Data Science Myths vs Reality Explained…

Original article was published by Mussaveer shariff on Deep Learning on Medium


Data Science Myths vs Reality Explained…

We all know that each and every field or subject or something which you are intrested in, will be having some myths(false belief or idea).

Likewise without surprising we have some myths in Data Science field too which especially beginners and aspirants should not believe.

If you want to get clear vision about this, read this article till end sure you will not regret.

I got 9 points to explain lets discuss in detail…

1. You need to have master degree or PHD.

So, this the very common myth among many people that they need masters or PhD to get in this field — which is a complete myth.

In reality you can get a job in this field with Batchelor’ s degree itself …but the skills plays the major role here.

You should master the skills which are all required and if you are a fresher try to do some end-to-end projects and apply for internships to get the hands on experience which will increase the chances of getting full time job.

At the end having masters degree or PhD may get you a job with high salary but remember that without that also we can enter this field.

we can conclude that having masters and Ph.d is good but not compulsory.

2. You need a Data Science certification to become a Data scientist.

Again we get the same answer here that having certification is not a compulsory but good to have it.

why?

Because the data in real world is different when we compare with learning ones and also data science knowledge is much important than having a certificate.

Mentioning a certificate in resume simply gives the recruiter the idea of asking question about the course you did, but the main thing is you should have idea about the work you did.

we conclude that Data science knowledge is more important than certificate.

3. Your previous work experience is not important.

This is the myth here.. remember that Data Science is not just one work we perform many kind of works.

Your previous work experience will be definitely useful.

If you are trying to change your career towards data science than your previous work experience also matters.

For freshers the project works and internships with a decent work helps.

4. You need to be from Computer Science/statistics or programming background.

You need not to be from anyone of these background, Many people from other backgrounds has excelled in this field.

people from non technical background are getting successfully placed in this field.

Nowadays learning programming has become easier as there are many free resources.

The key here is the effort you make to gain the skills will definitely help you in getting job irrespective of your background.

5. Data Science is all about model building.

Which is not at all true.

In Data science we perform various tasks like:

  • Discussion with the product owner,
  • Requirement gathering,
  • Understanding which data is important,
  • Following the lifecycle of the project,
  • Storing the data efficiently in any other databases.

And also in lifecycle we have steps like:

  • Data collection
  • Data cleaning
  • Data preprocessing
  • Exploratory data analysis(EDA)
  • Feature engineering
  • Model training
  • Model evaluation
  • Improving the model with hyperparameter tuning
  • Model deployment in production

So, all these steps are to be taken by a data scientist while doing a project, its not just about model building.

6. Kaggle, Hackathon and real world projects are same.

This is again a separate myth

Understand clearly that Kaggle competition is just to make you clear that you are good in lifecycle of the data science project and also in kaggle we don’t model deployment part.

In real world the data changes from time to time we should know how to approach the data at that time and how to do the model optimization.

But doing projects in kaggle and Hackathon is good which makes you stronger in lifecycle of the project.

7. Most of the time of the project is invested in model building.

This is a complete myth.