Fatima Jinnah Women University · CS 2025

Degree vs. Skill:
Are FJWU Students
Truly Job-Ready?

A Data Analytics Perspective on Skills vs Academic Performance — Survey of 108 FJWU Students

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108
Survey Responses
5
Team Members
12
Variables Analyzed
4
Analysis Domains

Survey Insights

Key distributions and patterns discovered from the 108-response dataset collected from FJWU Computer Science students.

CGPA Distribution
Academic performance across surveyed students
Self-Rated Job Readiness
How ready do students feel for employment?
Technical Skills Proficiency
Self-reported skill levels across key CS domains
Industry Exposure
Internships, projects & professional experience
Academic Performance vs Practical Skills — Gap Analysis
Comparing CGPA-based academic scores against practical skill self-assessments by category

What the Data Reveals

Core insights from statistical analysis and NLP processing of 108 FJWU CS student responses.

📊
Academic-Skills Disconnect
Students with high CGPAs (3.5+) do not consistently show higher practical skills, suggesting academic grades alone are a weak predictor of job readiness.
💼
Low Industry Exposure
68% of respondents had never completed an internship. Students with even one internship rated their readiness 2.1× higher than peers without.
🛠️
Practical Skill Gaps
Programming and DSA skills are rated moderate-to-high, but communication, project management, and DevOps remain critically underdeveloped.
🤖
NLP Sentiment Analysis
Open-ended responses showed predominantly neutral-to-negative sentiment around job market confidence, citing "lack of real projects" as the top concern.
🎓
Certification Effect
Students with external certifications (Coursera, HackerRank, etc.) scored significantly higher on practical skill metrics, suggesting self-learning fills curriculum gaps.
Top Skill Demand Mismatch
Industry-demanded skills like cloud computing, APIs, and agile methodology are rarely covered in coursework yet appear in nearly every job listing reviewed.

Research Pipeline

A structured, reproducible data analytics workflow from survey design to actionable insights.

STEP 01
Survey Design
Google Form with 108 responses collected across FJWU CS cohort
STEP 02
Data Cleaning & EDA
Manual & automated wrangling; null handling, normalization, overall analysis (Insa)
STEP 03
EDA & Stats
Descriptive stats, distributions, correlation matrices, deep analysis (Eman)
STEP 04
NLP Analysis
Sentiment analysis & keyword extraction on open-ended responses (Zunaira)
STEP 05
Reporting
Research paper, charts & documentation (Maria)
STEP 06
Interface
Dashboard & interactive UI for non-technical users (Isra)

Project Members

Five Computer Science students from FJWU, Batch 2025 — each responsible for a distinct analytical domain.

IS
Insa Saleem
2025-BCS-043
Data Engineering
  • Manual Collection & Digitization
  • Data Merging & Integration
  • Data Cleaning & Wrangling
  • Overall Analysis
EF
Eman Fatima
2025-BCS-025
Statistical Analysis
  • Exploratory Data Analysis
  • Descriptive Statistics
  • Correlation & Regression
  • Deep Analysis
ZT
Zunaira Tariq
2025-BCS-095
NLP & Applied
  • NLP Sentiment Analysis
  • Keyword Extraction
  • Performance Metrics
  • Applied Analysis
MY
Maria Yasmeen
2025-BCS-059
Project Lead
  • Technical Writing
  • Research Paper Drafting
  • Report Generation
  • Presentation & Charts
IA
Isra Asif
2025-BCS-045
Interface Design
  • Presentation Design
  • Result Consolidation
  • Interactive Dashboard
  • React / PHP Interface