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Why an approach from a data scientists’ point of view is necessary for developers

Developers point of view for data scientists

Data is growing even more rapidly than is imaginable. In just a span of two years, roughly 1.7 megabytes of new information will be created every second for each and every human being on the planet. With the immense amount of data that is generated and present around us, it’s no wonder there is a huge demand in all industries for talent that can collect and analyze the data. Data scientists around the world are now in highly sought after for their skill sets and high value to organizations. With the businesses becoming more data driven, it will only be a matter of time before adopting data scientists’ thought processes becomes imperative for developers. As of now, although overlapping sometimes, developers and data scientists play distinct roles within organizations. Software engineers look at company needs and either recommend, modify or create the software based on them. Of course, this extends as far as creating and improving operating systems, apps, and more. Developers create the systems that generate the data, which is the foundation for the data scientist position. The huge sets of data collected by the operating systems created by developers,or big data, can be managed by data scientists. 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 the approximate time when something might happen – when the market may take a drop, when an employee may quit, etc. Developers and data scientists may chance upon each other sometimes, but they rely on completely different tools and skill sets to complete the task at hand and typically have different end goals. Why Developers Need A Background in Data Science The Rise of AI Data science will start to enter the blooming development realm first and foremost because of artificial intelligence. The artificial intelligence market will surpass $100 billion, by 2025. Executives are investing huge amounts in AI and exploring its uses across all industries. Data makes software “more intelligent” or gives it the ability to learn from its own experience (machine learning). Software updates will rely on data since it keeps on upgrading itself by continuing to train the software to evolve to a new level or new release. And if software updates rely on data, developers will need to shift their skill sets in order to be able to work with data efficiently. Even though AI will not replace the role of developers, the advancements  in it will make it necessary for developers to learnt how to work with data and not just leave the task up to the data scientist. Predictive Analytics Predictive analytics are used across a large variety of industries to predict when something is probable to happen. Predictive analytics can forecast when a part needs to be changed to avoid the breakdown of a car, instead of it actually happening. Now, predictive analytics are being discovered by the software development life cycle. Predictive analytics can help with a problem that plagues many developers: what to test and when. Data scientists can analyze when in a development life cycle QA should test the product by collecting and analyzing large data sets and historical data from past projects. Data scientists will be able to prioritize testing, identify what needs testing to produce an MVP, and illustrate focus areas for testing along the life cycle. This results in cost savings for the company making predictive analytics for software development a goldmine to capture as soon as they can. Furthermore, it leads to faster delivery and fewer defects in the final product. Software developers, therefore, will need to work in close collaboration with data scientists, help produce clean, usable data, and allow data to drive the decision-making process, especially in the case of testing. Supply and Demand Even though the demand for software developers has not diminished, the demand for data scientists is increasing as well. Machine learning jobs grew by 9.8 times in the past five years and data scientist positions increased by 6.5 times in the past five years according to a report by LinkedIn. It’s important to note that a full-stack engineer grew by 5.5X in the past five years, so it is still highly sought after. Data scientist positions are in high demand due to the vast amount of data and having the ability to analyze and explain the data to business executives. Developers that can program as well as manipulate data are in high-demand, which is why it’s important to pay attention to advancements in data science. It’s about time that developers 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 The number of businesses that will invest in these tools to see financial gains and stay competitive in the market is increasing by the day.Developers can learn a lot from data-driven approaches, especially as businesses clamor to hire data-knowledgeable candidates in addition to AI and predictive analytics entering the software development sphere.

Manu Jeevan

Manu Jeevan is a self-taught data scientist and loves to explain data science concepts in simple terms. You can connect with him on LinkedIn, or email him at manu@bigdataexaminer.com.
Manu Jeevan
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