Case Study

CareerLens: labor market intelligence for students choosing what to learn next.

CareerLens helps students compare roles, identify demanded skills and certifications, and turn market signals into a practical application packet.

Live App GitHub Back to Portfolio

Problem

Students do not always know what employers are actually asking for.

Internship postings are full of repeated skills, tools, certifications, and experience patterns. Without a system, students guess what to learn and struggle to connect their resume to market demand.

Users

Students comparing career paths and internship roles.

The target user is a student deciding between roles like data analyst, AI/data trainer, business analyst, cloud support, or entry-level software roles.

Product

A dashboard that turns job postings into decisions.

CareerLens organizes role demand, certification signals, salary context, resume gaps, priority insights, decision briefs, and application assets into a readable dashboard.

Technology

Data workflow with a browser-ready interface.

The project uses JavaScript, HTML/CSS, Python, SQL planning, CSV-style data pipelines, and dashboard logic to show how information systems can support career decisions.

Architecture

How CareerLens processes career signals.

Role Data Job-posting patterns, skill keywords, certifications, and role categories

Analysis Layer Skill demand counts, certification demand, resume gaps, priority scoring, and benchmark context

Dashboard Role comparison, decision brief, learning roadmap, portfolio evidence, and salary context

Student Action What to learn next, what to add to a resume, what to post, and what project proof to build

What I Built

Core features

Role-based skill analysis for student-facing technology paths.
Certification demand tracking for cloud, analytics, and AI-related credentials.
Resume gap logic that connects market demand to portfolio evidence.
Priority insights, decision briefs, learning roadmaps, and application packet outputs.

What I Learned

Data product thinking

How to turn broad career advice into structured, comparable data.
How dashboards can guide decisions when they focus on the next action.
How SQL-style schemas and CSV pipelines support scalable analysis.
How to move from analysis into assets a student can use while applying.

Roadmap

Where CareerLens goes next.

More role categories Larger posting dataset Resume upload workflow Power BI export Regional salary filters LLM-assisted summaries