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Executive PG Certification Program in Data Science & AI/ML for R&D Engineering Applications [100% LIVE]

By DIYguru · 10/22/2024 · 20 min read

The Executive PG Certification Program in Data Science & AI/ML for R&D Engineering Applications is a specialized, 100% live, instructor-led program designed to equip professionals with advanced skills in data science, artificial intelligence (AI), and machine learning (ML). Tailored specifically for R&D engineering contexts, this course focuses on practical applications of AI/ML in solving real-world engineering problems.

  • 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.


Leverage the prestige of IIT Guwahati

  • Program certificate and 'Executive Alumni Status' from the E&ICT Academy, IIT Guwahati
  • Opportunity to attend a campus immersion program hosted by IIT Guwahati


    • 150+ Live Sessions Over 12 Months

  • Engage in interactive sessions led by IIT faculty and industry veterans.
  • Deep dive into advanced EV design, control systems, and embedded systems.
  • Flexible scheduling to accommodate working professionals and freshers.


    • Exclusive, Industry-Aligned Projects

  • Work on powertrain optimization and battery management systems.
  • Apply skills through EV simulations using MATLAB and SIMULINK.
  • Hands-on projects aligned with real-world industry needs.




    • One-on-One Mentorship

  • Receive personalized guidance on technical challenges.
  • Get career advice from seasoned mentors and industry experts.
  • In-depth support tailored to individual learning needs.




    • 24/7 Learning Support by DIYguru

  • Access round-the-clock assistance for a smooth learning experience.
  • Immediate response to academic and technical queries.
  • Dedicated support team available for continuous guidance.




    • Learn from IIT Faculty & Industry Experts

  • Gain insights directly from IIT Guwahati faculty.
  • Learn from industry professionals with practical experience in EVs.
  • Bridge the gap between theoretical concepts and real-world applications.




    • Tailored for Working Professionals and Freshers

  • Flexible learning pathways to suit diverse needs.
  • Accommodates the schedules of working professionals.
  • Offers a comprehensive introduction for freshers entering the EV sector.




    • Flexible Payment Options with No-Cost EMI

  • Access quality education with interest-free payment plans.
  • Ease the financial burden of pursuing advanced studies.
  • Multiple payment options available for added convenience.




    • Industry-Recognized EICT - IIT Guwahati Certification

  • Boost your credentials with a globally recognized certification.
  • Enhance your career prospects in the EV industry.
  • Certification recognized by leading industry players and employers.




    • Comprehensive Career Services by DIYguru

  • Resume building and LinkedIn profile optimization.
  • 1:1 mock interviews to prepare for job opportunities.
  • Dedicated career support to enhance employability.




    • Specialized Training from Tadpole Projects & EVI Technology Professionals

  • 24 hours of additional training focused on EV embedded applications.
  • Hands-on training for EV retrofitment and practical applications.
  • Gain expertise from industry-leading professionals.




    • 5-Day Campus Immersion at DIYguru COEs developed with L&T Edutech

  • Experience state-of-the-art labs and facilities.
  • Network with peers and industry leaders during the program.
  • Engage in on-campus activities for a deeper learning experience.




    • Hardware Kit delivered at door-step Worth ₹10,000/

  • Receive a kit developed by DIYguru in collaboration with IIT Delhi, Tadpole Projects, and EVI Technologies.
  • Enhance your learning with practical, hands-on experience.
  • Tools included for building and testing EV components.




    • NEAT AICTE & ASDC Accreditation

  • Accredited by NEAT AICTE and ASDC for high educational standards.
  • Ensures industry relevance and compliance with technical standards.
  • Recognized by employers across the EV industry.




    • Vast Industry Tie-Ups

  • Leverage DIYguru's extensive network of industry partnerships.
  • Access opportunities for job placements and industry exposure.
  • Collaborations with leading automotive and EV manufacturers.




    • Access to emobility.careers Portal

  • Leverage DIYguru's extensive network of industry partnerships.
  • Access opportunities for job placements and industry exposure.
  • Collaborations with leading automotive and EV manufacturers.
    • Admission Closes on 1st Nov

    Days Hours Minutes Seconds

    • Get more information

    Want to know more? Enter your information to learn more about this program from EICT - IIT Guwahati.

    • Career Opportunities


    Key Job Areas


    Job Roles


    Skill Sets


    Companies Hiring

    • 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
    • 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
    • FOR ENTERPRISE
    Looking to enroll your employees into this program ?
    • Inquire Now
    • Program Outcomes


    EICT - IITG Certification


    DIYguru + NEAT AICTE + ASDC


    Tadpole Projects + EVI Technology






    • 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
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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).
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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


    For Freshers


    For Professionals


    After Successfully Completion

    • 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.
    • 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

    Learn more

    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

    Learn more

    EV Retrofitment Solution

    The EV Retrofitment Solution manual provides guidelines for converting internal combustion engine vehicles (ICE) to electric vehicles (EV). It includes tests for system functionality, battery management, performance optimization, and safety feature integration. The manual focuses on ensuring proper installation, verifying throttle, pedal assist, and battery management systems, and conducting performance tests under real-world conditions
    LAB 3


    Learn more

    Electronics & Embedded System Development KIT

    The Electronics & Embedded Systems Development Kit is designed for hands-on learning with embedded systems and microcontrollers like Arduino and STM32. It covers key experiments like controlling LEDs, setting up Pulse Width Modulation (PWM) for dimming lights, serial communication, and sensor integration. Advanced projects include battery monitoring, motor control, and safety alert systems for electric vehicles​
    LAB 4


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    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

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    Advanced Battery System KIT

    The Advanced Battery System Kit is designed for developing, testing, and optimizing high-performance battery systems. It includes tools for monitoring battery health, managing thermal conditions, and ensuring efficient energy storage through load balancing circuits, thermal shutdown mechanisms, and Battery Management System (BMS) integration. This kit helps to extend battery lifespan and optimize charging cycles
    LAB 6


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