📊 Data Analytics and Artificial Intelligence Course

Welcome to the Data Analytics and Artificial Intelligence course repository! This course is designed to equip you with the essential skills to understand, analyze, and apply data analytics and AI techniques in real-world scenarios.

🌟 Course Overview

This course is a hands-on, project-driven exploration into the realms of data analytics and artificial intelligence. You will learn to:

• 📈 Analyze datasets and extract meaningful insights.
• 🤖 Understand the fundamentals of machine learning and AI.
• 🛠️ Utilize popular libraries and frameworks like NumPy, Pandas, Scikit-learn, and more.
• 🗂️ Work with various data sources such as SQL databases, CSV, JSON, APIs, and Web Scraping.
• 🧠 Develop AI models to solve practical problems.

🗂️ Repository Structure

The repository is organized into several sections corresponding to the course modules. Each folder contains materials, code examples, and exercises.

📁 datasets/            # Contains datasets used throughout the course
📁 notebooks/           # Jupyter Notebooks with step-by-step lessons
📁 projects/            # Capstone projects and AI applications
📁 resources/           # Additional resources, links, and cheat sheets
📁 assignments/         # Weekly assignments and project descriptions

🧑‍🏫 Course Curriculum

(This serves as just the outline, and actual inclass content might differ later in the repository)

1. Course 1: Introduction to Data Analytics, and Python Programming

   • Overview of Data Analytics
   • Introduction to Python Programming
   • Setting up GitHub for collaboration

2. Course 2: Data Acquisition and Cleaning

   • Sources of Data (APIs, Databases, Web Scraping)
   • Data Cleaning Techniques
   • Handling Missing Data and Data Imputation

3. Course 3: Exploratory Data Analysis (EDA) and Power BI

   • Data Visualization Tools and Techniques
   • Exploratory Data Analysis (EDA) with Python
   • Introduction to Power BI for Business Intelligence and Dashboarding
   • Advanced Power BI: DAX for Data Analysis

4. Course 4: Advanced SQL and Data Warehousing

   • Advanced SQL Queries and Optimization
   • Data Warehousing Concepts
   • ETL Process (Extract, Transform, Load)

5. Course 5: Introduction to Machine Learning

   • Supervised and Unsupervised Learning
   • Common Machine Learning Algorithms
   • Hands-on with Scikit-learn

6. Course 6: Feature Engineering and Model Evaluation

   • Feature Selection and Extraction
   • Model Evaluation Metrics (Confusion Matrix, ROC-AUC)
   • Cross-Validation and Hyperparameter Tuning

7. Course 7: Deep Learning Fundamentals

   • Introduction to Neural Networks
   • Basics of Deep Learning Models (CNNs, RNNs)
   • Hands-on with TensorFlow and Keras

8. Course 8: Natural Language Processing (NLP) and Large Language Models (LLMs)

   • Text Preprocessing and Feature Extraction
   • Building NLP Models
   • Introduction to Large Language Models (LLMs)

9. Course 9: Time Series Analysis

   • Time Series Data and Forecasting Techniques
   • ARIMA, SARIMA, and Exponential Smoothing
   • Hands-on with Time Series in Python

10. Course 10: Big Data, Cloud Computing, and Data Compliance

    • Introduction to Big Data Tools (Hadoop, Spark)
    • Cloud Platforms (AWS, Azure, Google Cloud)
    • Data Privacy and Compliance (GDPR, CCPA)

11. Course 11: AI, Model Deployment, and Business Analysis

    • Deploying Machine Learning Models to Production
    • Business Analysis Techniques and Tools
    • Ethical Implications of AI in Business

Capstone Projects

• End-to-end project applying concepts from the entire course to solve real-world problems

🛠️ Tools and Technologies

Throughout the course, we’ll be working with:

• Python for data analysis and AI development
• Jupyter Notebooks for hands-on coding and experimentation
• NumPy, Pandas, Matplotlib for data manipulation and visualization
• Scikit-learn, TensorFlow for building AI models
• MySQL and APIs for real-world data acquisition

📝 How to Use this Repository

1. Clone the repository:

   git clone https://github.com/your-username/data-analytics-ai-course.git

🚀 Getting Started

Make sure you have Python installed along with the necessary packages. You can install all dependencies via requirements.txt:

pip install -r requirements.txt

📚 Resources and References

• Python Documentation
• Pandas Documentation
• Scikit-learn Documentation

✨ Contact and Support

If you encounter any issues or have questions, feel free to open an issue or reach out to me:

• Email: Mail@: [email protected]
• GitHub: Mohammad Idrees Bhat