All Advance course

Industrial Training on IIoT 4.0 and Machine Learning
Advance Course
Industrial Training on IIoT 4.0 and Machine Learning

Module 1: Introduction to Industry 4.0 & IIoT

Objectives:

  • Understand Industry 4.0 concepts, smart factories, and cyber-physical systems.

  • Identify IIoT use cases in manufacturing, utilities, logistics, etc.

Key Topics:

  • Evolution from Industry 1.0 to 4.0

  • Components: CPS, Cloud, Edge, Big Data, AI

  • What is IIoT? Difference between IoT and IIoT

  • Industrial use cases: predictive maintenance, OEE monitoring, energy optimization, asset tracking

Moodle items:

  • Resource: PDF/Presentation – “Industry 4.0 & IIoT Fundamentals”

  • URL: 1–2 short videos (YouTube)

  • Activity: Quiz 1 (MCQs on concepts)

  • Forum: “Share an IIoT use case from your industry”

Module 2: IIoT Architecture, Protocols & Platforms

Objectives:

  • Explain IIoT reference architectures.

  • Understand industrial communication protocols.

Key Topics:

  • IIoT reference architecture (sensor → edge → gateway → cloud → dashboard)

  • Industrial connectivity & protocols:

    • MQTT, HTTP/REST, CoAP

    • OPC UA, Modbus basics

  • Edge vs Cloud vs On-prem deployment

  • Overview of common IIoT platforms (open-source & cloud)

Moodle items:

  • Page/Book: “IIoT Architecture & Protocols Overview”

  • Assignment: Draw your organization’s current architecture and propose an IIoT-enabled version (upload PDF/image).

  • Quiz 2: Protocols & architecture.

Module 3: Sensors, Edge Devices & Data Acquisition

Objectives:

  • Understand field devices, sensors, and edge hardware.

  • Learn how data is collected and streamed.

Key Topics:

  • Types of industrial sensors: temperature, vibration, pressure, flow, current, proximity, etc.

  • Microcontrollers & edge devices (generic explanation: PLCs, Raspberry Pi, ESP32, industrial gateways)

  • Data acquisition basics: sampling rate, resolution, signal conditioning

  • Streaming data to broker/cloud (MQTT publisher–subscriber pattern)

  • Data logging: CSV, time-series DB, dashboards

Moodle items:

  • Resource: PDF with sample wiring diagrams or conceptual diagrams

  • URL/Lab sheet: Simple “MQTT publisher/subscriber” demo (even if simulated)

  • Assignment: Short report – “Identify 5 important sensors for your plant/use case and justify why”.

Module 4: Data Engineering for IIoT Analytics

Objectives:

  • Prepare raw IIoT data for ML.

  • Understand handling of time-series and event data.

Key Topics:

  • Data types in IIoT: time-series, events, alarms, logs

  • Data cleaning: missing data, noise, outliers

  • Feature extraction/engineering from sensor data:

    • Statistical features: mean, RMS, peak, variance, rolling window stats

    • Time-lag features, moving averages, aggregation

  • Basic data pipelines: ingest → clean → transform → store

  • Tools overview: Python (pandas), Jupyter, basic CSV operations

Moodle items:

  • Resource: Jupyter notebook/PDF: “Basic preprocessing of IIoT sensor data”

  • Assignment: Mini-task – Upload a CSV (provided) after cleaning & adding 2–3 features.

  • Quiz 3: Data preprocessing & time-series basics.

Module 5: Machine Learning for IIoT Applications

Objectives:

  • Train basic ML models for industrial use cases.

  • Interpret model outputs for decision-making.

Key Topics:

  • Quick recap: supervised vs unsupervised learning

  • Common ML tasks in IIoT:

    • Predictive maintenance (remaining useful life, breakdown prediction)

    • Anomaly detection (faults, leakage, abnormal vibration)

    • Quality prediction (pass/fail, defect classification)

  • Algorithms (conceptual, not super heavy maths):

    • Regression: Linear/Random Forest Regressor

    • Classification: Logistic Regression, Random Forest, XGBoost (intro)

    • Clustering: k-Means for anomaly detection

  • Model evaluation metrics: accuracy, precision, recall, F1, ROC-AUC, confusion matrix

  • Interpreting results in an industrial context (reducing downtime, cost savings).

Moodle items:

  • Resource: Notebook or PDF – sample ML pipeline on sensor dataset

  • Quiz 4: ML concepts & metrics

  • Assignment: Build a simple ML model (or concept report) using provided dataset (predict normal/faulty condition).

Module 6: Edge AI & Real-time Deployment Concepts

Objectives:

  • Understand deployment challenges in real-time industrial environments.

  • Introduce Edge AI and lightweight models.

Key Topics:

  • Why Edge AI? Latency, bandwidth, privacy, reliability

  • Deploying models on edge devices (concept level, not deep coding):

    • Model compression basics (quantization, pruning – high level)

    • Real-time inference pipeline

  • Integration with dashboards and alert systems

  • Cybersecurity basics in IIoT & ML systems

Moodle items:

  • Page: “From Prototype to Production: Deploying ML in IIoT Environments”

  • Forum: Discussion – “Cloud vs Edge: What is realistic for your industry?”

  • Short assignment: Design a conceptual architecture diagram showing where the ML model will run.

Module 7: Capstone Project & Assessment

Objectives:

  • Apply the entire pipeline from IIoT concept to ML model & deployment plan.

  • Present a practical, industry-oriented solution.

Capstone Task (example):
Learners must choose one industrial problem, e.g.:

  • Predictive maintenance of a motor/pump

  • Energy consumption optimization in a small plant

  • Temperature/humidity monitoring with anomaly alerts

  • Production line defect detection concept

Deliverables (for Moodle submission):

  1. Architecture Diagram – Sensors → Edge → Gateway → Cloud → Analytics.

  2. Data & ML Plan – What data, features, and ML algorithm will be used?

  3. Demo/Prototype (optional if time) – Notebook, screenshots, or simulation.

  4. Business Impact Note – 1–2 pages on cost saving, reliability, safety, or efficiency improvement.

Moodle items:

  • Assignment (Project Report + Files Upload)

  • Activity: Online Presentation (via BigBlueButton/Zoom link) or offline evaluation

  • Feedback form (Questionnaire) for course evaluation.

Advance Excel
Advance Course
Advance Excel

🎯 Course Objective

To equip learners with advanced Excel tools for data analysis, financial modeling, automation, and dashboard creation, aligned with corporate productivity, analytics, and MIS roles.

🧩 Module-Wise Breakdown

📦 Module 1: Excel Efficiency Tools

  • Keyboard shortcuts for productivity

  • Named ranges & cell referencing (absolute vs relative)

  • Paste special, quick analysis, and flash fill

  • Custom views and worksheet management

📊 Module 2: Advanced Formulas & Functions

  • Logical Functions: IF, IFS, AND, OR, IFERROR

  • Lookup Functions: VLOOKUP, HLOOKUP, INDEX & MATCH, XLOOKUP (Excel 365)

  • Text Functions: LEFT, RIGHT, MID, CONCATENATE, TEXTJOIN

  • Date & Time: TODAY, NOW, NETWORKDAYS, DATEDIF

  • Math/Statistical: SUMIF, COUNTIF, AVERAGEIF, RANK, ROUND, RAND, RANDBETWEEN

  • Array Formulas: FILTER, SORT, UNIQUE, TRANSPOSE

📊 Module 3: Data Cleaning & Validation

  • Data preparation and cleansing techniques

  • Removing duplicates, trimming spaces

  • Data validation (drop-down lists, custom rules)

  • Text to columns, split and combine data

  • Power Query introduction

📉 Module 4: Data Analysis & Pivot Tables

  • Creating and customizing pivot tables

  • Multi-level pivot table analysis

  • Calculated fields & pivot charts

  • Grouping and filtering

  • Slicers and timelines

  • Drill-down analysis

📈 Module 5: Charts and Visualization

  • Recommended charts vs custom charts

  • Combo charts, dual axis charts

  • Conditional formatting with icons and color scales

  • Dynamic charts with named ranges

  • Data bars, sparklines, trendlines

📊 Module 6: Dashboard Design

  • Principles of dashboard design (KPI-driven)

  • Linking slicers, interactive visualizations

  • Dynamic charting and data binding

  • Using form controls (buttons, drop-downs, checkboxes)

  • Final project: Build a Sales or HR dashboard

⚙️ Module 7: Excel Automation with Macros (VBA Basics)

  • Introduction to Macros & security settings

  • Recording basic macros

  • VBA editor walkthrough

  • Writing simple VBA code (loops, MsgBox, functions)

  • Automating repetitive tasks

💼 Module 8: Excel in Business Applications

  • MIS Reporting automation

  • Inventory/stock management models

  • HR salary sheets and attendance trackers

  • Financial modeling basics (NPV, IRR, break-even)

  • Budgeting and forecasting templates

🎓 Capstone Project

Choose one:

  • Dynamic business dashboard

  • HR MIS report generator

  • Sales/Finance KPI tracker

  • Automated data consolidation workbook

🏆 Learning Outcomes

After completing this course, learners will:

  • Create advanced analytical reports with pivot tables and dashboards

  • Automate tasks using macros and basic VBA

  • Handle real-world business datasets confidently

  • Be job-ready for MIS Analyst, Business Analyst, or Excel Automation roles

📜 Certification Criteria

  • Minimum 80% module completion

  • At least 1 capstone project submission

  • Final quiz score ≥ 70%