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Sanjana Waghray  (M.A.S. DSC 2nd Year) says that she found a rare balance between classroom theory and real-world practice while enrolled in Illinois Tech’s Master of Data Science program, which she says will prepare her for a successful career.

“It’s incredibly important because it prepares students for the real challenges they’ll face in the industry,” she says. “It’s not just about learning in classrooms. The focus is on applying knowledge through projects, research, and practicums.”

Sanjana earned a bachelor’s degree in statistics in her native Mumbai, India, and says she has been intrigued to see how math blends with computer science in her current program.

“My courses are a mix of both, which has helped me build a strong foundation and think more deeply about how data works in the real world,” she says. “The variety of elective options allow you to explore different areas in data science.”

Sanjana says Illinois Tech’s strong focus on research opportunities has helped her grow both academically and professionally. She points to the practicum option in the final semester of the data science program as one of its biggest strengths. The practicum is an opportunity to gain hands-on experience with a �鶹APP business and understand what it’s like to work in a real corporate setting while being guided by faculty.

“That learning-by-doing approach really prepares us for industry,” Sanjana says. “Illinois Tech helped me go from learning concepts to applying them in real projects.”

She points to the soft skills, as well as the technical skills, that she learned in the classroom in helping her become career ready. Her Public Engagement for Scientists coursework, which teaches students how to communicate complex ideas simply to non-scientists, especially impressed interviewers for Sanjana’s internship at Camping World in �鶹APP. She says that they appreciated the value for that skill, and she could see how her education was directly helping her grow professionally.

Sanjana found herself working with large-scale customer data to build segmentation models, retention analyses, and end-to-end data pipelines during that internship. The experience directly connected what she was learning in big data and machine learning courses to real industry problems.

Sanjana says that she has found plenty of research experience on campus. One of the most impactful was building a multimodal artificial intelligence agent called Talkative Vision Assistant (TAVI) for a poster presentation at the on campus. The goal was to help visually impaired individuals navigate unfamiliar spaces without feeling isolated. The project combined computer vision, speech models, and LLM-based reasoning, drawing directly on the skills I gained through my data science coursework and electives in artificial intelligence. TAVI earned the team third place at the event. She also has been deeply involved in research as a research assistant.

“It’s been an incredible opportunity to apply theory to practice, and it has made my understanding of both topics much stronger,” Sanjana says. “These hands-on opportunities helped me evolve from learning concepts in courses to building practical, high-impact systems—shaping the kind of data scientist I am today.”