Data Science with AI Focus

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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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