Seneca Polytechnic's Data-Driven Solutions Capstone is designed for aspiring data science students eager to apply their analytical skills to real-world challenges. Participants will leverage their knowledge of data analysis and machine learning to tackle a specific business problem or research question using an existing dataset. This experience empowers learners to translate theoretical concepts into practical solutions, enhancing their ability to derive insights and make data-driven decisions. By collaborating with industry professionals, learners will gain valuable exposure to the nuances of applying data science in a business context.

Students will act as consultants and will work with you to solve a business problem.

Project outcomes:

1. Data Preparation: Perform data cleaning, preprocessing, and exploratory analysis to ensure the dataset is ready for modeling.
2. Machine Learning Component: Develop, train, and evaluate a machine learning model to solve the identified problem. Students may explore supervised, unsupervised, or deep learning techniques based on the problem domain.
3. Evaluation: Assess model performance using appropriate metrics, compare multiple models, and refine as needed.
4. Visualizations: Create insightful visualizations to illustrate findings, model performance, and key trends in the data.
5. Presentation: Summarize the project outcomes in a final presentation, communicating the methodology, insights, and impact of their work.

Deliverables (to confirm with Elnaz):

• Project proposal
• Comprehensive data analysis report
• Documentation of the project process and outcomes
• Predictive model with performance metrics
• Evaluation results
• Data visualization
• Presentation of findings and recommendations