thezachdrake/UMD-INST627-Fall2024
Dec 2024 – Dec 2024
1 Month active
Languages Used
PythonSQL
Technical Skills
Data AnalysisJupyter NotebookJupyter NotebooksPandasProblem DefinitionSQL
Jalil
PROFILE
Jalil
Jalil Goosby developed an NBA Team Investment Analytics Notebook for the thezachdrake/UMD-INST627-Fall2024 repository, focusing on aligning business objectives with actionable analytics. He designed a reproducible workflow in Jupyter Notebooks using Python, pandas, and sqlite3 to compute and display descriptive statistics such as scoring averages, performance streaks, playoff appearances, year-over-year improvement, and defensive effectiveness for NBA teams. By consolidating problem definition and analytics questions into a single, shareable notebook, Jalil established a reusable analytics template that supports future investment analyses. His work emphasized data storytelling, reproducibility, and efficient, data-driven decision-making for investment scenarios.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
400
Activity Months1
Your Network
6 people
Work History
December 2024
2 Commits • 1 Features
Dec 1, 2024December 2024 monthly work summary for thezachdrake/UMD-INST627-Fall2024. Delivered an NBA Team Investment Analytics Notebook that aligns business objectives with five analytics questions and provides a reproducible workflow for computing descriptive statistics of NBA team performance (scoring averages, streaks of consecutive below-average performances, playoff appearances, year-over-year improvement, and defensive effectiveness) using pandas and sqlite3. The notebook is designed for easy sharing and extension, supporting faster, data-driven investment decisions.
2 Commits • 1 Features
Dec 1, 2024December 2024 monthly work summary for thezachdrake/UMD-INST627-Fall2024. Delivered an NBA Team Investment Analytics Notebook that aligns business objectives with five analytics questions and provides a reproducible workflow for computing descriptive statistics of NBA team performance (scoring averages, streaks of consecutive below-average performances, playoff appearances, year-over-year improvement, and defensive effectiveness) using pandas and sqlite3. The notebook is designed for easy sharing and extension, supporting faster, data-driven investment decisions.
December 2024
Activity
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Quality Metrics
Correctness85.0%
Maintainability80.0%
Architecture70.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonSQL
Technical Skills
Data AnalysisJupyter NotebookJupyter NotebooksPandasProblem DefinitionSQL
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