Data Analytics and Machine Learning

Lect: 3. IEOR students only; priority to MSBA students. Practical survey of Python tools for acquiring, cleaning, and analyzing data. Techniques for obtaining data from files, web scraping, and APIs (CSV, HTML, JSON, XML); performing core data-cleaning tasks; and using data analysis, machine learning, and visualization libraries (NumPy, Pandas, Matplotlib, Seaborn, TensorFlow/Keras). Introduces foundational machine learning and deep learning concepts, including backpropagation, gradient descent, and implementation of neural networks with TensorFlow/Keras. Covers text mining using word, sentence, and document embeddings. Includes a group project requiring students to collect, store, and analyze a dataset of their choice and build a predictive model.