News

Some learners like WorldQuant University so much, they come back for a second academic offering. Alexander Zhukov did more. He completed the Deep Learning Fundamentals Lab on Feb. 2, 2026, giving him three certificates from the institution.

Alexander, originally from Kyrgyzstan and now living in Russia, previously finished the Applied Data Science Lab in 2024 and the Computer Vision Lab in 2025.

“I completed all three certificate programs and especially appreciated the balance between theory and practice,” Alexander says. “The programs helped me develop a more formal and systematic approach to data, analytics pipelines, and validation of results.”

Alexander says the coursework aligned with the progression of his career, which started in the telecommunications industry. Over time, he gained experience in commercial projects and eventually shifted his focus toward data analytics.

“My interest in data science and artificial intelligence developed naturally from my engineering background,” he says. “I have always been interested in building systems that transform raw data into clear, reliable, and reproducible results, rather than working with data in isolation.”

Today, Alexander leads an analytics team in the public sector but plans to continue growing.

“Continuous learning is essential at any stage of a professional career,” he says.

His enrollment in the Deep Learning Fundamentals Lab shows his eagerness to explore new frontiers. WorldQuant University launched the free, online, 16-week certificate program in September 2025, and Alexander quickly signed up.

Using PyTorch, a leading deep learning framework, participants build neural networks, learn image classification, and solve real-world problems related to health care, engineering, and other STEM fields.

“I particularly appreciate the project-based learning approach,” Alexander says. “Instead of theoretical lectures, we immediately immersed ourselves in practical applications, implementing real neural networks and solving image classification problems.”

The format allowed him to explore convolutional neural networks (CNNs) and achieve tangible results on his first projects. He says the most challenging aspect was not the technical concepts, but the instructional language.

Alexander is not a native English speaker, so he had to work hard to express himself precisely on graded assignments. He persevered and met all requirements with help from his classmates and instructors.

“The quality of the materials and the community support are excellent,” he says. “It’s clear that a team of professionals is behind the program.”