The IBM SkillsBuild Data Analytics program is a free, self-paced learning path aimed at equipping registered learners with the fundamental technical and analytical skills to begin a career in data analytics. It spans 60+ hours of curriculum covering a range of topics including data analysis, data manipulation and cleaning, basic statistics, and data visualization.

1. IBM SkillsBuild Decoding Data PBL Program 2025
Final Project Presentation
Project Name: Student Performance Analysis
Team Name: DecodeX
College Name: College of Technology And Engineering, Udaipur

2. Team Members
Team Leader:
Name: Arriyaan Ali Syed
College: C.T.A.E., Udaipur
Team Member-1:
Name: Harsh Nagori
College: C.T.A.E., Udaipur
Team Member-2:
Name: Naman Yadav
College: C.T.A.E., Udaipur
Team Member-3:
Name: Prem Saini
College: C.T.A.E., Udaipur

3. Project Overview: Student Performance Analysis
Objective:
Analyze student academic performance across subjects (Math, Reading,
Writing) to identify trends based on gender, parental education, lunch type,
and test preparation.
Key Tasks:
 Cleaned and prepared the dataset
 Calculated average, highest, and lowest scores
 Created bar/column charts for subject and gender comparisons
 Added slicers for interactive filtering (class, gender, etc.)
Tools & Data:
Power BI | Dataset from Kaggle
Outcome:
An interactive Power BI dashboard visualizing performance by subject, class,
gender, and other key demographics.

4. Problem Statement
There is limited visibility into how various socio-demographic factors—such as gender, parental
education, lunch type, and test preparation—impact student academic performance. This lack of
insight can hinder targeted educational improvements and policy decisions.
Significance of the Problem
 Understanding performance patterns helps educators and policymakers:
 Identify achievement gaps
 Provide focused support to underperforming groups
 Promote data-driven educational reforms
 Enhance student outcomes and equality in learning opportunities
Relevant Sustainable Development Goals (SDGs)
 🎯SDG 4: Quality Education
Ensure inclusive and equitable quality education and promote lifelong learning opportunities for
all.
 🎯SDG 5: Gender Equality
Achieve gender equality and empower all women and girls by identifying and addressing
gender-based disparities in education.

5. Sources of Data
 Publicly available dataset from Kaggle.
 Provided by SPSScientist, based on simulated yet realistic student exam data.
Data Description
 Total Records: 1,000 students.
 Features:
1. Gender.
2. Race/ethnicity.
3. Parental level of education.
4. Lunch type (standard/free).
5. Test preparation course (completed/not completed).
6. Scores in Math, Reading, and Writing (0–100).
Data Collection Methods
 Dataset is synthetically generated for research/educational use.
 Designed to simulate real-world student performance and demographic patterns.
 No personally identifiable data; fully anonymized and open-source.

6. Data Cleaning Methods
 Checked for duplicate records and removed any redundancies
 Standardized categorical values (e.g., consistent capitalization)
 Renamed columns for clarity and consistency (e.g., "parental level of education" →
"Parental_Education")
Handling Missing Values
 Verified dataset completeness (no missing values in original dataset)
 In case of missing values in future/extended datasets:
1. Numerical Data: Impute using mean or median
2. Categorical Data: Fill with mode or use "Unknown" category
Data Transformation Techniques
 Converted categorical variables (e.g., gender, lunch type) into numerical codes for analysis
 Created derived fields (e.g., total score, average score)
 Binned score ranges for easier visual grouping (e.g., 0–50: Low, 51–75: Medium, 76–100: High)

7. Analytical Tools and Methods Used
 Power BI for data visualization and dashboard creation
 Descriptive statistics to calculate average, highest, and lowest scores
 Bar and column charts for comparing subject-wise and gender-based performance
 Slicers for interactive filtering by gender, class, lunch type, etc.
Key Findings
 Students who completed test preparation scored significantly higher
 Male students performed better in Math, while females excelled in Reading and
Writing
 Higher parental education levels correlated with better student performance
 Students with standard lunch outperformed those with free/reduced lunch
Insights Derived
 Academic outcomes are strongly influenced by socio-demographic factors
 Targeted support (like test prep) can bridge performance gaps
 Gender-specific strengths suggest areas for tailored teaching strategies
 School lunch programs may indicate broader socioeconomic impact on education

8. Formulated Hypothesis
 Students who complete a test preparation course achieve higher average scores
across all subjects compared to those who do not.
Rationale Behind the Hypothesis
 Test preparation provides structured revision and exam strategies
 Students gain confidence and familiarity with exam formats
 Prior studies and observed trends suggest a positive impact on performance
Method for Testing the Hypothesis
 Grouped students based on test preparation status (completed vs. none)
 Calculated and compared average Math, Reading, and Writing scores for both groups
 Used visualizations (bar charts) and summary statistics in Power BI to evaluate
differences
 Considered additional factors like gender and parental education to ensure robustness

9. Proposed Solution
 Develop an interactive Power BI dashboard that visualizes student performance trends by
subject, gender, and socio-demographic factors. This will support data-driven decisions in
education policy and classroom strategies.
Implementation Plan
 Data Preparation: Clean and structure the dataset
 Analysis: Calculate key metrics (average, max, min scores)
 Dashboard Creation: Build visuals (bar/column charts, filters) in Power BI
 Deployment: Share dashboard with educators and decision-makers for actionable insights
Alignment with SDGs
 🎯 SDG 4 – Quality Education:
Promotes inclusive, data-informed learning interventions
 🎯 SDG 5 – Gender Equality:
Highlights gender-based academic patterns to address disparities
 🎯 SDG 10 – Reduced Inequalities:
Supports equitable education by identifying impacts of socio-economic factors

10. Prominent Features of the Platform (Power BI Dashboard)
1. Interactive Visualizations: Bar and column charts for subject, gender, and class performance
2. Slicers/Filters: Dynamic filtering by gender, parental education, lunch type, and test preparation
3. Score Breakdown: Average, highest, and lowest scores by subject
4. Comparative Insights: Side-by-side comparisons of different demographic groups
5. User-Friendly Interface: Clean layout for easy navigation and interpretation
Site Map / Dashboard Layout
6. Home Page / Overview
 Summary of dataset and performance metrics
7. Subject Performance Page
 Visuals for Math, Reading, Writing scores
8. Demographic Analysis Page
 Filters and charts by gender, lunch type, parental education
9. Test Preparation Impact Page
 Comparison between students with/without prep
10. Insights & Recommendations Page
 Key takeaways and suggested interventions

11. Screenshots of the Dashboard

12. Impact of the Proposed Solution
 Enables data-driven decision-making in education
 Identifies underperforming student groups for targeted support
 Helps schools/policymakers allocate resources more effectively
 Promotes equity and transparency in academic performance tracking
Future Work
 Integrate real-time data from school systems for ongoing monitoring
 Include predictive analytics to forecast student performance
 Expand to analyze additional factors like attendance, teacher experience, or
curriculum
 Build custom dashboards for schools or districts based on their specific needs

13. Tools and Software Used
 Power BI: Data visualization and dashboard development
 Microsoft Excel / Google Sheets: Initial data cleaning and exploration
 Python (optional): For advanced preprocessing or analysis (if applicable)
 Kaggle: Source of dataset and peer insights
 Canva / PowerPoint: For presentation design and reporting
Additional References
 Kaggle Dataset: Student Performance in Exams
 SDG Goals: United Nations – Sustainable Development Goals
 Power BI Documentation: Power BI Learn
 Educational Research Articles (optional):
 Studies on test preparation impact and academic performance
 Reports on gender and socio-economic disparities in education

14. Thank You