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Student Programming Test Scores & Placement – EDA

Student Programming Test Scores & Placement - EDA

This Power BI dashboard titled “Student Programming Test Scores & Placement – EDA” is a data visualization project focused on analyzing the test performance of students across various programming-related skills and correlating that with their placement status. Here’s a tutorial-style breakdown of its structure and insights:

🔍 Objective

To explore the relationship between students’ performance in different programming subjects (Excel, ML, Python, SQL, Tableau) and their final placement status (Placed or Not Placed).

Dataset: https://www.kaggle.com/datasets/samarsaeedkhan/scores

Get the Practice File ( Power BI ) File : Get Project Practice File

🧱 Dashboard Components Explained

1. Dropdown Filter: Student Placement Status

  • Purpose: Interactive slicer for filtering the entire dashboard based on placement (Yes or No).
  • Use: Select “Yes” to see data only for placed students, or “No” for non-placed.

2. Donut Chart: Passed vs Failed Student Count

  • Data: Shows the total number of students who were placed vs not placed.
  • Insight:
    • 116 students placed (58%)
    • 84 students not placed (42%)

3. Bar Charts for Subject-Wise Score Distribution

Each of these bar charts displays the distribution of marks binned into intervals (e.g., 0–10%, 10–20%, etc.), split by placement status.

📊 Excel Marks Distribution

  • Green: Placed
  • Blue: Not Placed
  • Insight: Higher bins (80–100%) are dominated by placed students.

📊 ML (Machine Learning) Marks Distribution

  • Insight: Balanced participation, but higher placement rates in 60–100% bins.

📊 Python Marks Distribution

  • Horizontal Bar Chart
  • Insight: Stronger Python scores correlate with placement.

📊 SQL Marks Distribution (2 Views)

  • One chart compares scores vs placement status.
  • The other compares SQL scores vs passed/failed categorization.
  • Insight: Higher SQL scores lead to better placement chances.

📊 Tableau Marks Distribution

  • Similar bin-wise distribution with a noticeable increase in placement in higher bins.

4. Right-Side KPIs (Card Visuals)

These cards summarize the average scores per subject across all students:

  • Excel: 47.50%
  • ML: 51.44%
  • Python: 51.41%
  • SQL: 49.59%
  • Tableau: 49.52%
  • Total Students Placed: 200

📌 Key Insights

  • Python and ML have the highest average scores.
  • Students scoring above 60% in most subjects are more likely to be placed.
  • Placement seems to correlate positively with higher proficiency across all tools.

🛠️ Power BI Techniques Used

  1. Slicer for interactivity.
  2. Donut Chart for categorical comparison.
  3. Bar Charts (Vertical & Horizontal) for distribution analysis.
  4. Card Visuals for KPIs and summary statistics.
  5. Color Coding (Green = Placed, Blue = Not Placed) for intuitive comparison.
  6. Bin Groups used for converting percentage scores into ranges.