- Program Highlights
This 12-month Executive PG Certification Program is designed specifically for engineering students and professionals, providing comprehensive knowledge and hands-on experience in data science, artificial intelligence, and machine learning (AI/ML) for R&D engineering applications. The program focuses on practical applications of data-driven techniques in the engineering industry, bridging the gap between theory and industry needs. Students will work on real-world projects, develop AI/ML models, and learn how to apply data science to solve complex engineering problems in R&D, predictive maintenance, automation, and system optimization.
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- Admission Closes on 1st Nov
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- Career Opportunities
- Automotive Engineering: Apply AI/ML for vehicle design, diagnostics, and autonomous driving.
- Aerospace Engineering: Use AI/ML to optimize flight operations, maintenance, and simulations.
- Electrical & Electronics Engineering: Enhance circuit design and electrical systems with AI-driven solutions.
- Control Systems: Implement AI for automated control and process optimization.
- Renewable Energy: Optimize energy generation and distribution using data science models.
- Robotics and Automation: Design and control robotic systems using machine learning algorithms.
- IoT Systems Engineering: Develop intelligent IoT devices with embedded AI for smart data processing.
- Manufacturing & Industry 4.0: Use predictive analytics for smart factories and automated quality control.
- Energy Systems: Model and predict energy consumption for improved efficiency and sustainability.
- Smart Cities: Apply data science for urban planning, traffic management, and energy distribution.
- Telecommunications: Use AI/ML to optimize network performance, capacity planning, and fault management.
- Biomedical Engineering: Implement machine learning in medical diagnostics, imaging, and device development.
- Data Scientist: Extract insights from data and build predictive models for engineering applications.
- AI/ML Engineer: Design and implement AI/ML algorithms to solve complex engineering problems.
- R&D Engineer: Use data science and AI/ML to innovate and enhance R&D processes.
- Predictive Maintenance Engineer: Develop AI/ML models to predict and prevent equipment failure.
- Control Systems Engineer: Apply data-driven control strategies for automated industrial systems.
- Data Engineer: Build data pipelines and manage large-scale datasets for engineering analysis.
- Big Data Analyst: Analyze large volumes of engineering data using big data tools and frameworks.
- Machine Learning Researcher: Conduct research on new AI/ML algorithms for engineering advancements.
- Robotics Engineer: Integrate AI/ML into robotics for smarter, more efficient automation solutions.
- IoT Engineer: Develop intelligent, AI-enabled IoT systems for industrial and engineering applications.
- Data Analyst: Analyze engineering datasets to generate actionable insights and predictions.
- Deep Learning Engineer: Design and deploy deep learning models for tasks such as image processing or speech recognition in engineering contexts.
- Python Programming: Proficiency in Python for data manipulation, modeling, and AI/ML applications.
- Data Wrangling & Cleaning: Expertise in cleaning, processing, and preparing datasets for analysis.
- Machine Learning Algorithms: Knowledge of algorithms like decision trees, regression, and clustering for predictive modeling.
- Statistical Analysis: Ability to perform statistical tests, analyze data, and draw meaningful conclusions.
- Time Series Forecasting: Skills in building time series models for predicting trends and seasonality in data.
- Neural Networks: Understanding of deep learning and neural networks for advanced AI applications.
- Data Visualization: Creating visual representations of data to communicate insights effectively.
- Big Data Tools (Hadoop, Spark): Working with large datasets and distributed computing frameworks.
- Predictive Maintenance: Implementing models to predict equipment failure and optimize maintenance schedules.
- Feature Engineering: Identifying and engineering features to improve machine learning model accuracy.
- Control Systems & AI: Applying AI in control systems for real-time decision making and automation.
- Deep Learning: Utilizing neural networks for advanced machine learning tasks like image and speech recognition.
- Automotive: Tata Motors, Mahindra, Maruti Suzuki, Bosch
- Aerospace: DRDO, HAL, Airbus India
- Electronics: Samsung R&D, Intel, Texas Instruments, Qualcomm
- Energy: NTPC, Reliance Power, Siemens Energy, Tata Power
- Manufacturing: L&T, BHEL, Honeywell, GE India
- Telecommunications: Airtel, Jio, Nokia, Ericsson
- IoT & AI: Wipro, Infosys, TCS, Cognizant
- Healthcare/Biomedical: Philips India, Medtronic, Siemens Healthineers
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- Program Outcomes
- Program Curriculum
Week 1-4: Course-1: Introduction to Data Science for Engineers
- Module Details:
- Overview of Data Science: History, Evolution, Process (Collection, Cleaning, Analysis, Interpretation)
- Importance in Engineering R&D: Data-Driven Product Development, Case Studies
- Data Science Workflow: CRISP-DM Model, Data Collection, Preparation, Modeling, Deployment
- Weekwise Planner:
- Week 1-2 : Introduction to Data Science fundamentals.
- Week 3-4 : Understanding its significance in R&D.
Week 5-9: Course-2: Python for Data Science & Engineering Applications
- Module Details:
- Python Basics: Syntax, Data Types, Functions, Control Structures
- Data Manipulation with Pandas & NumPy: DataFrames, Array Operations, Reshaping, Handling Missing Data
- Data Visualization with Matplotlib: Plotting Basics, Customization, Plot Types (Line, Bar, Histograms)
- Weekwise Planner:
- Week 5-6 : Python Programming for Data Science.
- Week 7-8 : Data manipulation with Pandas/NumPy.
- Week 9 : Visualizing Data with Matplotlib.
Week 10-13: Course-3: Statistics & Probability for Engineering
- Module Details:
- Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation.
- Probability Distributions: Normal, Poisson, Binomial, Uniform.
- Hypothesis Testing & Confidence Intervals: Z-test, T-test, P-value, Confidence Levels, Error Margins.
- Weekwise Planner:
- Week 10-11 : Descriptive statistics and probability theory.
- Week 12-13 : Hypothesis testing and interval estimation.
Week 14-17: Course-4: Linear Algebra & Calculus for Data Science
- Module Details:
- Vectors, Matrices, and Tensors: Operations, Inverses, Eigenvalues, Eigenvectors
- Eigenvalues and Eigenvectors: Their Role in Data Science (PCA, Dimensionality Reduction)
- Differentiation in Machine Learning: Gradient Descent, Partial Derivatives, Chain Rule
- Weekwise Planner:
- Week 14-15 : Linear algebra applications in AI/ML.
- Week 16 : Eigenvalues and eigenvectors.
- Week 17 : Gradient descent and differentiation.
Week 18-21: Course-5: Data Wrangling & Cleaning Techniques
- Module Details:
- Handling Missing Data: Imputation, Dropping, Interpolation
- Data Normalization & Transformation: Standardization, Scaling, Log Transformation
- Feature Engineering: Feature Creation, Feature Selection, Handling Categorical Variables, One-Hot Encoding
- Weekwise Planner:
- Week 18-19 : Data cleaning and missing data handling.
- Week 20 : Data normalization.
- Week 21 : Feature engineering for better models.
Week 22-25: Course-6: Exploratory Data Analysis (EDA)
- Module Details:
- EDA Techniques: Summary Statistics, Pairwise Plots, Visualizing Distributions
- Outlier Detection: Z-Scores, IQR, Boxplots
- Correlation & Covariance Analysis: Pearson/Spearman Correlation, Covariance Matrix
- Weekwise Planner:
- Week 22-23 : Conducting exploratory data analysis.
- Week 24 : Detecting and handling outliers.
- Week 21 : Analyzing correlation and covariance.
Week 26-30: Course-7: Supervised Learning for Engineering Applications
- Module Details:
- Linear & Polynomial Regression: Model Building, Interpretation of Coefficients, Residuals
- Decision Trees & Random Forests: Splitting Criteria, Overfitting, Feature Importance, Bagging & Boosting
- Model Evaluation Metrics: R-squared, Mean Absolute Error (MAE), Confusion Matrix, ROC Curve
- Weekwise Planner:
- Week 26-27: Building regression models.
- Week 28-29 : Implementing decision trees and random forests.
- Week 30 : Evaluating model performance.
Week 31-33: Course-8: Unsupervised Learning for Engineering Data
- Module Details:
- Clustering Techniques (K-means, Hierarchical): Clustering Criteria, K Selection, Dendrograms
- Dimensionality Reduction (PCA, LDA): Eigenvectors/Eigenvalues in PCA, Discriminant Analysis, Applications in Large Datasets.
- Weekwise Planner:
- Week 31-32: Clustering engineering data with K-means.
- Week 33 : Dimensionality reduction techniques (PCA, LDA).
Week 34-37: Course-9: Time Series Analysis for Engineering
- Module Details:
- Time Series Forecasting (ARIMA, Exponential Smoothing): AR, MA, ARIMA Models, Smoothing Techniques, Forecast Accuracy.
- Trend and Seasonality Detection: Decomposition of Time Series, Seasonal ARIMA, Trend Detection Methods.
- Weekwise Planner:
- Week 34-35: Building time series models (ARIMA).
- Week 36-37: Forecasting trends and seasonality in engineering data.
Week 38-40: Course-10: Introduction to AI & Machine Learning in Engineering
- Module Details:
- AI/ML Overview: Machine Learning Types (Supervised, Unsupervised, Reinforcement)
- Applications in Engineering R&D: Predictive Maintenance, Fault Detection, Optimization
- ML Workflow: Data Preprocessing, Model Training, Hyperparameter Tuning, Model Evaluation
- Weekwise Planner:
- Week 38-39: Introduction to AI/ML and its applications in R&D.
- Week 40: Learning machine learning workflows.
Week 41-44: Course-11: Predictive Maintenance with AI/ML
- Module Details:
- Predictive Maintenance Concepts: Failure Prediction, Condition-Based Maintenance, Remaining Useful Life (RUL) Estimation
- Applications in Engineering R&D: Predictive Maintenance, Fault Detection, Optimization
- ML Workflow: Data Preprocessing, Model Training, Hyperparameter Tuning, Model Evaluation
- Weekwise Planner:
- Week 38-39: Introduction to AI/ML and its applications in R&D.
- Week 40: Learning machine learning workflows.
Week 45-50: Course-12: Capstone Project
- Module Details:
- Project Definition & Planning: Problem Identification, Objective Setting, Scope Definition
- Data Collection & Model Development: Data Acquisition, Model Design, Testing & Validation.
- Presentation and Evaluation: Report Writing, Presentation Techniques, Industry Feedback
- Weekwise Planner:
- Week 45-50: Capstone project, including planning, model development, testing, and presentation, focusing on real-world R&D problems in engineering.
Week 50: Onwards: Elective Course (1-4)
- Elective 1: Data Science for Control Systems
- Elective 2: AI and Machine Learning in IoT
- Elective 3: Big Data Analytics for Engineering
- Elective 4: Deep Learning for Engineering Applications.
Electives
- Elective 1: Data Science for Control Systems
- Control System Basics
- Data-Driven Control System Design
- AI in Control Algorithms
- Elective 2: AI and Machine Learning in IoT
- IoT Architecture
- Machine Learning for IoT Data
- Edge Computing with AI
- Elective 3: Big Data Analytics for Engineering
- Introduction to Big Data Tools (Hadoop, Spark)
- Data Processing Pipelines
- Big Data Applications in Engineering
- Elective 4: Deep Learning for Engineering Applications.
- Introduction to Neural Networks
- Convolutional Neural Networks (CNNs)
- Applications of Deep Learning in Engineering
- Skills Covered
Python Programming
Data Wrangling & Cleaning
Machine Learning Algorithms
Statistical Analysis
Time Series Forecasting
Neural Networks
Data Visualization
Big Data Tools (Hadoop, Spark)
Predictive Maintenance
Predictive Maintenance
Feature Engineering
Control Systems & AI
Deep Learning
- Benefits
- Learn in-demand AI/ML skills from scratch.
- Hands-on project experience to build your portfolio.
- Opportunity to work on real-world engineering data.
- Exposure to AI-driven solutions in manufacturing and R&D.
- Build a career path in growing fields like IoT and data science.
- Access to live training and mentorship from industry experts.
- Stand out to top employers in engineering sectors.
- Gain advanced AI/ML skills for engineering-specific applications.
- Improve problem-solving capabilities in R&D.
- Enhance your ability to work with big data in real-time systems.
- Strengthen your expertise in predictive maintenance.
- Expand knowledge in control systems and IoT engineering.
- Boost career prospects in industries such as automotive, aerospace, and energy.
- Lead AI/ML projects within engineering organizations.
- Apply AI/ML techniques to solve engineering problems in R&D environments.
- Develop predictive models for industrial equipment maintenance and energy forecasting.
- Use Python and relevant data science tools to process, visualize, and analyze large engineering datasets.
- Understand and implement supervised and unsupervised learning algorithms.
- Leverage IoT and AI in smart systems and control applications.
- Master deep learning for solving complex engineering problems.
- Projects
Data Analysis and Visualization for Engineering Datasets
Predictive Maintenance Model for Industrial Equipment
Feature Engineering for Sensor Data in R&D Applications
Time Series Forecasting for Energy Consumption
Building a Supervised Learning Model for Fault Detection
Clustering and Anomaly Detection in Manufacturing Data
AI-Powered Predictive Maintenance for Engineering Systems
Capstone Project: AI/ML Solution for Engineering R&D
Clustering and Anomaly Detection in Manufacturing Data
- Tools Covered
Hardware Labs Access
Two-Wheeler Simulator & Test Bench
The 2 Wheeler Simulator & Testbench focuses on evaluating EV battery performance, including voltage, current, discharge profiles, and capacity testing under various load conditions. It also covers Battery Management System (BMS) testing and dynamic load analysis to optimize electric bike performance and safety.
LAB 1
Charging Station Simulator and Test Bench
The Charging Station Simulator and Test Bench covers experiments focusing on the efficiency, behavior, and safety features of EV charging systems. It includes the measurement of voltage, current, and power consumption during charging, and tests security features like RFID and OTP-based authentication. Key areas include energy efficiency analysis, protection unit testing, and charging behavior under different conditions
LAB 2
EV In-house manufacturing & Development KIT
The EV In-house Manufacturing & Development KIT provides all the essential tools and components for building and prototyping electric vehicles, supporting the entire development process from concept to production. It includes equipment for battery monitoring, motor control, system integration, and real-time data analysis, ensuring seamless development for EV projects
LAB 5
