DA-PYTHON.AW1
IoT Data Analytics using Python
Learn practical Python programming for IoT data analysis and how to use your talent and skills in a tech-driven world.
- 12 Interactive Lessons and 110 topics mapped to the official exam objectives
Intermediate Self-paced · 1 year access
01 / Skills you'll get
What you will be able to do
Enroll in our IoT Data Analysis course and learn how to turn raw IoT data into powerful insights using practical Python programming.
In this course, you’ll master IoT data analytics and visualization from the ground up. Starting with IoT fundamentals, setting up your analytics environment, and building real-time data pipelines. Discover how to clean, analyze, and visualize IoT data, implement predictive models, and deploy machine learning on edge devices.
From descriptive analytics with Pandas to time series forecasting and edge computing with MicroPython, you’ll gain hands-on experience with real-world problems.
- IoT Data Pipelines: Learn to set up and manage real-time data flows from IoT devices using Python, Kafka, and MQTT.
- Data Cleaning & Transformation: Master techniques to clean, wrangle, and prepare raw IoT data for analysis.
- Time Series Forecasting: Use Python libraries (Pandas, ARIMA) to analyze and predict trends in IoT sensor data.
- Edge Computing & Analytics: Deploy machine learning models on edge devices and optimize performance with MicroPython.
- Predictive Maintenance & Automation: Implement condition monitoring, text mining, and automated maintenance workflows.
- Real-World IoT Applications: Apply analytics to industry use cases, including self-driving cars, using the CRISP-DM framework.
Course Highlights
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12 Structured Lessons Comprehensive coverage of core course objectives
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1 Year Full Access Self-paced learning accessible anytime on all devices
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
12 Interactive Lessons · 110 topics01 Preface +
02 Necessity of Analytics Across IoT 10 topics +
- Introduction
- Internet of Things and Industrial Internet of Things
- Industrial Revolution and Industry 4.0
- IoT Data Analytics
- IoT Data Analytics for Digital Transformation
- Hardware Devices for IoT Data Analytics
- Data Pipeline for Analytics
- Python: The Go-to Language for Analytics
- Conclusion
- Points to Remember
03 Up and Running with Data Analytics Fundamentals 5 topics +
- Introduction
- Data Analysis Methods and Frameworks
- How to Perform Data Analysis
- Conclusion
- Points to Remember
04 Setting Up IoT Analytics Environment 13 topics +
- Introduction
- Why Python Language
- Installation and Configuration of Python IDE
- Installation and Configuration of Apache Kafka
- Installation and Configuration of MQTT
- Installation and Configuration of PostgresSQL
- Important Python Packages Used
- Basics of Python Language with Examples
- Data Analysis using Python
- Data Wrangling with Python
- Data Visualization using Python
- Conclusion
- Points to Remember
05 Managing Data Pipeline and Cleaning 13 topics +
- Introduction
- IoT Data Formats
- Realtime Streaming and Data Pipeline
- IoT Dataflow
- Data Simulation and Digital Twin
- Data Simulation
- Digital Twin
- IoT Simulator Tools
- IoT Data Simulator Python Implementation
- Data Cleansing Implementation in Python
- Data Transformation Rule Implementation in Python
- Conclusion
- Points to Remember
06 Designing Data Lake and Executing Data Transformation 8 topics +
- Introduction
- Data Lake Concept
- IoT Real-time Data Streaming
- Building Data Pipeline to the Raw Zone
- Transformation Zone of the Data Lake
- Building KPIs and Metrics
- Conclusion
- Points to Remember
07 Implementing Descriptive Analytics Using Pandas 7 topics +
- Introduction
- Descriptive Data Analysis
- Download Wind Turbine Dataset
- Time Series Analysis
- Testing Methods for Time Series Data
- Conclusion
- Points to Remember
08 Time Series Forecasting and Predictions 13 topics +
- Introduction
- Data Smoothing
- Data Lag Identification
- Autocorrelation and Partial Autocorrection
- Forecasting using AR Model
- Moving Average
- ARIMA
- Time Series Feature Extraction
- Automatic Time Series
- Storing Wind Turbine Predictions
- Analytical Base Table
- Conclusion
- Points to Remember
09 Monitoring and Preventive Maintenance 9 topics +
- Introduction
- Condition Monitoring
- Condition Based Maintenance
- Corrective Maintenance
- Preventive Maintenance
- Text Mining the Product Manual
- Automating the Creation of Maintenance Ticket
- Conclusion
- Points to Remember
10 Model Deployment on Edge Devices 13 topics +
- Introduction
- Objectives
- Introduction to Edge Computing and Analytics
- Simulators for IoT Systems
- Installation and Configuration of Edge Devices
- Installation and Configuration of FastAPI
- Model Building and Reuse
- Expose Models using FastAPI
- Deploying Machine Learning Model
- Concept of Continuous Learning
- Concept of Adaptive Learning
- Conclusion
- Points to Remember
11 Understanding Edge Computing with MicroPython 10 topics +
- Introduction
- Concepts of Edge Computing
- Concepts of Edge Analytics
- Introduction to Edge Platform
- Data Flow from Edge to Cloud
- Use Cases for Edge Analytics
- MicroPython for Edge Computing
- Invoking ML Models using MicroPython
- Conclusion
- Points to Remember
12 IoT Analytics for Self -driving Vehicles 9 topics +
- Introduction
- CRISP-DM Framework
- Business Understanding of Self-driving Vehicles
- Data Collection and Understanding
- Data Preparation and Feature Engineering
- Modeling and Evaluation
- Deployment of Machine Learning Models
- Conclusion
- Points to Remember
03 / FAQs
Questions before you start
What is IoT data analytics?+
IoT Data Analytics is the process of collecting, processing, and analyzing data generated by IoT (Internet of Things) devices to extract actionable insights.
It involves techniques like descriptive, diagnostic, predictive, and prescriptive analytics to optimize decision-making in industries such as manufacturing, healthcare, and smart cities.
Is Python good for IoT analytics?+
Yes! Python is one of the best languages for IoT analytics due to:
- Ease of use: simple syntax, quick prototyping
- Rich libraries: Pandas, NumPy, Scikit-learn for data analysis; MQTT, Flask for IoT communication
- Hardware compatibility: works with Raspberry Pi, Arduino, ESP32 via MicroPython
- Scalability: handles both small IoT projects and large cloud-based analytics
Hence, enroll in our data analytics for IoT course and benefit from Python’s features.
What tools/libraries will I use?+
In this Python for IoT analytics course, you’ll use the following tools:
- Data processing: Pandas, NumPy
- Visualization: Matplotlib, Seaborn
- IoT protocols: MQTT, HTTP
- Edge devices: Raspberry Pi, MicroPython
Keep going
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