Masterclass e-Certificate in Artificial Intelligence, Internet of Things and Data Science

1. Introduction to IoT (2 hours)

• Definition and Components of IoT
• Types of IoT devices (Smart homes, Industrial IoT, Wearables, etc.)
• Communication protocols: MQTT, CoAP, HTTP, and Bluetooth
• IoT Architecture and Ecosystem

2. Sensors and Actuators in IoT (2 hours)

• Introduction to IoT sensors and actuators
• Types of sensors: Temperature, Humidity, Motion, Pressure, etc.
• Working with IoT sensors and actuators (Basic hands-on session)

3. IoT Data Communication (2 hours)

• Wireless Communication for IoT: Zigbee, LoRaWAN, Wi-Fi, and Bluetooth
• Data Transmission and Cloud Connectivity
• Introduction to IoT platforms: ThingSpeak, AWS IoT, Google Cloud IoT

4. Building IoT Applications (2 hours)

• Building a simple IoT system using an Arduino or Raspberry Pi
• Collecting data from sensors
• Sending data to the cloud for analysis
• Visualizing data from IoT devices

5. Security and Privacy in IoT (2 hours)

• Security challenges in IoT systems
• Encryption and Authentication techniques
• Privacy concerns in IoT devices and applications
• Best practices for securing IoT devices

1. Introduction to Data Science (2 hours)

• What is Data Science?
• The Data Science Life Cycle
• Role of Data Science in AI and IoT
• Overview of Data Analysis, Data Wrangling, and Visualization

2. Data Exploration and Preprocessing (2 hours)

• Types of data: Structured, Unstructured, and Semi-structured
• Handling Missing Data
• Data Normalization, Feature Engineering, and Feature Selection
• Data Preprocessing using Pandas (Hands-on demo)

3. Data Visualization (2 hours)

• Introduction to Data Visualization
• Tools: Matplotlib, Seaborn, and Plotly
• Visualizing different types of data: Time Series, Categories, Histograms, Box Plots
• Creating interactive visualizations for IoT/AI data (Hands-on session)

4. Statistical Analysis for Data Science (2 hours)

• Descriptive Statistics: Mean, Median, Mode, Standard Deviation
• Inferential Statistics: Hypothesis Testing, p-values, Confidence Intervals
• Correlation and Causation
• Hands-on demonstration using Python libraries (NumPy, SciPy)

5. Machine Learning in Data Science (2 hours)

• Overview of Supervised vs. Unsupervised Learning
• Key algorithms for Data Science: Linear Regression, Decision Trees, Clustering (K-means), and Dimensionality Reduction (PCA)
• Hands-on session: Using Scikit-learn to build predictive models

6. Big Data and Data Science (2 hours)

• Introduction to Big Data Concepts
• Technologies used in Big Data: Hadoop, Spark, and NoSQL databases
• Data Processing in Big Data Environments
• Handling large-scale IoT or AI data sets

1. AI and Data Science in IoT (2 hours)

• Role of AI in IoT: Predictive Analytics and Automation
• IoT Data Processing with AI: Real-time analysis, anomaly detection
• Example use case: Smart Cities, Healthcare, and Industrial IoT

2. Building End-to-End Solutions (2 hours)

• Combining AI, IoT, and Data Science for a complete solution
• Example Project: Building a Smart Home System with AI-driven automation and Data Analysis
• Hands-on demo: Integrating AI models with IoT sensors for smart predictions

3. Case Studies and Real-world Applications (1 hour)

• Reviewing successful case studies (Healthcare, Smart Cities, Predictive Maintenance)
• Discussing industry trends and future opportunities for AI, IoT, and Data Science
• How AI, IoT, and Data Science are reshaping industries

• Recap of key concepts from AI, IoT, and Data Science
• Final Q&A and discussion on how to move forward with further learning or real-world applications
• Final thoughts and resources for continued learning
• Hands-on: Building an AI-based AR or VR application (e.g., using AI for object recognition or scene understanding)

• Mid-course Quiz covering key concepts from the AI, IoT, and Data Science modules
• Final Project: An end-to-end solution combining IoT, AI, and Data Science
• Certification awarded upon completion of the course and project submission