This Specialization will cover the fundamentals of data science, such as statistical modelling, exploratory data analysis, data preprocessing, data gathering, and data visualisation. This specialization horns the graduate to utilise these techniques to extract significant patterns and trends from varied datasets to tackle difficult problems and make educated decisions in fields like healthcare, finance, marketing, and engineering.

Experiential learning through projects, case studies, and hands-on exercises with industry-standard tools and technology. 
Interdisciplinary Approach incorporating concepts from computer science, statistics, mathematics, and domain-specific expertise to train practical applications.
Industry-Relevant Skills through Intern and projects on real-world datasets and projects, making graduate’s industry- ready.
Professional Advancement: Graduates will be well prepared to work as data scientists, or data analysts, or to pursue postgraduate work in related subjects.

• Data Acquisition and Sensors: Designing and implementing systems for data acquisition, including sensors, data loggers, and communication protocols.
• Signal Processing: Develop algorithms and techniques to extract useful information from signals to process and analyse diverse data types, including audio, image, and sensor data.
• Internet of Things (IoT): Development of IoT systems and protocols, enabling seamless data communication and integration with data science for modern IoT integrated Electronics landscape.
• Data Transmission and Networking: ECE with Data Science engineers design, optimize communication networks to access, and process data from distributed sources for data-intensive applications.
• Embedded Systems and Edge Computing: ECE with Data Science engineers specialize in developing embedded systems and edge computing devices that process real-time data analysis and filtering locally at the source.
• Hardware Acceleration for Machine Learning: ECE with Data Science engineers contribute to the design of specialized hardware architectures, optimized for data-intensive tasks like deep learning inference and training.
• Communication Protocols and Standards: ECE with Data Science Engineers contribute to the development and standardization of protocols facilitating seamless data exchange and integration in data science workflows for interoperability and compatibility.