CEI342 Data Analytics for Iot Syllabus:
CEI342 Data Analytics for Iot Syllabus – Anna University Regulation 2021
COURSE OBJECTIVES:
▪ To learn the concepts of big data analytics.
▪ To get exposure on IoT cloud analytics environment.
▪ To be familiar with general strategies on IoT analytics.
▪ To get exposure on social impact of multimedia.
▪ To identify applications that makes use of multimedia Big Data and IoT.
UNIT – I INTRODUCTION TO TECHNOLOGICAL DEVELOPMENTS
Defining IoT Analytics and Challenges- Defining IoT analytics, IoT analytics challenges, Business value concerns, IoT Devices and Networking Protocols- IoT devices, Networking basics, IoT networking connectivity protocols, Analyzing data, IoT Analytics for the CloudBuilding elastic analytics, Designing for scale, Cloud security and analytics, The AWS, Microsoft Azure, The ThingWorx overview.
UNIT -II CLOUD ANALYTICS ENVIRONMENT
The AWS Cloud Formation, The AWS Virtual Private Cloud (VPC), terminate and clean up the Environment, data processing for analytics, big data technology to storage, Apache Spark for data processing, Handling change, Exploring and visualizing data, Techniques to understand data quality Techniques to understand data quality, R and RStudio.
UNIT – III GENERAL STRATEGIES ON EXTRACTING VALUE FROM DATASETS
Decorating Your Data, Communicating with Others Visualization and Dashboarding, Applying Geospatial Analytics to IoT Data, Data Science for IoT Analytics- Machine learning (ML), eep learning.
UNIT – IV SOCIETAL IMPACT OF MULTIMEDIA BIG DATA
Multimedia Social Big Data Mining, Process Model, SWOT Analysis, Techniques for Social Big Data Analytics, Advertisement Prediction , MMBD Sharing on Data Analytics Platform , Legal/Regulatory Issues.
UNIT – V APPLICATION ENVIRONMENTS
Big Data Computing for IoT Applications-Precision Agriculture, Machine Learning in Improving Learning Environment, Network-Based Applications of Multimedia Big Data Computing, Recent Trends in IoT-Based Analytics and Big Data, Future Directions and Challenges of Internet of Things.
TOTAL: 45 PERIODS
SKILL DEVELOPMENT ACTIVITIES (Group Seminar/Mini Project/Assignment/Content Preparation / Quiz/ Surprise Test / Solving GATE questions/ etc)
1. Skills in Data mining, Data cleaning, Data analysis.
2. Educate in Data and System maintenance.
3. Develop knowledge in Competitive edge, Streaming Analytics, Spatial Analytics, Time Series Analytics, and Prescriptive analysis.
COURSE OUTCOMES:
Students able to
CO1 Describe big data and IoT.
CO2 Define cloud based IoT analytic environment.
CO3 Apply various Big data strategies.
CO4 Analyse social impact of multimedia big data.
CO5 Design smart IoT systems with big data.
TEXT BOOKS:
1. Andrew Minteer, “Analytics for the Internet of Things (IoT): Intelligent analytics for your intelligent devices”, Packt Publishing, first edition, July 2017.
2. Sudeep Tanwar, Sudhanshu Tyagi, Neeraj Kumar, “Multimedia Big Data Computing for IoT Applications:Concepts, Paradigms and Solutions”, Springer, 2020.
REFERENCES:
1. John Soldatos, “Building Blocks for IoT Analytics”, River Publishers Series In Signal, Image and Speech Processing, 2017.
2. Nilanjan Dey, Aboul Ella Hassanien, Chintan Bhatt, Amira S. Ashour, Suresh Chandra Satapathy, “Internet of Things and Big Data Analytics Toward Next-Generation Intelligence”, Springer International Publishing, 2018.
3. Stackowiak, R., Licht, A., Mantha, V., Nagode, L.,” Big Data and The Internet of Things Enterprise Information Architecture for A New Age”, Apress, 2015.