racousin/data_science_practice_2024
Nov 2024 – Jan 2025
2 Months active
Languages Used
CSVExcelPython
Technical Skills
Data AnalysisData CleanupData PreprocessingData ScienceDeep LearningJupyter Notebook
SamyBraik
PROFILE
Samybraik
Worked on the racousin/data_science_practice_2024 repository to deliver core data science features focused on data ingestion, cleanup, and machine learning experimentation. Built modular Jupyter notebooks and Python scripts for loading, preprocessing, and analyzing multi-store sales data from CSV and Excel sources, improving data accessibility and hygiene by removing obsolete datasets. Developed an end-to-end MNIST digit classification pipeline using PyTorch, incorporating batch normalization, dropout, and advanced training strategies such as early stopping and learning rate scheduling. Established reproducible workflows for exploratory data analysis and data quality checks, leveraging Pandas, Matplotlib, and Seaborn to accelerate downstream modeling and onboarding.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
3
Lines of code
19,827
Activity Months2
Your Network
15 peopleShared Repositories
15
Work History
January 2025
1 Commits • 1 Features
Jan 1, 2025Month: 2025-01 | Repository: racousin/data_science_practice_2024. Focused monthly summary highlighting feature delivery, bug fixes, impact, and technical skills demonstrated for performance review.
1 Commits • 1 Features
Jan 1, 2025Month: 2025-01 | Repository: racousin/data_science_practice_2024. Focused monthly summary highlighting feature delivery, bug fixes, impact, and technical skills demonstrated for performance review.
January 2025
November 2024
3 Commits • 2 Features
Nov 1, 2024November 2024 performance summary for racousin/data_science_practice_2024. Focused on delivering core data assets and enabling ML experimentation with clean data pipelines. Key outcomes included: 1) Sales Data Assets Ingestion and Cleanup: added ingestion assets (CSV/Excel files) for multiple stores and a data analysis notebook for loading, preprocessing, and exploratory ML work; cleaned obsolete datasets and ZIPs to reduce clutter and improve data hygiene. 2) MNIST Digit Classification Model: implemented end-to-end classifier with data loading, preprocessing, model definition (sequential network with batch norm and dropout), training with Adam optimizer, and evaluation with early stopping and learning rate scheduling. 3) Foundation for scalable ML workflows: established modular notebooks and scripts to enable repeatable experiments and faster onboarding for ML tasks. Business value: improved data accessibility for multi-store analysis, accelerated experimentation, and reduced maintenance overhead by cleaning data assets and assets clutter.
November 2024
3 Commits • 2 Features
Nov 1, 2024November 2024 performance summary for racousin/data_science_practice_2024. Focused on delivering core data assets and enabling ML experimentation with clean data pipelines. Key outcomes included: 1) Sales Data Assets Ingestion and Cleanup: added ingestion assets (CSV/Excel files) for multiple stores and a data analysis notebook for loading, preprocessing, and exploratory ML work; cleaned obsolete datasets and ZIPs to reduce clutter and improve data hygiene. 2) MNIST Digit Classification Model: implemented end-to-end classifier with data loading, preprocessing, model definition (sequential network with batch norm and dropout), training with Adam optimizer, and evaluation with early stopping and learning rate scheduling. 3) Foundation for scalable ML workflows: established modular notebooks and scripts to enable repeatable experiments and faster onboarding for ML tasks. Business value: improved data accessibility for multi-store analysis, accelerated experimentation, and reduced maintenance overhead by cleaning data assets and assets clutter.
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance75.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVExcelPython
Technical Skills
Data AnalysisData CleanupData PreprocessingData ScienceData VisualizationDeep LearningJupyter NotebookMachine LearningMatplotlibPandasPyTorchPythonRepository ManagementRequestsScikit-learn
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