Introduction to Real-World Data Science.

A student-taught, project-based course for anyone who wants practical data science experience. No prior experience is required.

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(01) — Course overview

Learn by building.

What you will learn

Work through the full data science life cycle.

Learn how to transform a question about anything into a thoughtful result. The course covers project scoping, data cleaning, exploratory analysis, data visualization, machine learning, model evaluation, and deep learning, with an emphasis on understanding why each decision matters. Throughout the course, we connect these principles to real-world problems and show how data science teams apply them in industry.

What to expect from your project

Turn an idea into a finished project.

Choose a real-world question you care about and develop an end-to-end team project around it. You will define the scope, work with a dataset, build and evaluate an approach, and turn your findings into a clear final presentation. Throughout the process, Acadev instructors provide feedback and mentorship to help your team work through technical challenges, make thoughtful decisions, and communicate what you learned.

Any questions or want to learn more? Read the Q&A guide on Medium that one of our members wrote.

(02) — Inside the course

What students get

A guest lecturer speaking to DeCal students in a Berkeley lecture hall, slides projected on two screens behind him.
Hear guest lectures directly from faculty at UC Berkeley, including professors like Josh Grossman and Eric Van Dusen, as well as accomplished students working in the field.
A DeCal project group standing together at the front of their classroom after class.
Form a close bond with your project group and work closely with DSS mentors at every stage of the project.

(03) — Student work

See what students have built.

These featured projects show the range of questions students explore across domains with guidance from Acadev instructors.

Side-by-side comparison of an original face image and an AI-generated deepfake
DeCal project

Deepfake Detection with Computer Vision

Built a computer vision system with MobileNetV3 and XceptionNet to distinguish real face images from AI-generated deepfakes. The project combined visual and frequency analysis with data augmentation, dropout, and early stopping to create a more robust detection pipeline.

NBA player Shai Gilgeous-Alexander pictured above an MVP odds ranking graphic
DeCal project

NBA MVP Predictor

Analyzed historical player statistics to identify what separates NBA MVP candidates, then used Random Forest and Ridge regression models with feature reduction to predict and rank the leading contenders for the 2025 award.

County-level map of limited vehicle and supermarket access across the United States
DeCal project

Mapping Food Desert Risk Across the U.S.

Developed an interactive map of food desert risk scores across the United States, using machine learning and LLM-based analysis to help users explore geographic disparities in food access.

Bar and line chart illustrating quarterly e-commerce sales growth
DeCal project

E-Commerce Sales: Seasonal Spending Patterns in India

Compared pre-monsoon and early-monsoon transaction data to study shifts in spending behavior, then evaluated Random Forest and Logistic Regression models for predicting high-value purchases.

Hertzsprung-Russell diagram comparing star temperature, luminosity, and classification
DeCal project

Classifying Star Types

Explored how temperature, luminosity, and radius vary across different types of stars, then built a K-Nearest Neighbors model to classify stars from their physical characteristics.

Medical illustration of a human heart and cardiovascular system
DeCal project

Heart Failure Mortality Risk Analysis

Analyzed heart-failure clinical records to explore relationships between health indicators and mortality, combining visual analysis and chi-square testing with a K-Nearest Neighbors classification model.

Interested?

Learn more about the DeCal.

Visit the course site to see the syllabus and course materials from previous years.

Visit the DeCal site