Course Modules:
Module 1: Introduction to Data Science & AI
What is data science?
The AI-powered data science workflow
Use cases across industries
Module 2: Data Collection, Cleaning, and Wrangling
Data types and sources (CSV, APIs, SQL, web scraping)
Handling missing values and outliers
Pandas and NumPy for preprocessing
Module 3: Exploratory Data Analysis (EDA)
Descriptive statistics and correlation analysis
Data visualization with Matplotlib and Seaborn
Feature engineering basics
Module 4: Introduction to Machine Learning
Supervised vs. unsupervised learning
Training, validation, testing
Model evaluation metrics (accuracy, F1, ROC)
Module 5: Predictive Modeling with AI
Regression and classification models
Decision trees, Random Forest, Gradient Boosting
Model tuning with cross-validation and grid search
Module 6: Deep Learning for Data Science
Introduction to neural networks
Using Keras and TensorFlow for structured data
Multilayer perceptrons and deep feature learning
Module 7: Natural Language and Text Analytics
Text preprocessing and vectorization
Sentiment analysis and topic modeling
NLP pipelines with scikit-learn and spaCy
Module 8: Time-Series and Forecasting
Understanding temporal data
ARIMA, Prophet, and LSTM models
Forecast evaluation and visualization
Module 9: Tools and Platforms for AI-Driven Analytics
Jupyter Notebook, Google Colab
BigQuery, Snowflake, and cloud-based pipelines
Using AutoML for faster model development
Module 10: Capstone Project
Choose a dataset and define a business or research question
Clean, explore, model, and present your findings
Submit dashboard, final notebook, and model report
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