New technology products are entering the market at a revolutionary pace as innovation in this space changes how we communicate, interact, socialize, operate our businesses, and work. AI, machine learning (ML), augmented reality (AR), virtual reality (VR), IoT, cloud, 5G, blockchain, and mobile and web app products are being adopted at increasing rates to help businesses overcome new challenges, optimize costs, deliver better services, and benefit from greater efficiencies.
Software development has become a critical component in driving new technology trends across manufacturing, healthcare, finance, education, aerospace, and ecommerce, and it’s becoming an ever-broadening area of interest for data scientists.
Data science and software development haven’t traditionally been as closely aligned as they are today. But with the increasing adoption of intelligent technologies and automation solutions to enhance digital transformation initiatives while differentiating products and services—these two skill sets—data science software development—are rapidly merging.
Data scientists should further develop their software engineering skills as this integration continues, especially as demand for both software and software developers is forecasted to increase by 21 percent by 2028.
In this article on Data Science Software Development, let us look into the software development trends.
Software Development Trends
The growth surge of the software development industry is attributed to the rapid pace of change that’s creating new urgency for digital technology adoption, as well as rising IT infrastructure development, new advances in cloud computing, and expansion of IT services.
SaaS is one of the quickest growing sectors in the software industry, which also includes open source, programming services, and systems services, because of the rise of cloud computing technologies that help businesses accelerate their transformation and analytics objectives so they can deliver critical services.
Moving into 2021, there’s going to be further, diversified software development trends emerging in areas like IoT, AI, and continuous integration and deployment, that will alter how software developers and engineers approach programming, coding, and development. For example, new IoT use cases in smart wearables and hearables, manufacturing, and smart home systems, is going to drive a corresponding need for IoT engineers who work in IoT software development to refine their programming and coding skills for energy-efficient and sustainable connected devices.
Let us next learn about the Data Science Software Development union.
The Union Between Data Science and Software Development
Developing and running software products demands massive quantities of raw data regarding customer use and the development process, which must be transformed into powerful insights via data science skills, particularly analytics, predictive modeling, and software engineering. In other words, data scientists work on the data that’s used to develop predictive models and ML capabilities for analysis of the data collected by software.
Software engineers develop and build the software, mobile apps, and operating systems that are used by organizations. Data scientists often develop their software engineering skills to open up new career opportunities, and vice versa with software engineers.
But the development of many product-facing applications, such as AI-driven recommendation systems, has seen a mingling of these two separate skill sets. For instance, data scientists may help deploy or productize these products, while software engineers may perform modeling.
Having robust knowledge of software development standards and skills is an advantage for data scientists who are interested in shaping their careers in this sector. Skills in blockchain development, RPA, SQL, IoT development, Python, and Java are key for professionals who want to pursue a career as a cloud developer, software architect, mobile app developer, full stack web developer, or front-end developer.
Next, in this Data Science Software Development article, let us learn the role of AI and automation.
The Role of AI and Automation
It would be impossible to discuss the convergence of data science and software development without acknowledging the role and impact of AI and automation.
AI tools, like ML and natural language processing (NLP) help to make the design, development, analysis, and deployment process more efficient, fast, and less expensive for software developers, who use AI to:
- Produce project requirements
- Write and review code
- Detect and resolve bugs, anomalies, and security issues
- Test the software
- Deploy and manage projects
- Enhance development projects
With the aid of AI tools, business analysts, software coders and developers, project managers, and testers can produce less expensive, quality software with less effort. This in turn enables organizations to efficiently and effectively release new software products to better keep up with the demand for new, custom software.
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A Fast-Paced Career Path for Data Scientists
Software development is a domain characterized by fast change, with new innovations frequently and continuously emerging. This field holds a lot of potential for data scientists who want to contribute their expertise and take advantage of a career in a fast-growing market.
Simplilearn offers specific learning paths and certifications to explore software development and mold their careers around a rapidly growing industry with numerous exciting and lucrative opportunities. For data scientists, you can begin from the foundation with the Basics of Data Science, Data Scientist Course or even the PG in Data Science.