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Screenshots

Python Version

This project delivers two console applications for Weyland-Yutani’s crisis response analytics—a C# and a Python version. Both connect to the SQL server instance hosting data, which enables field agents to quickly access reports on employees, facilities, incidents, and mission success.
The design emphasizes portability (two languages—Python for cross-platform and C# for Windows), CSV export for data sharing, and alignment of query outputs across platforms to ensure consistent situation awareness.

Both terminals connect to the WeylandYutani database on Microsoft SQL Server via port 1433 (the default port)
C# Uses Microsoft.Data.SqlClient with the connection string Server=127.0.0.1,1433;Database=WeylandYutani;User Id=SA;Password=YourStrong!Password;TrustServerCertificate=true;
Python uses SQLAlchemy (the setup code was originally using pyodbc, but it had a few issues with pandas DataFrames) with the connection string `mssql+pyodbc://SA:YourStrong!Password@127.0.0.1,1433/WeylandYutani?driver=ODBC+Driver+18+for+SQL+Server&TrustServerCertificate=yes

C# application implements interactive login via the Authenticate() method, which allows 3 attempts and credentials are hardcoded as admin/weyland2179
Python application connects directly via the database credentials; no interactive authentication prompt implemented
In production, both should use env variables or a more secure method of storing credentials, as well as a read-only DB role for the analytics application

Aggregates employee count and average security clearance by department using SQL JOIN on EMPLOYEES and DEPARTMENTS
Results sorted by average security clearance (descending) to surface highest-clearance departments first

Counts facilities by operational status and calculates the average security level per status category
Helps identify under-secured or offline facilities during emergencies

Groups incidents by year/month using DATEPART and categorizes by severity
Reveals monthly patterns in incident frequency and severity for operational forecasting

Calculates mission success rate via completed / total * 100
Sorts results by success rate and number of assignments to highlight critical mission patterns

Both implementations export last viewed dataset to a timestamped CSV file (e.g., weyland_data_export_20250812_145233.csv) for offline review or sharing

The Python implementation threw deprecation warnings related to pandas DataFrame handling when using pd.read_sql() with pyodbc—so I ended up switching to SQLAlchemy, which is a layer above pyodbc and worked better
Certain SQL servers column types were read into pandas with unexpected Python types (such as int64 vs float64)—this required a little bit of conversion for a clean output
There were slight differences in the SQL server driver behavior between the .NET SQLClient and Python’s pyodbc, which caused a few minor formatting inconsistencies in date/time columns

Field teams can quickly pull operation summaries without relying on heavier systems
Reports highlight high-clearance departments, offline facilities, and mission success trends for targeted intervention
CSV export allows data to be sent securely or imported into other tools during emergencies

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