CS420

CS420 – Data Science

Data Science is a multidisciplinary field that combines programming, statistical analysis, and domain expertise to extract meaningful insights from structured and unstructured data. This course begins with the foundations of data collection and preprocessing, teaching students how to clean, normalize, and transform raw datasets using tools like Pandas, NumPy, and OpenRefine. Students are introduced to exploratory data analysis (EDA) through visualization libraries such as Matplotlib, Seaborn, and Plotly, which help identify trends, patterns, and anomalies. The course progresses into machine learning, where students learn the difference between supervised and unsupervised learning. Algorithms like linear regression, decision trees, K-nearest neighbors, K-means clustering, and Naïve Bayes are explored, with emphasis on model training, testing, and validation. Students also work with scikit-learn and are briefly introduced to deep learning using frameworks such as TensorFlow or PyTorch. Other key components include Big Data processing using Spark, cloud-based data handling (Google Colab, AWS S3), and database querying (SQL/NoSQL). The course culminates in a capstone project where students build a complete data pipeline — from ingestion to prediction — showcasing real-world applications in finance, health, business, or social media.