{"id":117476,"date":"2026-04-29T12:38:12","date_gmt":"2026-04-29T07:08:12","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/how-natalia-vazquez-colon-transitioned-from-epidemiology-to-finance\/"},"modified":"2026-04-29T09:17:08","modified_gmt":"2026-04-29T03:47:08","slug":"how-natalia-vazquez-colon-transitioned-from-epidemiology-to-finance","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/how-natalia-vazquez-colon-transitioned-from-epidemiology-to-finance\/","title":{"rendered":"MIT Professional Education Applied AI & Data Science Review: How Natalia V\u00e1zquez-Col\u00f3n Transitioned from Epidemiology to Finance"},"content":{"rendered":"\n

Natalia V\u00e1zquez-Col\u00f3n<\/a> had always lived at the intersection of numbers and impact. Trained in epidemiology and quantitative analysis, she had spent years working with surveillance data, building statistical models, and developing dashboards that helped public health systems make sense of complex, high-stakes information. She was good at her work. The problem wasn't her skill. It was the fragility of the sector in which she had built her career.<\/p>\n\n\n\n

When public health funding became increasingly unstable, Natalia faced a choice that many analytically-minded professionals quietly dread: do you wait it out, or do you deliberately expand so that the uncertainty becomes irrelevant? She chose to widen her scope by investing in upskilling by enrolling in the Applied AI and Data Science Program from MIT Professional Education, offered in collaboration with Great Learning.<\/p>\n\n\n\n

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<\/div>\n Explore Data Science Program<\/span>\n <\/div>\n

\n MIT Professional Education's Data Science Course<\/a>\n <\/p>\n

Gain the expertise top companies seek and open doors to Data Science jobs.<\/p>\n

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<\/div>\n Duration: 12 weeks<\/span>\n <\/div>\n
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<\/div>\n Ratings: 3<\/span>\n <\/div>\n <\/div>\n \n Discover the Program\n
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\n \"A key challenge was navigating career uncertainty in public health during periods of funding instability,\" she says. \"Rather than narrowing my focus, I chose to expand my technical depth so my skills would remain adaptable across industries. Investing in advanced analytics strengthened both my flexibility and resilience in an uncertain job market.\"\n<\/div>\n\n\n\n

Before the Program<\/strong><\/h2>\n\n\n\n

As she explored opportunities beyond public health, she began to encounter a consistent gap in the skills employers were seeking. machine learning and Python, in particular, were becoming essential requirements across industries such as finance, consulting, operations, and applied data science. While she already knew how to think like a scientist, she wanted to build like one, too.<\/p>\n\n\n\n

\n \"My background is in epidemiology and quantitative analysis,\" Natalia explains, \"working with surveillance data, statistical modeling, and dashboard development across research and applied settings. While I was experienced in statistical analysis using R, I wanted to deepen my expertise with machine learning and Python to broaden the types of problems I could solve.\"\n<\/div>\n\n\n\n

She wasn't looking for a career reinvention. She was looking to expand her career, and she needed a program that understood the difference.<\/p>\n\n\n\n

Why She Chose the MIT Professional Education Applied AI and Data Science Program<\/strong>?<\/h2>\n\n\n\n

For someone with Natalia's profile, technically grounded but looking to bridge from one analytical tradition into another, the choice of program mattered a lot. She needed something that would meet her at her level and provide genuine depth in areas where she had gaps.<\/p>\n\n\n\n

Applied AI and Data Science Program from MIT Professional Education, offered exactly that combination: 27+ hours of live online sessions from MIT faculty, multiple hands-on projects, and a curriculum infused with the latest advancements in artificial intelligence and generative AI. The Program included extensive work with high-demand tools such as Python, TensorFlow, and ChatGPT.<\/p>\n\n\n\n

The program's emphasis on real-world application, with 50+ case studies and a capstone project focused on solving actual business problems, matched how Natalia already thought about problems: from data to decision.<\/p>\n\n\n\n

The program's recognition and institutional credibility also mattered. MIT is ranked #1 university in the world (QS World University Rankings in AI and Data
Science and the #2 national university in the U.S. (U.S. News & World Report). Upon completion, the program offered a Certificate of Completion from MIT
Professional Education and 16 Continuing Education Units (CEUs).<\/p>\n\n\n\n

The Learning Journey: Scientific Thinking Meets Machine Learning<\/strong><\/h2>\n\n\n\n

Natalia entered the program with strong statistical knowledge and exited it with a substantively expanded technical toolkit.<\/p>\n\n\n\n

The curriculum covered foundational Python programming and statistics, machine learning model building and evaluation, deep learning, computer vision, recommendation systems, generative AI and prompt engineering, retrieval-augmented generation (RAG), and agentic AI approaches. She worked with tools including Python, TensorFlow, ChatGPT, Hugging Face Transformers, and OpenCV throughout the program.<\/p>\n\n\n\n

The capstone project brought everything together in the most concrete way possible. Natalia built and evaluated machine learning models in Python for a loan default prediction problem, a genuine finance domain challenge that sits squarely within the program's sample capstone projects. It was her first sustained experience applying ML methodology to a finance domain, and it clicked in an important way.<\/p>\n\n\n\n

\n \"Through hands-on projects, including a capstone on loan default prediction, I built and evaluated machine learning models in Python,\" she reflects. \"The experience reinforced that scientific reasoning and analytical thinking are transferable across sectors, from public health to finance and applied data science.\"\n<\/div>\n\n\n\n

That realization, that the discipline of thinking she had developed in epidemiology was the same discipline machine learning demanded, was more than reassuring. It was clarifying. She wasn't changing who she was professionally. She was extending what she could do.<\/p>\n\n\n\n

Real Impact at Work<\/strong><\/h2>\n\n\n\n

The proof is in how Natalia works now. She currently operates across two domains simultaneously, the financial sector and ongoing health-related projects, in a way that would have been much harder before the MIT Applied AI and Data Science Program gave her the technical vocabulary and tools to move fluidly between them.<\/p>\n\n\n\n

The machine learning models she built during the program. The Python workflows she developed. The understanding of how to apply AI to structured prediction problems. These are now part of her professional toolkit, extending what her prior R-based training had built, because Python and ML opened different doors.<\/p>\n\n\n\n

Her ability to work across finance and public health simultaneously isn't a compromise or a side effect. It's the outcome she designed for, and the program helped her get there.<\/p>\n\n\n\n

\n I currently work in the financial sector while continuing to manage health-related projects, which reflects how transferable scientific and analytical skills truly are.\n<\/div>\n\n\n\n

Who Should Take This Program?<\/strong><\/h2>\n\n\n\n

The program is designed for three professional profiles: <\/p>\n\n\n\n