ask for such diverse experience and knowledge? To understand this let us see what do companies do with the data and how they handle it.

Automated Softwares

Companies have already best practiced the data, about storing and retrieving data, many developers have taken such tasks. In billion-dollar companies, there is software already that can generate direct reports given a chunk of data with minimal human effort. The machine produces the best visualization too. Companies are already using tools like MicroStrategy, Looker, Domo, etc.

Every Data on Purpose

Say a client is asking you to build an application that uses the data from their database and does some tasks (say it performs recommendations and analytics). So here Data Engineers study and understand the various parameters on what data transformations and endpoint need to be applied. Then they do some visualizations and builds a pipeline that supplies the necessary data through endpoints. If necessary, the ML Engineers creates the model and test-train it. Then the application is built by SDEs, ML Engineers, Data Scientists, Data Analysts, and some more. And yes, it’s not at all easy work, of course, you are guided by the experts and you learn a lot during building such a huge project.

But here are some points that need to be noted! Say you are a fresher, and building such a project will take a time to learn, adapt, and enforce the standards within a given amount of time. The time which you require is large enough than the time given by the client, so the company won’t be taking any risks and they will assign the project to an experienced data scientist.

There are so many simple yet twisted decisions needed to be taken by you as a developer to complete the project. And the company expects you to tackle it in such a way that it will not haunt back in the future. The company still hesitate to allow freshers to take decisions on such project. They want people who also have domain expertise so such decisions are taken correctly. I mean as a fresher, you cannot just keep asking what to do next!

Programming…As some great developers said, “If you write a clean and understandable code, you are saving someone in the future from mental torture.” And that someone can be you. Freshers still focus on “How to solve complex problems?” rather than “How to write clear, clean, and understandable code”

So? You and everyone can get into Data Science, but the bar is just set high. If everyone wants experts then what will happen to freshers? And that’s the problem.

Like if you ask a fresher or not experienced one “What task does the company give you?” They would say:

  • Building pipelines (ETL Work)
  • Doing some EDT
  • Building small applications to handle data for a specific purpose
  • Working in Excel, PowerBI, or Tableau
  • Heavy work on statistical tests and reporting inferences
  • Labelling, Cleaning Data
  • Reporting (which is common)

Then where is all the cutting edge and hype stuffs done? like Image Segmentation, GPT-3, DALL-E, Transformers, AI Robotics, Virtual Worlds, and much more. The answer is: Mostly the juicy stuff is done in Company’s Research Department and they make prototypes, Implementation really takes a long time.

Just remember, Whatever technology you create, if you can generate business from what you build and help the company in getting more money, then only companies are interested! It’s sure AI is hyped but understanding the applications and implementations is a crucial thing.

There is a long way to go my friend, just do and stick to whatever is your interest, but also think about the future, do some analysis. Don’t push the wall too hard, see whether we are pushing the wall or is there really a door!

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