Course Description
Focuses on using advanced data engineering and machine learning techniques like APIs, ETL, and Deep Learning with an emphasis on application development and implementation. Students will also dive into specific domains in the Industry verticals like FinTech, agriculture, manufacturing, and others.
Athena Title
Informatics II
Equivalent Courses
Not open to students with credit in INFO 3000E
Prerequisite
INFO 2000 or INFO 2000E or permission of department
Semester Course Offered
Offered every year.
Grading System
A - F (Traditional)
Course Objectives
Upon successful completion of this course, students will be able to: • Apply advanced data engineering technologies using APIs and relational databases and perform ETL operations on industry-related data. • Use advanced machine learning technologies like Deep Learning (DL). • Develop models using frameworks for ML and DL. • Understand and apply Time Series Analysis and forecasting techniques. • Develop and deploy Web Applications using web development frameworks. • Do hands-on project work and implementations in various FinTech verticals.
Topical Outline
Machine Learning and Forecasting: Intuition and the Model Building Process Optimization (Convexity, Grad Descent) Supervised ML (Regression, Classification Algorithms, and Models) Unsupervised Learning (Clustering, PCA, and other Algorithms) Time Series Analysis Programming for Data Science – Development Frameworks: Machine Learning Frameworks – Scikit Learn, Pytorch Machine Learning Deployment – Web Apps Advanced-Data Engineering: Data Wrangling – ETL, Mapping, Scraping, Formatting Advanced Machine Learning: Anomaly Detection (Gaussian Approach, DBSCAN) Deep Learning (CNN, NLP, RNN, Frameworks) Time Series Forecasting – ARIMA and RNN Methods Clinical Work: Sandbox Projects – Predictive Analytics for Industry Applications Programming – Natural Language Processing (NLP)
Syllabus