EE25C08 Applied Data Science – Semester IV – EEE / EEE(Comp) – R-2025

Subject Code & Name: EE25C08 – Applied Data Science

Regulation: R-2025

Semester: IV (Fourth Semester)

Branch: B.E. Electrical and Electronics Engineering (EEE) / B.E. Electrical and Computer Engineering (EEE(Comp))

Credits / L-T-P: 3 Credits | L-T-P: 3-0-0

Course Objectives

• The Students will be equipped with data science competencies to analyze complex electrical systems through machine learning and statistical modelling, and foster application-based problem solving for optimizing smart grid operations, load forecasting, and renewable energy integration.
• This course enables to design of IoT- driven frameworks for real-time monitoring and anomaly detection in modern power networks, facilitates to develop industry-oriented skills in predictive maintenance and fault diagnosis for enhancing data-driven decision-making within digital energy ecosystems.

Full Unit-wise Syllabus

Note: under Suggested Practices, the official PDF lists only this one practice and the sentence breaks off after “analyze energy”; it is reproduced here exactly as printed.

Unit I – Foundations of Data Science

Data science lifecycle, Electrical engineering data: sensor, SCADA, IoT, Data preprocessing and visualization, Data Science Context- Prerequisites for a Data Scientist – Tools and Skills required.

Activity: Microsoft Visio / MATLAB Simulink 1. Smart Meter Data Cleaning 2. Transformer Temperature Monitoring

Unit II – Statistical Methods in Electrical Engineering

Probability and distributions- Statistical analysis of load uncertainty and renewable energy variability in power systems. Correlation and linear regression models for prediction of electrical parameters and energy consumption, Hypothesis testing

Activity: Preparation of Cheat Sheet/Microsoft Visio / MATLAB Simulink 1. Fault Detection using Statistical Analysis 2. Load vs Temperature Correlation Analysis

Unit III – Machine Learning Applications in Electrical Systems

Supervised and unsupervised learning techniques for power systems and smart grids, Regression methods for load and energy prediction, Classification algorithms for fault detection and system state identification, Clustering techniques for load pattern analysis and anomaly detection, and model evaluation using accuracy, precision, recall, and F1- score.

Activity: Quiz, Reproduction of research paper.

Unit IV – Signal Processing and Data Analytics

Time series analysis of electrical signals for trend and forecasting, Fourier transform and FFT for frequency and harmonic analysis, feature extraction from signals for condition monitoring, and application of signal analytics for power quality assessment and predictive maintenance.

Activity: Project Based Learning/Research writing 1. Power Quality Analysis to detect harmonics. 2. Predictive Maintenance to analyze motor vibration data.

Unit V – Data-Driven Smart Grid and Sustainable Energy Systems

Smart grid architecture and communication, renewable energy forecasting and integration, IoT-based monitoring and real-time analytics, data-driven optimization for load management, and applications in smart grids and energy systems.

Activity: Project Based Learning/Research writing 1. Solar Power Prediction 2. Smart Grid Optimization

Suggested Practices (Analogy)

1. Simulation of Smart Meter Data Cleaning- Clean and preprocess power data to analyze energy

Course Outcomes (COs)

• CO1: Understand data science concepts in electrical systems
• CO2: Apply preprocessing and statistical analysis to electrical data
• CO3: Analyze data using regression and hypothesis testing
• CO4: Develop ML models for forecasting and fault detection
• CO5: Apply signal analytics for power quality and maintenance
• CO6: Design data-driven solutions for smart grids and energy systems

Assessment Pattern (Quick Note)

• Assessment mapping (as printed): IAT 1 – CO1 and CO2; IAT 2 – CO3 and CO4; IAT 3 – CO4 and CO5
• The official PDF prints no Continuous Assessment / End Semester weightage split for this course.

Source: Official Anna University – B.E. Electrical and Electronics Engineering R-2025 Syllabus
Last Updated: October 2026