Why is Agility Critical for a Computer Science Engineer?

Inclusion of new technologies like AI, Data Science, Blockchain, IoT in curriculum holds the key, writes Anurag Goswami.

In the modern world of computing, business and technology is changing rapidly. The introduction of modern technologies like Artificial Intelligence (AI), Data Science, Blockchain, Internet of Things (IoT), and many more have led industries to adapt to the change in technology and business. The world has witnessed an increase in the trend of technologies mentioned above that is easily visible among consumers. This rapid change has enabled the growth in the demand for skilled employees in the industries. According to the itworld.com, even before 2014, 61% of Computer Science graduates had full-time jobs and an average salary of $66,161, which is the highest among all the disciplines of engineering.

Nowadays, working in industries is no longer about the technical skills but an entrepreneurial motivation of an employee that can contribute to the goal(s) of the industries. Modern software industries believe in empowering everyone to achieve the agile culture, where teams are working collaboratively in a cohesive manner and employees are also moving between several types of roles and teams. This results in the increase in the need of employees who have the quality to become visionary, who can lead, collaborate, and can deliver outstanding outcomes that lead to the success of the software industries.

Hence, agility is expected from the Computer Science graduates who have both technical and non-technical skills. It is, therefore, the responsibility of educators to focus and train students on the skills that prepare them well to face future challenges during their jobs in the industry. However, it is still found that the academic organisations are following the old curriculum and are pursuing the traditional classroom approach for teaching a course. Students also lack motivation as it becomes difficult for them to realize the real-world connection of the topics and how acquiring certain skills (technical and nontechnical) will help them in the future. This creates an unintentional gap between academia and industry and it is observed that students who join the industry right after their undergraduate, graduate programmes, face multiple challenges.

Imbibing the rapidly changing technological and business trends while envisioning its future, CSE at BU has introduced courses such as AI, Machine Learning (ML), Deep Learning (DL), Cloud Computing, IoT, Data Science, Blockchain, Image Processing at undergraduate and graduate level courses. Along with the traditional in-class teaching approach; faculty members must utilise videos, animations, tools, active learning, flipside learning, and experiential learning approach in their courses. To add on the experiential learning and enabling flexibility, students should be exposed to the industrial strength project work, where they find out the real-world problems in the society. Exposing students to the breadth of the technical knowledge that is in demand with the industries increase their chance to get hired and it aids students to adapt in multiple technical projects in industry. It is also witnessed that some students with exceptional technical knowledge are not comfortable in working within teams. Therefore, it is also necessary for today’s CSE graduates to grasp the idea of working in teams and how your team members should enjoy working with you, as teamwork is a critical factor for the success of the industries.

Overall, modern industries require employees who can adapt in a 360  environment of roles and techniques to achieve their objectives. It becomes necessary for the academic organisations to empower students with both technical and non-technical skills that add to their agility. This can be achieved by acquiring the latest trends in teaching techniques, which can be introduced by revising the teaching curriculum along with fostering students with relevant skills.

(The author is assistant professor, Department of Computer Science Engineering, School of Engineering and Applied Sciences, Bennett University)