Graphene-Based Sensor Data Analytics

Introduction to the Course

Course Objectives

• Understand the basics of graphene sensor technology and its usage in different sectors.
• Learn how to extract, preprocess, and clean data from graphene sensors for analysis.
• Get practical experience in signal processing and noise removal techniques for sensors.
• Learn advanced machine learning algorithms for pattern recognition, anomaly detection, and predictive analytics in sensor data analysis.
• Understand how data visualization methods can be used to analyze sensor readings in real-time applications.
• Develop the skill to use analytics for improving sensor performance and decision-making processes in different sectors.

What Will You Learn (Modules)

Module 1 – Signal Preprocessing for SAW Gas Sensors

• Overview of Surface Acoustic Wave (SAW) Gas Sensors
• Signal Characteristics of Graphene-Based Sensing Devices
• Algorithms for Signal Smoothing and Normalization

Module 2 – Anomaly Detection Using Autoencoders

• Unsupervised Anomaly Detection
• Autoencoders
• Sensor-Specific Anomalies

Module 3 – Hands-On: Transfer Learning & Adaptation to New Analytes

• Transfer Learning Introduction
• Visualize and load preprocessed SAW sensor data.
• Use a pretrained autoencoder for anomaly detection on the data.

Who Should Take This Course?

This course is ideal for:

• Materials scientists involved in the application of graphene
• Data scientists and analysts applying machine learning algorithms to sensor data
• Biomedical engineers using sensors for medical diagnostics and health monitoring
• Environmental scientists monitoring air quality, water quality, and other ecological parameters using sensors
• Engineers involved in electronics and smart systems using advanced sensors

Job Opportunities

After completing this course, learners can pursue roles such as:

• Sensor Data Scientist
• Graphene Materials Engineer
• Biomedical Data Analyst (Graphene Sensors)
• Environmental Monitoring Specialist (Sensors)
• Smart Systems Engineer (Sensors)

Why Learn With Nanoschool?

At NanoSchool, we focus on career-relevant learning that builds real capability—not just theory.

• Expert-led training: Learn from instructors with real-world experience in applying skills to industry and research problems.
• Practical & hands-on approach: Build skills through guided activities, templates, and task-based learning you can apply immediately.
• Industry-aligned curriculum: Course content is designed around current tools, workflows, and expectations from employers.
• Portfolio-ready outcomes: Create outputs you can showcase in interviews, academic profiles, proposals, or real work.
• Learner support: Get structured guidance, clear learning paths, and support to stay consistent and finish strong.

Key outcomes of the course

Upon completion, learners will be able to:

• In-depth knowledge of graphene-based sensors and their use in bioscience, healthcare, and environmental applications
• Practical skills in signal processing, machine learning, and data visualization techniques for sensor data analysis
• Skills to develop and implement real-time analytics systems for decision-making using sensor data
• A project to showcase your skills in sensor data analytics, ready to be included in your portfolio
• Industry-ready skills for data science, sensor development, and smart system design