Is coding necessary to work as a data scientist?

No/low-code solutions lower the bar for hiring, increasing competition as the potential applicant base widens.

Data scientists are in high demand and a ‘qualified data scientist’ is pure gold dust. But what goes into the making of a solid data scientist? Is coding a must-have skill for data scientists? Is it possible to break into data science without a coding background? Let’s find out.

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Carly T, Senior Manager, Security Strategy & Expert Engineer, Machine Learning Activision, says no-code data science solutions are more popular lately. With the proliferation of no-code/low-code platforms, the definition of ‘data scientist‘ has grown to include the no-code/low-code experts.

For non-coders

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Many data scientists started their careers without prior knowledge or experience in coding. The basic requirements for a non-coder to become a data scientist include:

  • Thorough understanding of probability and statistics.
  • Having a passion for working with numbers.
  • Ability to identify business problems.
  • Ability to extract insights from a given data set.
  • Ability to analyse data from different perspectives.
  • Ability to visualise model outputs
  • Communicate with stakeholders.
  • Good modelling skills, communication skills, analytical skills, and technical programming skills.

If you have the ability to analyse data and extract meaningful information from it, you will fit in a data science team. However, learning basic programming skills, including R, Python and SQL goes a long way.

Mixed opinions

Though experts argue over the need for coding for data scientists, many non-programmers have built impressive careers in data science.

Experts who say coding is a must, include Rachael Tatman. In her article for freeCodeCamp, she said every data scientist should be able to “write code for statistical computing and machine learning.” In his recent blog, Ronald Van Loon, CEO, Principal Analyst Intelligent World, drafted a long list of technical skills required for a data scientist. He said there was a need to have knowledge about programming languages that included Python, Perl, C/C++, SQL and Java, plus expertise in SAS, Hadoop, Spark, Hive and Pig.

Meanwhile, some argue that coding is not necessary for a data scientist. In his blog for Rapid Miner, Tom Wentworth says, “Yes, you can do real data science without writing code.” Today, organisations are also tapping ‘citizen data scientists’– generally non-coders– to solve data science problems.

Making a case for non-coding data scientists

– Common algorithms are already known as they are already coded and optimised.

– Explicit coding is being replaced with drag-and-drop interfaces, like Trifacta and Tableau.

– Data science is becoming more automated with options like AutoML or DataRobot, both of which help you find the right algorithm.

Google Duplex demo has also hinted about the future of AI, where the future data scientist might simply be having a conversation with a machine rather than coding one.

A decade ago, coding was a must for data scientist. Today, we have many workarounds available to bypass solid coding skills.

Poornima Nataraj
Poornima Nataraj has worked in the mainstream media as a journalist for 12 years, she is always eager to learn anything new and evolving. Witnessing a revolution in the world of Analytics, she thinks she is in the right place at the right time.

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