Degree

Bachelor of Science (Computer Science)

Department

Department of Computer Science

School

School of Mathematics and Computer Science (SMCS)

Advisor

Dr. Tariq Mahmood, Professor and Program Coordinator MS(CS) and MS(DS) Programs

Keywords

Data Visualisation, Statistical Modelling, Business Intelligence, Automated Dashboard, Data Analysis

Abstract

In a world dominated by massive datasets, transforming intricate data into easy-to-understand visual representations is essential. The market, however, is dominated by BI tools that require a high skill cap to be effectively used to produce the required result. Moreover, there is a noticeable lack of tools that enable users new to data science, data analysis, business analysis, and business intelligence to create insightful analytical dashboards with minimal effort. ENVI intends to streamline this process by permitting users to create interactive dashboards with minimal effort. At ENVI’s core is a statistics-based algorithm that aims to implement automated BI. This will allow it to automatically create insightful visual representations for the given data set at the clicks of a few buttons, as part of a short workflow that is seamless and intuitive for even non-technically-inclined users. Atop this, the tool allows users to select from a range of data import methods, customize their charts and dashboards for any fine-tuning, as well as reflect any changes made to the source data in the relevant visualizations. ENVI caters to a variety of users, including business stakeholders seeking immediate solutions to a business problem, students embarking on their analysis and BI journey, educators requiring visual aids for academic work and individuals looking to make data driven decisions. This will enable users to utilize data driven decision making without a steep learning curve.

Tools and Technologies Used

Frontend:

  • Framework: Next.js (React-based)

  • Visualization Library: Recharts

  • Styling: Tailwind CSS

Backend:

  • Language: Python 3.11

  • Framework: Django (Django REST Framework for APIs)

  • Libraries: Pandas, NumPy, Scikit-learn, SciPy, StatsModels

  • Statistical Methods: Pearson correlation, T-test, ANOVA, Chi-square test

Database:

  • PostgreSQL v14

Development & Testing Tools:

  • Visual Studio Code (IDE)

  • Postman (for API testing)

  • Google Chrome (for frontend and UX testing)

Key Techniques:

  • Automated dataset ingestion and field type detection

  • Dynamic statistical profiling for field relevance

  • RESTful API integration between frontend and backend

  • Automated chart selection based on statistical significance

  • Real-time dashboard editing and PDF export functionality

Methodology

ENVI employs a layered, statistics-driven methodology to automate business intelligence and data visualization for users of all skill levels. Users begin by uploading their dataset (e.g., CSV file), which is ingested and dynamically mapped to a PostgreSQL table. The system automatically distinguishes between numerical and categorical fields to ensure accurate statistical processing.

Once data is ingested, ENVI’s backend applies a suite of statistical profiling methods, including correlation analysis, ANOVA, and chi-square tests, to identify the most significant relationships within the data. Based on these results, the system automatically generates the most relevant charts and visualizations, which are rendered on an interactive dashboard. Users can further customize these dashboards using a drag-and-drop interface and export their results as professional PDF reports.

The methodology ensures a seamless workflow:
Upload → Analyze (Statistical Profiling) → Auto-Generate Visualizations → Customize & Export, all with minimal technical overhead for the user.

Document Type

Restricted Access

Submission Type

BSCS Final Year Project

Download

Share

COinS