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Python | SQL | Machine Learning | Microsoft Power BI | Microsoft Azure

A structured hands-on learning repository covering Data Analysis with Python, SQL for Data Science, Machine Learning fundamentals, Power BI, and Microsoft Azure.

This repository contains practical notebooks and exercises designed to build strong foundational and applied data skills.

🎓 Certifications

• Databases and SQL for Data Science with Python – Coursera
  📜 View Certificate

• Machine Learning Specialization – Coursera
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• Python for Data Science – Coursera
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• IBM Data Science Professional Certificate – Coursera
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• Machine Learning with Python – Coursera
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• Data Analysis and Visualization with Power BI – Coursera
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• Microsoft Azure Data Scientist Associate (DP-100) Professional Certificate – Coursera
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• Microsoft Power BI Data Analyst Professional Certificate – Coursera
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• Microsoft Azure Data Scientist Associate (DP-100) Exam Prep Professional Certificate – Coursera
  📜 View Certificate

• Microsoft Azure Machine Learning for Data Scientists – Coursera
  📜 View Certificate

• Perform data science with Azure Databricks – Coursera
  📜 View Certificate

• Build and Operate Machine Learning Solutions with Azure – Coursera
  📜 View Certificate

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📌 Overview

This repository is organized as a practice and learning workspace for:

• Data loading and preprocessing
• Exploratory Data Analysis (EDA)
• Data cleaning techniques
• Statistical summaries
• Introductory machine learning concepts
• SQL querying
• Cloud-based data workflows (Azure)
• Data visualization (Power BI)

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📂 Repository Structure

Python_SQL_Machine_Learning_Microsoft_PowerBI_Microsoft_Azure/
│
├── Data Analysis with Python/
│   ├── examples/
│   └── practice notebooks
│
├── SQL examples/
│
├── Machine Learning examples/
│
├── Azure practice/
│
└── README.md

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🐍 Data Analysis with Python

Topics covered:

• Importing CSV datasets from URLs
• Assigning and modifying column headers
• Handling missing values (? → NaN)
• Dropping invalid records
• Inspecting data types (.dtypes)
• Generating statistical summaries (.describe())
• Reviewing dataset structure (.info())

Example datasets used:

• Automobile dataset
• Laptop pricing dataset

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🧠 Machine Learning (Foundations)

Planned and ongoing topics:

• Linear Regression
• Train/Test Split
• Model Evaluation
• Feature Engineering basics
• Data preprocessing pipelines

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🗄 SQL for Data Science

Examples include:

• SELECT queries
• Filtering (WHERE)
• Aggregation (GROUP BY)
• JOIN operations
• Working with structured datasets

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☁ Microsoft Azure & Power BI

Hands-on exploration of:

• Azure Machine Learning
• Azure Databricks
• Cloud-based data workflows
• Power BI dashboards and reporting basics

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🛠 Technologies Used

• Python 3.x
• Pandas
• NumPy
• Jupyter Notebook
• SQL
• Microsoft Azure
• Power BI
• Git & GitHub

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🚀 Getting Started

Clone the repository

git clone https://github.com/<your-username>/Python_SQL_Machine_Learning_Microsoft_PowerBI_Microsoft_Azure.git
cd Python_SQL_Machine_Learning_Microsoft_PowerBI_Microsoft_Azure

Install required packages

pip install pandas numpy matplotlib scikit-learn

Run notebooks

You can open the notebooks using:

• VS Code
• Jupyter Notebook
• GitHub Codespaces

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🎯 Purpose

This repository serves as:

• A structured learning environment
• A practical data science lab
• A portfolio demonstrating technical skills
• A foundation for advanced ML and cloud projects

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📌 Future Improvements

• Add data visualization examples
• Add full end-to-end ML projects
• Add Azure ML deployment workflows
• Add CI/CD examples for data projects

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