Shayan ShamimCase studies
← Back
Evidence library

Systems, logic & outcomes.

The back action stays fixed while you scan every case.

CASE STUDY 01

Faculty Hour scheduling & analytics platform

A role-based university application that turns timetables, venue rules and stakeholder roles into a guided scheduling decision.

The problem

  • Manual coordination across faculty, students and administration.
  • Unclear attendance potential for a selected meeting time.
  • Venue and minimum-booking rules add friction.

What I built

  • SSO auto-authentication and role routing.
  • Dedicated Faculty, Student, Admin and RO portals.
  • Cross-campus slot and venue management.

Decision logic

  • Processes live student timetables.
  • Uses a 15-minute minimum availability window.
  • Calculates percentages and suggests better alternatives.
Application flow
University SSOAuthenticate once
Role routerRight portal
Live timetablesRead availability
Scheduling logicScore and suggest
Role portalsBook and manage
Discuss this application →
CASE STUDY 02

Institutional ETL & trusted reporting layer

Automated ingestion, quality checks and SQL architecture that make multi-source institutional data usable across departments.

Ingestion

  • Incremental extraction with SSIS and Python.
  • Data from the LMS and multiple applications.
  • Recurring automation reduces manual handling.

Quality & modeling

  • Stage and validate incoming records.
  • Normalize complex datasets.
  • Create views and stored procedures for reusable logic.

Delivery

  • Secure, ready-to-use departmental access.
  • Power BI reporting for planning and analysis.
  • A repeatable layer instead of one-off extracts.
Data flow
LMS & applicationsSources
SSIS / PythonIncremental extraction
Staging & qualityValidate
SQL ServerViews and procedures
Power BIReporting
Discuss an ETL project →
PUBLIC DEMO 03

SaaS revenue & cohort analytics

A Power BI proof of concept using simulated data to demonstrate KPI modeling, customer value and retention cohort analysis.

Questions

  • How is revenue changing?
  • Which segments contribute most?
  • Where does retention weaken?

Analytical layer

  • Revenue and user KPI model.
  • Dynamic subscription measures.
  • 12-month retention cohort matrix.

Experience

  • Executive summary before detail.
  • Controlled visual density.
  • Interactive exploration in one report.
Next step

Need this kind of work?

Return to the focused contact form or open the latest resume.

Send inquiryResume