Today, being a data scientist is a tough career, and finding a "qualified data scientist" is difficult. Is it true that a data scientist must have a strong coding background when we say 'qualified'? Is it possible to break into data science without any prior experience? Although most firms prefer to hire a skilled data scientist, this does not rule out non-coders. In the end, a business needs to get work done, whether it's through a coder or a non-coder. Let's take a look at what some experts have to say.

For those who aren't developers

Many successful enterprise data scientists started their careers in data science with no prior coding skills. When you want to be a data scientist, it doesn't matter how much coding you know; what matters is that you understand basic programming concepts like mean loops, functions, and if-else logic.

Aside from that, there are other data science courses available to improve coding skills.

The following are the essential prerequisites for a non-coder to become a data scientist:

  • Understanding probability and statistics in depth.
  • Having a strong desire to deal with numbers.
  • Being able to recognise and solve business issues.
  • Being able to operate with the data set that has been provided.
  • Having faith in your ability to master any new programming language.
  • Being able to look at facts from many angles.

Being able to see and anticipate outcomes using a machine learning model.

Stakeholders are being informed about the inner workings of certain data.

Possessing strong modelling, communication, analytical, and technical programming abilities.

If you can analyse data and extract useful information from it, you'll be a good fit for a data science team. Learning basic programming abilities, including R, Python, and SQL queries, can be quite useful in this field.

Experts have differing viewpoints

Though some experts say that coding is required for data scientists to work, many non-programmers with no coding background have successful careers in data science and programming, and coding is more of a talent than a requirement.

Non-coders also provide reasons why they do not need to code:

The following are some of the reasons:

  • Most algorithms are well-known because they have been coded and optimised.
  • Drag-and-drop interfaces, such as Trifacta and Tableau, are replacing explicit code.
  • Data science is getting increasingly automated, thanks to tools like Google Cloud AutoML and DataRobot, which assist you in finding the best algorithm.
  • Google also claims that a data scientist with minimal effort and machine learning expertise can train high-quality custom machine learning models.
  • The Google Duplex demo also alluded to the future of AI, suggesting that instead of coding, the future data scientist may simply converse with a machine.

The internet was still in its infancy, and coding was a need; however, numerous algorithms have since been developed and are widely available online, so coding does not appear to be a "must" any longer.