Knowledge has acquired the role of catalytic agent for social and economic development of nations. Knowledge is derived from data. Current technological developments are forcing the world to embrace the concepts of digital data driven society to conceptualize the ideas of “Smart and Sustainable Habitat”. To achieve this, it is required to train the learner for making Data-Driven Decisions looking to harness data in new and innovative ways, towards design and implementation of next generation data driven applications.
Specifically, there is a need to empower the students to compete in data driven knowledge based economy. Careful observation reveals that to achieve the vision of data driven sustainable smart habitat, associated challenges can be divided in following broad categories: Data acquisition, Data transference, Data storage, Modelling and analytics. With increasing use of IoT devices and advances in instrumentation, Data is being produced at a rate that is mind boggling, in every domain, surpassing Engineering, Science, business, society. The data is in the Exa-scale. With this huge amount of data, the challenges are, What to keep?, where to keep?, and how to keep? Data analytics is doing wonders, and is transforming economy, society by making deep insights in creating knowledge and making intelligent decisions. Data Transference deals with the requirement of collecting huge amount of data from end devices and transferring it to distant data centers for further analytics. Data storage requires data modelling for understanding the data.
The data generated from complex applications and heterogeneous devices requires different complex models. Data Analytics involves statistical learning, data mining, and pattern recognition. It also involves learning how to deploy machine learning algorithms to mine your data. “Should the data be stored and then analyzed?” or “Should the analysis be performed on streaming data?” is the biggest challenge faced by data science community. The typical method used by implementers to handle big data is to throw all data back to the cloud for storage, processing, and analytics. Though this appears to be a viable solution, continuously transferring all the data to the cloud is very costly and wasteful in terms of internet bandwidth. Further, not all the IoT applications can tolerate the latency introduced due to reliance on centralized cloud computing. Fog and edge computing are the recent advances which can come to rescue in these situations.
To empower the students in the above mentioned realm, Department of Computer Science and Engineering proposes to organize four week lab oriented self-financed short term course on “Data Driven Computing and Networking”. This course is designed for Undergraduate Students of Engineering Institutions. The course is also useful for M.Tech./PhD & MCA students and other young practitioners. It shall also be beneficiary for all those who wish to enter into research in this area. The “Computer Network/Wireless Network” lab of the Department is proposed to be used for laboratory purposes. The course has been designed to cover both theoretical as well as the practical concepts in the field along with the tools and platforms useful for the participants for their Projects/Research Work.
The proposed course intends to cover Theoretical and Practical aspects of the following areas along with hands on training on the respective state of the art platforms and tools on:
- 1). Software Defined Networks : Softwarization of Networks, Programmable Networks, Network Function Virtualization, SDWAN, Software defined Wireless Networks and related concepts.
- 2) Data Analytics: Data Modeling, Clustering/Classification, Data Mining, Data Visualization, Latest trends in predictive analysis and Machine Learning, Deep Learning, Recommendation engines, Information Retrieval.
- 3) Big Data Computing : Big data Platforms such as Hadoop, Map-Reduce, Spark, Storm, and other related concepts. Data models for handling big data. No SQL Databases.
- 4) IoT and Fog/Edge Computing: Session and Application Layer Protocols (CoRE, DDS, CoAP etc.), Message Broker Architectures (MQTT, SMQTT,AMPQ), Containerization for the edge (Docker etc.), Wireless Fog Mesh Architectures, Content Centric Networking and other related concepts