@viviantech
Turning small tasks into big insights | Data Science Learner | Public Health & Community Impact
Data science learner with a background in public health and community resilience. Passionate about turning data into actionable insights, from simple Python scripts to exploring how AI can solve real-world problems. Here on Hashnode, I document my learning journey in Python, data science, and AI, sharing projects, reflections, and beginner-friendly guides. Believer in using tech, storytelling, and volunteering to create impact.
Data Science Internships → hands-on projects in Python, data cleaning, and analytics. Research Collaborations → applying data-driven insights to sustainability, health, and education. Community Projects → building volunteer-driven systems for impact and resilience. Content & Knowledge Sharing → writing beginner-friendly guides, insights, and tutorials. Consulting & Partnerships → bridging public health expertise with data-driven decision-making.
Feb 17 · 4 min read · Week 18 of my data science bootcamp introduced tree-based classification through three progressively complex tasks, customer purchase prediction, diabetes risk classification, and employee attrition pipeline with quantified business recommendations. ...
Join discussionFeb 7 · 5 min read · Introduction – Why This Task Mattered At first glance, the objective of this take-home task was straightforward: predict crop yield using historical weather data. However, beyond building a functioning model, this exercise was an opportunity to demon...
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Jan 25 · 5 min read · When I first opened the take‑home task, the goal sounded simple on paper: predict crop yield using weather data. But behind that single sentence was a much broader challenge, an assignment designed to test not just whether I could run models, but whe...
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Jan 19 · 5 min read · As a young female data science intern navigating the competitive tech landscape, I received a career-defining challenge: a comprehensive take-home assignment testing every regression technique from polynomials to production pipelines. Six datasets ar...
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Jan 7 · 6 min read · Introduction One of the biggest misconceptions about machine learning is that models do the hard work. In reality, models are only as good as the data they learn from. As part of an intensive take-home project, I worked through a complete data scienc...
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