📊 Current Technology Usage and Trends Dashboard

🧠 Project: Survey Data Analysis and Visualization

This project analyzes a developer survey dataset to uncover insights into respondent demographics, education levels, and the technologies they use or want to use. Python is used for data cleaning and transformation, and IBM Cognos Analytics is used to create an interactive dashboard.

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📥 Dataset

The dataset used in this project can be found here:
🔗 Survey Data CSV

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🧪 Data Preparation with Python

Before visualization, the dataset was cleaned and transformed using Python in a Jupyter Notebook:

• Missing Values: Removed nulls from critical fields (e.g., Country, FormalEducation, Age)
• Standardization: Cleaned and standardized inconsistent text entries
• Transformation:
  • Exploded multi-value fields (like LanguageHaveWorkedWith, DatabaseWantToWorkWith, etc.)
  • Grouped and aggregated data by country, age group, and education level
• Export: Saved the cleaned data as survey_data_cleaned.csv for use in IBM Cognos

📁 Key Files

• survey_data_cleaned.csv: The cleaned dataset
• developer_survey_analysis.ipynb: The full notebook with code and project description
• cognos_dashboard.pdf: The IBM Cognos Analytics dashboard report

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📊 IBM Cognos Dashboard Visualizations

Interactive visualizations were created using IBM Cognos Analytics:

📍 Respondent Distribution by Age

• Chart: Pie Chart
• Purpose: Visualize age group proportions
• Feature: Labeled segments with respondent counts and percentages

🌍 Respondent Count by Country

• Chart: Map Chart
• Purpose: Show geographic distribution
• Feature: Color-coded by count

🎓 Education Level Distribution

• Chart: Line Chart
• Purpose: Analyze education background of respondents
• Feature: Markers and value labels for clarity

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✅ Conclusion

This project demonstrates how to:

• Use Python to clean and reshape survey data
• Transform raw data into an analytics-ready format
• Build compelling and interactive visualizations using IBM Cognos

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🚀 How to Use

1. Clone the repository or download the files
2. Open the Jupyter Notebook (.ipynb) to view the data cleaning steps
3. Explore the dashboard via the PDF or recreate it in IBM Cognos using the cleaned CSV

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📄 License

This project is for educational purposes and is freely shareable under the MIT License.