Most Common Myths About Data-Science

Myth-Busting Time!

Myths about data science.. As said by Jacque Fresco, “In order to design a future of positive change, we must first become experts at changing our minds .”

We have also done some clustering and presented the myths in 3 types – Career-related myths, Tools and Framework related myths, and Data Science role-related myths. This blog will help you out to debunk some myths about Data Science. 

Come on, Let’s jump in and make sure you don’t fall for them

1)Non-tech people can become Data-Scientists -

Yes, you heard it right non-tech people can also build their career in Data-Science and learn easily through assignment based learning. So, it will super boost your confidence in coding on CloudyML

At CloudyML you will get instant doubt Clearance from top mentors for all assignments, projects and interview Q&As related queries everyday including weekends-Saturday and Sunday.

2)No need to invest a large amount in learning Data-Science-

That’s the most interesting part of CloudyML well-curated courses, which provides you the best content at minimally fair prices that’s unbelievable. At platforms like Simplilearn, GreatLearning students invest a lot of money and the ones who can’t afford it feel left out. So, we at CloudyML took this initiative to introduce.. The new era with new opportunities in Data-Science and master each concept at the comfort of their home.

Also, CloudyML keeps you updated and motivated with the latest interview Q&A mail and also information about jobs .

3)A math expert can only become Data-Science-

There’s a general myth that people keep in mind without discovering and researching much about Data-Science. But, we want to uncover all your doubts and remorse to step into this emerging field that would make you a “Zero to Hero” in Data-Science without having a strong grasp of math.

However, Data-Science demands analytical and business acumen along with the understanding of the applications of statistics. Machine learning and AI for solving business problems that we will make you understand during the course.

4) DSA is not a must for Data-Science-

There’s a hype in the Software industry regarding a good knowledge of Data-Structure and Algorithms. Which is another big myth that most of the students spend time learning and mugging up the logic building capabilities. 

Well, let me tell you it’s not necessary for you to be from computer-science or any IT background. So, you don’t have to code a lot and understand complex algorithms. Hence, being a fresher and having no knowledge of DSA is not any offense or setback in your journey to become a successful Data-Scientist. Keep learning with CloudyML. We are here to give you the right suggestions and best career pathways.

P.S. While I am saying Freshers, what I refer to is people with no Data-Science or Analytics experience.

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5)Data-Science is dead-

Hey, are you living in the 21st century? Or still living in that stereotypical era of, “Govt. Jobs hi sab kuch hai”🧐or “Papa chahtein hai mein placement lelu isi domain mein”😂, etc. CloudyML suggests, “Don’t just sacrifice, hustle for more and dream big”. 

In general trends a lot of people debate that the Data Science hype is too much and it won’t last long. The fact is, data Science is among the most important aspects in determining the success of any organization irrespective of the domain.

As per reports by IBM, we are generating data at the rate of 2.7 Quintillion bytes/day and 90% of the world’s data (i.e. 90 percent of all the data ever created) has been created in the past 2 years. To handle and manage this massive amount of data, Data Science comes into play in order to structure and analyze datasets, draw hidden patterns for business and build solutions to solve crucial real-world problems

6)Expert coding skills are required to become a Data-Scientist-

A big NO to this myth because this is what differentiates a Data Scientist with other IT professionals who have just started their career.  Programming is not the eye of data science spectrum – it’s just one part of a whole. With proper domain knowledge, you can discover so many important things which a world-class Programmer will not be able to do. 

In each and every step in Data Science pipeline we would assist you in having the domain knowledge. For eg.Learn Data Featurization and Data Visualization & Analysis. Gradually, it would be easier for you to know the main business objective when you have knowledge of the domain.

NOTE – We Provide Job Referrals to our Learners if there’s any matching opportunity in companies like Fractal Analytics, Tiger Analytics, Tredence Analytics  etc.

7)Engineers from any branch can become Data-Scientist-

Absolutely, yes even if you belong to Mechanical, Civil , Electrical or even commerce or Arts background. CloudyML assures you that no matter how you have spent your degree learning a particular subject. You are completely eligible to get a Data-Science job through our courses and we promise to give you what you deserve through your learnings.

Based on your performance you will get 1 month-internship opportunity In Data Science at CloudyML after your course completion. With industrial working experience, you will also get a Letter of Recommendation and a certificate of Internship. 

8)Ph.D is Not Mandatory to be Data Scientist

Holding a Ph.D. degree is an out-standing achievement. We respect the efforts of those who are willing to earn a doctorate degree in their area, of interest with a lot of hardwork and dedication. But, you need to understand it in a particular manner that to build a career in Data-Science you don’t have to owe any specific degree or Doctorate in the subject. So, boost yourself and invest in your learning curve rather than distracting yourself with superstitious myths.

If you’re looking for a role in Data-Science and are struggling to break through, make sure you check out this awesome CloudyML program! You would enjoy a perfect blend of learning from experienced Instructors and practical hands-on projects.😉

See you in the Course!

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