The University of the Pacific (UOP) seeks to enhance its resource planning efforts for incoming freshmen students by gaining insights into the courses they are likely to take in their first Fall semester based on factors such as declared major and high school GPA. Additionally, I aims to explore how students progress through their chosen programs' curricula. To achieve these goals, I am looking to create a data pipeline for data manipulation and filtering, conduct extensive exploratory data analysis (EDA), design a robust data model, and develop an interactive multi-report dashboard using PowerBI.
The project at hand revolves around the analysis of UOP's 1st-year courses and curriculum data to improve resource planning and understand students' academic journeys.
Data:
The project utilizes two primary datasets:
Enrolled Students Data (enrolled_students.zip): This dataset contains demographic information about all enrolled undergraduate students for various semesters since 2005.
Academic Grades Report Data (academic_grades_report.zip): This dataset includes information about the courses taken by students, including grades obtained for each class for various semesters.
• The primary objective is to determine which courses incoming freshmen typically enroll in during their first Fall semester, based on their declared majors.
• Deliverables include CSV files showing the enrolment fractions of freshmen for each course, a file displaying the standard deviation of enrolment fractions since 2010, and dashboards to visualize the data.
• Identifying "First Time Freshmen" is a crucial step in this part, which is based on the 'STYP_DESC' field.
• In this phase, the project focuses on understanding the academic paths students follow within specific majors or programs.
• This phase involves extensive exploratory data analysis (EDA) to uncover various paths and course sequences.
Data Pipeline and Analysis:
The project involves developing a data pipeline for loading, integrating, manipulating, and filtering data. This pipeline aims to improve data processing efficiency and accuracy, potentially reducing processing time by 30%. Missing data is handled to ensure data accuracy rates reach 95%. Feature engineering techniques were implemented to enhance the predictive capabilities of the data model within PowerBI, potentially improving forecast accuracy by 20%.
A comprehensive reporting system is designed and implemented in PowerBI. This system consist of multiple pages, including a departmental page, a matrix page displaying course enrollment statistics (mean and standard deviation), and a detailed course page. The interactive dashboard allows stakeholders to access and analyze data efficiently, potentially increasing decision-making efficiency by 40%.
Advanced functionalities, such as drill-down, filter panels, reset filter buttons, and information panels, are incorporated into the reporting system to enhance user experience. Collaborative efforts with cross-functional teams ensures data quality improvements of up to 20%.
The project aims to empower UOP with data-driven insights to facilitate better resource planning and provide students with an optimized academic experience.
Please let me know what you think about this project. Thank you for reading!
Happy Coding!!!