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Not all concrete is created equal. Wei “Great” Chen knows this as a construction project engineer and manager in Berkeley, California. One of the key things he checks is concrete compressive strength.

Great has conducted many laboratory and on-site tests using conventional methods, analyzing the way cement, water, additives, and curing time affect strength. So, he was stunned when he saw how deep learning could speed up and improve the process.

“Honestly, it felt like magic at first,” says Great, who is enrolled in the Deep Learning Fundamentals Lab at WorldQuant University.

Deep learning, a subset of machine learning, uses artificial neural networks to teach computers how to process data, recognize patterns, and make decisions in a way that mimics the human brain. One applied project in the free, online certificate program at WorldQuant University is “Predicting Concrete Strength with Deep Learning.”

“What surprised me most was how the model could capture non-linear relationships that I know from experience exist but are hard to write down as a simple formula,” Great says. “Things like how water ratio and superplasticizer interact or how age affects strength in a non-linear way. The model just learned it from the data.”

Great, who came to the United States from China to pursue advanced studies in real estate development and financial engineering, says the potential benefits from machine learning are huge.

“In real projects, being able to predict concrete strength early before lab tests come back could help us catch quality issues sooners, adjust mixes, or approve pours with more confidence,” he says. “I can already see practical value for my work.”

Besides the Deep Learning Fundamentals Lab, Great is enrolled in the Data Science Lab and will begin the Master of Science in Financial Engineering program in July 2026.

“WorldQuant University’s unique mission of offering tuition‑free, high‑quality education in data science and deep learning immediately caught my attention,” he says. “The fact that I could apply these skills directly to my work in construction quality—predicting concrete strength—made it an ideal fit.”

Great started his career in urban renewal development after earning degrees in engineering and economics. He then moved to real estate project management, overseeing construction quality for residential and commercial projects. He says data science and machine learning have opened a new dimension for him.

“My journey with WQU has been a pivotal milestone,” he says. “It has given me the quantitative and financial expertise to analyze stock investments and real estate opportunities more effectively, directly boosting my profitability and analytical capability in financial markets.”