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May 18, 2018

Why Developers Need to Think Like Data Scientists

Nacho De Marco


Data is growing faster than is even fathomable. By 2020, roughly 1.7 megabytes of new information will be created every second for every human being on the planet. Given the immense amount of data, it’s no wonder there is a call in all industries for talent that can collect and analyze the data.

Data scientists around the world are now in high demand for their skills sets and high value to organizations. And since businesses are becoming more data-driven, it’s only a matter of time before developers need to work more closely with data scientists and adopt their thought-processes.

As of now, developers and data scientists play distinct roles within organizations, though they may overlap in some areas. Software engineers look at company needs and either recommend or create the software based on those needs. Of course, this expands to creating and improving operating systems, apps, and more. Developers build the systems that generate the data, which is the building block for the data scientist position.

A data scientist can manage big data, or large sets of data, collected by the operating systems created by developers. Data scientists figure out how to collect, analyze, and explain the data to business types who can then make data-driven decisions that benefit the company. Data scientists can also build predictive models to forecast when something might happen – when a market may drop, when an employee may quit, etc.

Developers and data scientists may overlap, but they rely on different tools to complete the task at hand and typically have different end goals.


Why Data Science Is Important To Developers

The Rise of AI

Data science will start to enter the development realm first and foremost because of artificial intelligence, which is booming. By 2025, the artificial intelligence market will surpass $100 billion. Executives are investing in AI and exploring its uses across all industries.

Data makes software “more intelligent” or gives it the ability to learn from itself (machine learning). This means the data will continually train the software to evolve to a new level or a new release, which means software updates will rely on data. And if software updates rely on data, then developers will need to shift their skill sets to be able to work with data. Developers are still very much necessary, and AI will not replace their role, but the advancements of AI will mean developers should learn how to work with data and not just leave that task to the data scientist.

Predictive Analytics

Predictive analytics are used across a variety of industries to predict when something will happen. Instead of a car breaking down on the road, predictive analytics can forecast when a part needs to change to avoid the breakdown. Now, predictive analytics are being introduced into the software development lifecycle.


Predictive analytics can help with a problem that plagues many developers: what to test and when. By collecting and analyzing large data sets and historical data from past projects, data scientists can analyze when in a development lifecycle QA should test the product.

Data scientists will be able to prioritize testing, identify what needs testing to produce an MVP, and illustrate focus areas for testing along the lifecycle. This means cost savings for the company, which likely means businesses will invest heavily in predictive analytics for software development. Furthermore, it means faster delivery and fewer defects in the final product.

Software developers, therefore, will need to work with data scientists, help produce clean, usable data, and allow data to drive the decision-making process, especially when it comes to testing.

Supply and Demand

Software developers are still in demand, but data scientists are increasingly needed as well. A report by LinkedIn showed that machine learning jobs grew 9.8X in the past five years and data scientist positions increased by 6.5X in the past five years. It’s important to note that a full-stack engineer grew by 5.5X in the past five years, so this position is still highly valued as well.

Data scientist positions are in high demand due to the vast amount of data and the few people that can analyze and explain the data to business executives. Developers that can not only program but also manipulate data are in high-demand, which is why it’s important to pay attention to advancements in data science.

Developers should begin to take a data-driven approach to problem-solving. Using analytical tools that identify, break-down, and resolve coding issues will allow developers to be more productive and efficient. More and more businesses are and will continue to invest in these tools to see financial gains and stay competitive in the market. In addition to AI and predictive analytics entering the software development sphere, developers can learn a lot from

data-driven approaches, especially as businesses clamor to hire data-knowledgeable candidates.

About the author: Nacho De Marco is the CEO of BairesDev. His background includes a Bachelor’s Degree in Systems Engineering, as well as a Masters of Business Administration.

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