Software development standards to be adopted by data scientists in future

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Data has become a strategic asset for companies nowadays and all the crucial decisions taken are mostly data-driven. Hence the need for data scientists have seen an exponential rise.

In this era, with the rise in complexity of data, without robust data analytical models it is hard to get insights on these data. Developing robust and high-quality models on the grounds of ML and data science is an onerous process. But, incorporating software development standards into data science will surely help in this aspect.

A week ago, IBM at Think Digital 2020 virtual conference, launched Watson AIOps (Artificial Intelligence for IT operations management). AIOps uses bigdata, ML, advanced analytics together with automation technologies to simplify IT operations and accelerate and automate problem resolution.

According to Microsoft, to create and run a software, it requires huge amount of raw data about the development process and customer usage. In the hands of a good data scientist, this data can be used to get great insights.

Unfortunately, data scientists with both analytical and software skills are rare. Here, are some points that demands data scientists to follow software development standards.

Clean Code

Software developers while developing a software, include functions, class, comments, and error messages in their codes, which can help other readers to understand as well.

Data scientists, on the other hand, codes solution for their problem but don’t include comments and clear documentation. This results in messy codes and is hard to understand for other developers. Hence, data scientists should follow the clean code standard like the software developers.

Productive research

Software developers use a structure production-level codes at all stages to built and create a software without facing any failure.

Whereas, ML model fabrication for real-world use goes against the original research conducted. So, it is better for data scientists to also focus on production-level codes to have a productive research delivering a useful product.

Automation

Automation is an effective method in increasing efficiency of a software product.

Data science projects consume 80% of the developer’s time in data wrangling. Automation adopted here can expedite the development of ML models.

“The benefit of this automated approach is that it provides data scientists with the assistance needed to test for scenarios that they may not have ever considered, also, it allows data scientists to try significantly more use cases and dramatically shorten the time needed to reach highly impactful ones” said  Ryohei Fujimaki PhD is founder and CEO of dotData.

As the recent times demand the use of more ML models in analyzing the data, it is high time data scientist who are the ML developers to adopt to the software developer standards to meet the current crisis.

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