iramler/slu_score_module_development
Feb 2026 – Apr 2026
3 Months active
Languages Used
MarkdownN/APythonQMDRHTML
Technical Skills
PythonR programmingdata analysisdata scrapingdata visualizationdocumentation
emiliaagostinelli
PROFILE
Emiliaagostinelli
Developed a suite of analytics modules for the iramler/slu_score_module_development repository, focusing on women’s alpine skiing data. Over three months, delivered features for statistical analysis, data scraping, and visualization using Python and R programming. Built tools to analyze race performance, including modules for paired data analysis, boxplot visualizations, and normal distribution modeling. Enhanced data quality and metadata governance across multiple datasets, improving reliability and onboarding for end users. Prioritized clear documentation and reproducibility by refining data provenance, updating links, and consolidating user-facing materials. The work emphasized robust data management, educational content development, and reproducible analytics for sports data analysis.
Overall Statistics
Feature vs Bugs
89%Features
Repository Contributions
17Total
Bugs
1
Commits
17
Features
8
Lines of code
6,783,071
Activity Months3
Your Network
22 peopleShared Repositories
22
phoebecharMember
AlixfritzMember
Brendan KaradenesMember
cwgartMember
Cooper OlneyMember
deeriMember
ferhin23Member
amsiko21Member
iramlerMember
Work History
April 2026
2 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for iramler/slu_score_module_development: Focused on documentation improvements for World Cup Giant Slalom data analysis, enhancing clarity, data provenance, and dataset accessibility to support analysts and ensure reproducible results.
2 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for iramler/slu_score_module_development: Focused on documentation improvements for World Cup Giant Slalom data analysis, enhancing clarity, data provenance, and dataset accessibility to support analysts and ensure reproducible results.
April 2026
March 2026
5 Commits • 3 Features
Mar 1, 2026March 2026 monthly summary for the iramler/slu_score_module_development repository. Focused on metadata governance, data quality, and documentation consolidation across three datasets to improve reliability, onboarding, and decision-support for end users. The work delivered end-to-end improvements in data metadata, input data quality, and user-facing documentation, with explicit connections to business value and product readiness.
March 2026
5 Commits • 3 Features
Mar 1, 2026March 2026 monthly summary for the iramler/slu_score_module_development repository. Focused on metadata governance, data quality, and documentation consolidation across three datasets to improve reliability, onboarding, and decision-support for end users. The work delivered end-to-end improvements in data metadata, input data quality, and user-facing documentation, with explicit connections to business value and product readiness.
February 2026
10 Commits • 4 Features
Feb 1, 2026February 2026 monthly summary for iramler/slu_score_module_development: Delivered a cohesive suite of analytics modules to enhance women's alpine skiing data analytics, established robust data preparation workflows, and completed targeted documentation/refactor to improve clarity and collaboration. Key features introduced include a Women’s Alpine Statistics Analysis Module (paired data analysis, data scraping, statistics, and visualizations) and a Boxplot Analysis Module for Race Results (start order vs final ranking, with data scraping and preparation for statistical analysis). Also launched a Normal Distribution Module for World Cup Giant Slalom (data scraping, run-time difference analysis, plus an accompanying worksheet) and a Data Preparation Module for Run Time Differences (Women’s Giant Slalom) focusing on data manipulation and statistical testing. Documentation and refactor updates included renaming Tremblant_Module.qmd to index.qmd and related organizational improvements. These deliverables collectively enable faster, reproducible insights and higher-quality analytics for race-performance evaluation.
10 Commits • 4 Features
Feb 1, 2026February 2026 monthly summary for iramler/slu_score_module_development: Delivered a cohesive suite of analytics modules to enhance women's alpine skiing data analytics, established robust data preparation workflows, and completed targeted documentation/refactor to improve clarity and collaboration. Key features introduced include a Women’s Alpine Statistics Analysis Module (paired data analysis, data scraping, statistics, and visualizations) and a Boxplot Analysis Module for Race Results (start order vs final ranking, with data scraping and preparation for statistical analysis). Also launched a Normal Distribution Module for World Cup Giant Slalom (data scraping, run-time difference analysis, plus an accompanying worksheet) and a Data Preparation Module for Run Time Differences (Women’s Giant Slalom) focusing on data manipulation and statistical testing. Documentation and refactor updates included renaming Tremblant_Module.qmd to index.qmd and related organizational improvements. These deliverables collectively enable faster, reproducible insights and higher-quality analytics for race-performance evaluation.
February 2026
Activity
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Quality Metrics
Correctness93.0%
Maintainability90.6%
Architecture90.6%
Performance90.6%
AI Usage25.8%
Skills & Technologies
Programming Languages
HTMLMarkdownN/APythonQMDR
Technical Skills
PythonR programmingdata analysisdata managementdata presentationdata processingdata scrapingdata visualizationdocumentationeducational content developmenteducational toolsfile managementstatistical analysisstatistical modelingweb scraping
Repositories Contributed To
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