5 modules
Build smarter solutions with drone data & Python.
5 Level NCrF
National Credit Framework
60 Hours
Flexible Learning
Beginner Level
No prior experience required
Micro Course
02 Credit
Course Overview
This course introduces learners to the fundamentals of data science applied to unmanned aerial systems (UAS). Students will gain hands-on experience in processing, cleaning, and analyzing drone-collected datasets using Python. By the end of the course, learners will be able to transform raw aerial data into meaningful patterns, insights, and visualizations to support decision-making in various industries.
Key Learning Highlights
- Learn Python programming for drone data analysis
- Process aerial imagery and sensor-based datasets
- Apply statistical and machine learning techniques to UAS data
- Create impactful data visualizations and reports
- Practical, project-driven learning with real drone datasets
Tools & Platforms Used
Learning Outcome
By the end of this course, students will be able to:
- Ability to identify and categorize different types of data.
- Proficiency in processing and visualization of the data.
- Understanding and Gaining Knowledge on types of data and its sources in a UAS.
- Using tools for data processing and management to enhance UAS operations.
- Using visualization tools for UAS data and applying data science to enhance operations.
Master the course with just 5 Modules
This course is designed to give you hands-on expertise in processing and analyzing drone-collected data using Python. Across 5 focused modules, you’ll learn to clean, manipulate, visualize, and extract meaningful insights from aerial datasets, equipping you with practical skills to apply data science in real-world UAS projects.
Module 1
Data Science: Benefits and uses – facets of data – Data Science Process: Overview – Data preparation – Exploratory Data analysis – build the model– presenting findings and building applications – Data Mining – Data Warehousing – Basic Statistical descriptions of Data.
Module 2
- Importing Matplotlib – Line plots – Scatter plots – visualizing errors – density and contour plots – Histograms – legends – colors – subplots – text and annotation – customization – three-dimensional plotting – Geographic Data with Basemap – Visualization with Seaborn.
Module 3
Types of Data – Imagery Data – Sensor Data – Telemetry Data – LIDAR Data – Data Acquisition Methods – Techniques for Data Collection – Integration of Data Sources.
Module 4
Data Cleaning – Removing Noise – Correcting Errors – Data Integration – Combining Data from Multiple Sources – Techniques for Integration – Data Analysis – Extracting Insights – Identifying Patterns – Data Storage – Data Warehousing Systems – Storage Solutions for Large Volumes – Data Compression – Techniques for Reducing File Sizes– Optimizing Storage and Transmission
Module 5
Applications – Mapping and Surveying – Agricultural Monitoring – Environmental Monitoring – Infrastructure Inspection – Data Visualization – Geospatial Visualization – 3D Modeling – Visualization Tools and Technique.
Roles
UAS Data Analyst
