📊 Analytics and Visualization Systems

📌 Project Overview

This repository contains multiple analytics and visualization systems covering:

SQL analytics
data cleaning pipelines
REST APIs
interactive dashboards
Flask web clients
Plotly visualizations

The workspace demonstrates practical understanding of:

backend analytics workflows
visualization systems
modular application design
SQL data processing
lightweight analytics architecture

Each project is designed as an independent analytical system with its own workflow and execution environment.

🎯 What This Repository Demonstrates

This workspace demonstrates multiple real-world analytics engineering concepts:

Area	Demonstrated Skills
SQL Analytics	Query design, aggregation, filtering
Dashboards	Interactive visualization systems
APIs	Flask-based backend architecture
Web Clients	API consumption workflows
Visualization	Plotly analytics rendering
Data Pipelines	Cleaning and transformation workflows
Project Architecture	Modular multi-project organization

🏗️ Workspace Architecture

Analytics Systems Workspace
        ↓
Independent Analytical Projects
        ↓
SQL / APIs / Dashboards / Visualization
        ↓
Interactive Exploration & Analysis

📂 Repository Structure

analytics-and-visualization-systems/
│
├── DASH_DATA_INSIGHT_APP/
├── FOOD_DELIVERY_SQL_ANALYSIS/
├── INTERACTIVE_DATA_VISUALIZATION_PLOTLY/
├── IPL_API_FLASK/
├── IPL_ANALYTICS_WEB_CLIENT/
├── MYSQL_LAPTOP_DATA_PIPELINE/
└── README.md

📊 Included Projects

Project	Type	Main Technologies	Purpose
`DASH_DATA_INSIGHT_APP`	Dashboard	Dash, Plotly, Pandas	Interactive analytics dashboard
`IPL_API_FLASK`	REST API	Flask, Pandas, NumPy	Cricket analytics API
`IPL_ANALYTICS_WEB_CLIENT`	Web Client	Flask, Jinja2, Requests	API-powered frontend client
`INTERACTIVE_DATA_VISUALIZATION_PLOTLY`	Notebook	Plotly, Pandas	Visualization experiments
`MYSQL_LAPTOP_DATA_PIPELINE`	SQL Pipeline	MySQL	Data cleaning & transformation
`FOOD_DELIVERY_SQL_ANALYSIS`	SQL Analytics	MySQL	Food delivery business analysis

📈 Project Breakdown

📊 DASH_DATA_INSIGHT_APP

Overview

An interactive analytics dashboard built using:

Dash
Plotly
Pandas

The application enables:

interactive filtering
chart rendering
data exploration
business insight visualization

What It Demonstrates

dashboard engineering
Plotly integration
user-driven analytics
frontend analytical workflows

🏏 IPL_API_FLASK

Overview

A lightweight REST API system built using:

Flask
Pandas
NumPy

The API exposes cricket analytics data and supports backend analytical workflows.

What It Demonstrates

API architecture
backend analytics systems
Flask routing
JSON response handling

🌐 IPL_ANALYTICS_WEB_CLIENT

Overview

A Flask-based frontend client that consumes the IPL Analytics API.

Built using:

Flask
Jinja2
Requests

The client renders analytical data from the backend API into browser-based visualizations and pages.

What It Demonstrates

API consumption workflows
frontend-backend integration
web client architecture
templating systems

📉 INTERACTIVE_DATA_VISUALIZATION_PLOTLY

Overview

A Plotly experimentation notebook focused on:

chart design
interactive visualization
analytical storytelling

Built using:

Plotly
Pandas
Jupyter Notebook

What It Demonstrates

interactive visualization
analytical rendering
chart experimentation

🗄️ MYSQL_LAPTOP_DATA_PIPELINE

Overview

A SQL-based data cleaning and transformation pipeline using MySQL.

Includes:

cleaning workflows
transformation scripts
SQL preprocessing logic

What It Demonstrates

SQL pipeline engineering
data preprocessing
transformation workflows
structured SQL scripting

🍔 FOOD_DELIVERY_SQL_ANALYSIS

Overview

A SQL analytics project focused on food delivery business analysis.

Includes:

SQL aggregations
filtering
business metrics
customer/order analysis

What It Demonstrates

business analytics workflows
SQL query engineering
operational analysis
analytical SQL design

⚙️ Architecture Principles

The workspace follows several engineering principles:

separation of UI and logic layers
modular project organization
reproducible SQL workflows
configuration-driven systems
independent project execution
portable analytical architecture

🧠 System Design Philosophy

Each project is intentionally structured as an independent analytical system.

This allows:

isolated experimentation
easier deployment
modular scaling
independent dependency management

The repository simulates a lightweight analytics engineering workspace rather than a single monolithic application.

🛠️ Tech Stack

Category	Technologies
Programming	Python
Visualization	Plotly, Dash
Backend	Flask
Database	MySQL
Data Processing	Pandas, NumPy
Frontend Rendering	Jinja2
Notebook Analytics	Jupyter

⚙️ Quick Start

📊 Dash Dashboard

cd DASH_DATA_INSIGHT_APP

python -m venv .venv

.venv\Scripts\activate

pip install -r requirements.txt

python dashboard.py

Open:

http://127.0.0.1:8050/

🌐 IPL Analytics Web Client

Step 1 — Start API

cd IPL_API_FLASK

python -m venv .venv

.venv\Scripts\activate

pip install -r requirements.txt

python app.py

Step 2 — Start Client

cd IPL_ANALYTICS_WEB_CLIENT

python -m venv .venv

.venv\Scripts\activate

pip install -r requirements.txt

python app.py

Open:

http://127.0.0.1:7000/

📉 Plotly Notebook

cd INTERACTIVE_DATA_VISUALIZATION_PLOTLY

python -m venv .venv

.venv\Scripts\activate

pip install -r requirements.txt

jupyter notebook

Open:

plotly_practice.ipynb

🗄️ SQL Projects

Use:

MySQL Workbench
MySQL CLI

Run SQL scripts in the documented execution order.

📂 Data & Assets

Included Assets

CSV datasets
dashboard screenshots
SQL schema references
analytical notebooks

Asset Locations

Asset	Location
Dashboard Screenshots	`DASH_DATA_INSIGHT_APP/assets`
Food Delivery Schema	`FOOD_DELIVERY_SQL_ANALYSIS/zomato-schema.xlsx`
CSV Datasets	Inside respective project folders

☁️ Cloud & Deployment Direction

The projects are structured to support future cloud deployment workflows.

Potential deployment paths:

Component	Potential Cloud Service
Dash / Flask Apps	ECS / App Runner / Elastic Beanstalk
CSV Storage	AWS S3
SQL Databases	AWS RDS MySQL
APIs	FastAPI + Docker
ML Services	SageMaker / ECS

📊 Engineering Highlights

Multi-project analytical workspace
SQL analytics workflows
Flask backend systems
Interactive dashboards
API-driven architectures
Data transformation pipelines
Modular project organization
Plotly visualization systems
Business analytics workflows

📈 Potential Future Improvements

Planned enhancements include:

Docker containerization
AWS deployment workflows
centralized configuration management
API authentication
CI/CD integration
database-backed storage
caching layers
FastAPI migration
ML inference integration
real-time analytics pipelines

🎯 What This Repository Demonstrates

This repository demonstrates practical understanding of:

analytics engineering
SQL workflows
backend API systems
dashboard development
frontend-backend integration
data visualization architecture
modular project organization
lightweight analytics infrastructure

📌 Strategic Engineering Value

This repository demonstrates more engineering depth than isolated notebook projects because it includes:

multiple analytical systems
modular architectures
backend APIs
SQL pipelines
interactive dashboards
frontend-backend workflows
reproducible analytical structures

📸 Recommended Screenshot Section

Add screenshots for stronger recruiter impact:

![Dash Dashboard](your-image-link)
![IPL Web Client](your-image-link)
![SQL Analysis](your-image-link)

👨‍💻 Author

Rudra Tyagi

Focus Areas

ML Systems
MLOps
AI Infrastructure
Analytics Engineering
Backend Analytical Systems

⭐ Recruiter Notes

This repository demonstrates:

modular analytics engineering
SQL analytical workflows
dashboard architecture
Flask backend systems
frontend-backend integration
business intelligence engineering

📜 License

This repository is intended for educational, research, and portfolio purposes.

⭐ Support

If you found this repository useful, consider giving it a ⭐ on GitHub.

Name		Name	Last commit message	Last commit date
DASH_DATA_INSIGHT_APP		DASH_DATA_INSIGHT_APP
FOOD_DELIVERY_SQL_ANALYSIS		FOOD_DELIVERY_SQL_ANALYSIS
INTERACTIVE_DATA_VISUALIZATION_PLOTLY		INTERACTIVE_DATA_VISUALIZATION_PLOTLY
IPL_ANALYTICS_WEB_CLIENT		IPL_ANALYTICS_WEB_CLIENT
IPL_API_FLASK		IPL_API_FLASK
MYSQL_LAPTOP_DATA_PIPELINE		MYSQL_LAPTOP_DATA_PIPELINE
.gitignore		.gitignore
README.md		README.md

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📊 Analytics and Visualization Systems

📌 Project Overview

🎯 What This Repository Demonstrates

🏗️ Workspace Architecture

📂 Repository Structure

📊 Included Projects

📈 Project Breakdown

📊 DASH_DATA_INSIGHT_APP

Overview

What It Demonstrates

🏏 IPL_API_FLASK

Overview

What It Demonstrates

🌐 IPL_ANALYTICS_WEB_CLIENT

Overview

What It Demonstrates

📉 INTERACTIVE_DATA_VISUALIZATION_PLOTLY

Overview

What It Demonstrates

🗄️ MYSQL_LAPTOP_DATA_PIPELINE

Overview

What It Demonstrates

🍔 FOOD_DELIVERY_SQL_ANALYSIS

Overview

What It Demonstrates

⚙️ Architecture Principles

🧠 System Design Philosophy

🛠️ Tech Stack

⚙️ Quick Start

📊 Dash Dashboard

🌐 IPL Analytics Web Client

Step 1 — Start API

Step 2 — Start Client

📉 Plotly Notebook

🗄️ SQL Projects

📂 Data & Assets

Included Assets

Asset Locations

☁️ Cloud & Deployment Direction

📊 Engineering Highlights

📈 Potential Future Improvements

🎯 What This Repository Demonstrates

📌 Strategic Engineering Value

📸 Recommended Screenshot Section

👨‍💻 Author

Rudra Tyagi

Focus Areas

⭐ Recruiter Notes

📜 License

⭐ Support

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