Is coding necessary to work as a data scientist?


A data scientist is a very demanding job today and it is difficult to find a “qualified data scientist”. When we say “qualified”, does that mean that a data scientist must have a solid background in coding? Or is it possible to break into data science without this training? While most companies are looking for a qualified data scientist, that doesn’t make a non-coder any less eligible. Ultimately, what a business needs is for the work to be done through a coder or a non-coder. Let’s see what some data science experts, developers, vendors and bloggers have to say.


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Carly T., Senior Manager, Security Strategy & Expert Engineer, Machine Learning Activision, says no-code data science solutions have become more popular lately, and the demand for such data scientists is increasing. This request has now diluted the title of “data scientist”.

For non-coders

Many great corporate data scientists started their careers in data science without any prior coding knowledge or experience. It doesn’t matter how much coding you know when pursuing to become a data scientist, but it is important that you understand basic programming which includes medium loops, functions, and if-else programming logic.

Besides that, there are many data science courses to improve coding skills.

The basic requirements for a non-coder to become a data scientist include:

  • Deep understanding of probability and statistics.
  • Have a passion for working with numbers.
  • Be able to identify business problems.
  • Being able to work on the given dataset.
  • Have confidence in learning any new programming language.
  • Be able to analyze data from different angles.
  • Be able to build an ML model to visualize and predict outcomes.
  • Transmit inside particular data to stakeholders.
  • Have good modeling, communication, analytical and technical programming skills.

If you have the ability to analyze data and extract meaningful insights from it, you will fit into a data science team. However, learning basic programming skills including R, Python, and SQL queries can still be beneficial in this career.

Mixed opinion from experts

Although experts argue about the necessity of coding for data scientists, stating that it is mandatory to work in the field, many non-programmers without coding experience still have glorious careers in data science and data science. programming, and coding is more of a skill than a requirement.

Experts who say coding is a must include Rachel Tatman. In his article for freeCodeCamp, she said that every data scientist should be able to “write code for statistical computing and machine learning.” In his recent blog, Ronald VanLoon, CEO, Principal Analyst Intelligent World, provided a long list of technical skills required for a data scientist. He said knowledge of programming languages ​​including Python, Perl, C/C++, SQL, and Java is required, as well as expertise in SAS, Hadoop, Spark, Hive, and Pig.

Some opinions also argue that coding is not necessary for a data scientist. In his blog for fast miner, Tom Wentworth says, “Yes, you can do real data science without writing code.” Today, organizations are also identifying “citizen data scientists,” who are typically non-coders but capable of solving complex organizational problems.

Non-coders also give reasons why coding is not necessary for them:

Reasons include:

– The common algorithms are already known because they are already coded and optimized.

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

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

– Google has also ensured that a data scientist can train high-quality custom machine learning models with minimal effort and machine learning expertise.

Google Duplex Demo also hinted at the future of AI, where the future data scientist might just have a conversation with a machine rather than coding one.

Ten years ago, very few people used the title ‘data scientist’, and there was no degree in ‘data science’. The internet was still nascent and coding was a must; however, there are many algorithms already worked out and universally available online, so coding no longer seems like a “must”.


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