•Conducted predictive time series analysis using ARIMA models to forecast student headcount for resource allocation in upcoming yearsand generate insights to assist in strategic decision-making.•Developed and deployed automated Python scripts to streamline the collection and analysis of Qualtrics survey data using Qualtrics APIsand preprocess the retrieved data in a structured manner.•Assessed performance metrics of API requests to optimize survey data retrieval and processing. By fine-tuning the requests, I reducedresponse times and improved the overall data collection and analysis process.•Implemented automated tableau dashboard pipeline using AWS S3 using Alteryx, this reduced resource load by 25% and facilitated theseamless visualization of data, enabling stakeholders to monitor student engagement trends and make data-driven decisions.•Led pipelines that automated the collection, cleaning, and processing of student records and automating monthly data refreshes.

•Implemented a state-of-the-art pharmaceutical excipient recommender system using Collaborative Filtering to assist medical professionals in choosing optimal medications.•Utilized data mining techniques and OCR to process and filter over 20GB of heterogeneous medical data, to extract necessary information from diverse sources, ensuring data quality and consistency for subsequent analysis.•Developed a robust model using TensorFlow and Keras that enhanced the identification of allergic and harmful excipients in medications.•Employed SQL and a backend database system for storage and ensured data integrity, optimized performance, and maintained a well-structured database.

•Developed an application to deploy a profile assessment test for entry-level candidates, delivering a streamlined and user-friendly experience and ensured efficient data handling and processing.•Transformed the application into a scalable platform capable of analyzing up to 10,000 entries by utilizing MySQL as the database to efficiently store and manage the assessment data which resulted in a 50% improvement in efficiency compared to the initial model.•Leveraged AWS Comprehend to deploy a dashboard for textual data analysis. This dashboard enabled the identification of the most frequent assessment topics and the extraction of relevant features from the text data.•Utilized data visualization techniques to present insights to stakeholders and generated monthly reports for analysis and decision-making.•Collaborated with cross-functional teams to gather requirements, ensure data accuracy, and align analytical outputs with business objectives.