Instructors
Omid Abari, Associate Professor, Department of Computer Science, UCLA
Baharan Mirzasoleiman, Assistant Professor, Department of Computer Science, UCLA
Coursework Prerequisites
The prerequisite for this class is Algebra II or equivalent. Students should also be familiar with basic calculus and Computer Science. Although the course reviews the relevant programming that is necessary throughout the course, it is recommended to have programming experience in Python (or at least similar languages).
Course Description
Imagine a world where billions of devices — from tiny sensors in your home to smart systems in cities and farms — are constantly collecting and analyzing data, shaping how we live, work, and play. This program dives into the dynamic intersection of Internet of Things (IoT) and Data Science, where connected devices meet intelligent data analysis. Students will explore how sensors and smart devices generate data that can be harnessed to understand and improve real-world systems — from smart homes and digital health to precision agriculture and smart cities. Through a blend of lectures, hands-on labs, and projects, participants will learn the full data science lifecycle — including data collection, data cleaning and exploration, and predictive modeling on the cloud — while working directly with IoT platforms such as WiFi, Bluetooth, LoRa, and RFID. By the end of the program, students will have designed and analyzed their own Data Science and IoT-driven experiments, gaining practical skills in both sensor integration and data-driven decision making that power the connected world of tomorrow.
Structure
This 4-week course is designed to give students a hands-on introduction on how Data Science and Internet of Things (IoT) technologies make our world smarter. The program focuses each day on both topics and their connection. Lectures will be held each weekday morning for 2.5 hours by faculty and instructors, followed by a lunch break. Afternoons consist of 3-4 hours of lab work with teaching assistants, providing students the opportunity to reinforce lecture concepts through coding exercises, sensor integration, and small-scale experiments. The course culminates in the final week with a capstone project, where students will design, build, and analyze a fully functional IoT system that leverages both hardware and data analytics skills learned throughout the program, integrating ESP32 modules with data science methods to solve a real-world problem.
Final Project
At the culmination of this cluster, students will have the opportunity to implement an end-to-end IoT and Data Science system, fully integrating all of the concepts learned throughout the cluster. Successful completion of the final project will involve collecting data from IoT devices, transmitting it to the cloud or their laptop, and performing data analysis and visualization to extract meaningful insights.
Possible Field Trips
Research Labs at UCLA: students will visit research labs working actively on Data Science and IoT.
On these mini field trips, students will see how advanced IoT systems powered with Data Science and Machine Learning can enable emerging applications such as fruit ripeness sensing, gesture sensing using wireless signals, and battery less temperature and moisture sensing.