Professional Program in Data Science and AI Applications

Core Concepts:
Data analysis fundamentals using Python libraries like Pandas and NumPy
Exploratory Data Analysis (EDA), data wrangling, visualization, and hypothesis testing techniques
Interpreting trends and patterns through effective visual storytelling

Tools:
Python, Pandas, NumPy, Matplotlib, Seaborn, Jupyter Notebook

Project:
Perform EDA on a public sales dataset. Clean and visualize the data using Pandas and Seaborn, test key business hypotheses, and derive actionable sales insights through a structured mini project.

Core Concepts:
Supervised and unsupervised machine learning techniques including regression, classification, and clustering
Evaluating model performance using metrics like RMSE, AUC-ROC, and F1-score
Combining SQL queries with Power BI for data extraction and visualization

Tools:
Python (scikit-learn), SQL, Power BI, Jupyter Notebook

Project:
Build a customer churn prediction model using classification techniques. Evaluate model accuracy using appropriate metrics, extract customer behavior data via SQL, and present insights through interactive Power BI dashboards.

Core Concepts:
Deep learning fundamentals including neural networks, CNNs, RNNs, and transformer-based models like BERT
Model version control and reproducibility using Git and DVC
Building and deploying AI applications using Flask and cloud platforms like AWS

Tools:
TensorFlow, PyTorch, BERT, Git, DVC, Flask, AWS (EC2/S3), Docker (optional)

Project:
Train a sentiment classifier using deep learning (e.g., BERT or RNN). Build a front-end interface with Flask and deploy the complete application on AWS. Ensure model versioning and reproducibility using Git and DVC.

Core Concepts:
Advanced AI architectures including Transformers and Generative Adversarial Networks (GANs)
Designing scalable AI pipelines and workflows for production
Understanding Responsible AI practices, including fairness, explainability, and privacy

Tools:
Hugging Face Transformers, GAN libraries (e.g., TensorFlow/Keras or PyTorch-based), MLflow, SHAP, Azure/AWS/GCP tools

Project:
Work on a capstone project to solve a real-world industry problem. Build a complete AI solution pipeline—data to deployment—using advanced models and tools, while integrating responsible AI principles. Present the solution with a demo and detailed documentation.