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Updated by edureka.co on May 12, 2019
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10 Data Scientists Myths Regarding Roles in India

Data Science has emerged as one of the most trending fields in recent times. It is growing at an amazing pace and so is the demand for Data Scientists. The role of a data scientist is extremely dynamic; no two days are the same for them and that is what makes it so unique and exciting. Since it’s a new field there’s both excitement and confusion about it. So, let’s clear those Data Scientists Myths in this article.

1

You need to be a Ph.D. Holder

You need to be a Ph.D. Holder

A Ph.D. is a very big achievement no doubt. It takes a lot of hard work and dedication to doing research. But is it necessary to become a Data Scientist? It depends on the type of Job you wanna go for.

If you’re going for Applied Data Science Role which is primarily based on working with existing algorithms and understanding how they work. Most folks fit into this category and Most of the openings and job descriptions you see are for these roles only. For this role, you DO NOT need a Ph.D. degree.

But, if you want to go into a Research Role, then you might need a Ph.D. Degree. If working on Algorithms or writing any paper is your thing then Ph.D. is the way to go.

2

Data Scientist will be replaced by AI soon

Data Scientist will be replaced by AI soon

If you think a bunch of Data Scientists can do everything related to an AI/ML Project. It’s not a practical solution, cause if you focus on any AI project, it has a plethora of jobs attached to it. AI is a very complex field with a lot of different roles attached to it like:

Data Engineer
Statistician
Domain Expert
IoT Specialist
Project Managers

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.

3

More Data Provides Higher Accuracy

More Data Provides Higher Accuracy

There is a very big misconception and one of the big Data Scientists Myths that “more data you have, more will be the accuracy of the model”. More data doesn’t translate to higher accuracy. On the other hand, small yet well-maintained data might have better quality and accuracy. What matters most is understanding of data and it’s usability. It’s the Quality that matters the most.

4

Deep Learning is only Meant for Large Organizations

Deep Learning is only Meant for Large Organizations

One of the most common Myth is that you need a considerably good amount of hardware to run Deep Learning tasks. Well, that’s not entirely false, a deep learning model will always perform more efficiently when it has a powerful hardware setup to run on. But you can run it on your local system or Google Colab (GPU + CPU). It just might take longer than expected to train the model on your machine.

5

Data Collection is Easy

Data Collection is Easy

Data is being generated at an amazing rate of about 2.5 Quintillion Bytes per Day and collecting the right data in the right format is still a heavy task. You need to build a proper pipeline for your project. There are a lot of sources to get data. The cost and quality matter a lot. Maintaining the integrity of the data and pipeline is a very important part that shouldn’t be messed around with.

6

Data Scientists only work with Tools / It’s all about the Tools

Data Scientists only work with Tools / It’s all about the Tools

People usually start learning a tool thinking they’ll land a job in Data Science. Well, learning a tool is important to work as a Data Scientist, but as I mentioned earlier that their role is much more Diverse. Data Scientists should go beyond using a tool to derive at solutions; instead, they need to master essential skills. Yes, mastering a tool creates hope of easy entry into Data Science but companies hiring Data Scientists will not consider the tool expertise alone; instead, they look for a professional who has acquired a combination of Technical and Business skills.

7

You Need to have Coding/Computer Science Background

You Need to have Coding/Computer Science Background

Most Data Scientists are good at coding and might be having Experience in Computer Science, or Maths or Statistics. This doesn’t mean that people from other backgrounds can’t be a Data Scientist. So, one thing to keep in mind is that these people from these backgrounds 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.

8

Data Science Competitions and Real-Life Projects are the same

Data Science Competitions and Real-Life Projects are the same

These Competitions are a great start in the long journey of Data Science. You get to work with large data sets and algorithms. Everything is fine but considering it as a project and putting it on your resume is certainly not a good idea because these competitions are no way close to a real-life project. You don’t get to clean the messy data or build any pipelines or check the time limit. All that matters is the model accuracy.

9

It’s all about Predictive Model Building

It’s all about Predictive Model Building

People usually think that Data Scientists predict future outcome. Predictive Modeling is a very important aspect of Data Science, but it alone cannot help you. In any Project, there are multiple steps involved in the whole Cycle starting from Data Collection, Wrangling, Analyzing Data, Training the Algorithm, Building a Model, Testing the Model and finally Deployment. You need to know the whole end-to-end process. Let’s look at the final Data Scientists Myths.

10

AI will Continue to Evolve once Built

AI will Continue to Evolve once Built

It’s a common misconception that AI continues to grow, evolve and generalize by themselves. Well, Sci-Fi movies have constantly portrayed the same message. Now, this is not true at all, in fact, we are way behind. The most we can do is train models that train themselves if a new data is fed to them. They cannot adapt to change in environment and a new type of data.