The "big data revolution" has emphasized computational techniques for decision-making with data. Large-scale optimization, data analysis, and visualization are now commonplace among researchers and practitioners alike. More than ever, there is a need to develop, implement, and use techniques in computational practice.
This course is a multi-session workshop on software tools for informing decision-making using data, focusing on optimization, statistics, machine learning, and best research practices. We concentrate on teaching elementary and advanced principles of computational practice using common software and practical methods. By the end of the course, students will possess a baseline technical knowledge for modern research practice.
The course comprises 8 self-contained modules. Each module consists of a 3-hour interactive workshop where participants learn a specific software tool and a set of exciting concepts.
The 8 modules:
- Session 1: Introduction to Machine Learning with Python
- Session 2: Introduction to Deep Learning
- Session 3: ChatGPT, NLP, Adavanced Deep Learning
- Session 4: Data Wrangling in R
- Session 5: Intro to Optimization with Julia
- Session 6: Advanced Optimization
- Session 7: Terminal, Git, and Project Management
- Session 8: Distributed Computing and Tips & Tricks