Data Science Computing

This course is an introduction to mainstream programming and high performance computing techniques and tools in data science. 

List of Topics

• The fundamental Computer System (Computer Architecture and Operating Systems)

• Program Profiling to find bottlenecks

• Data Structures and Algorithms

• Matrix and Vector computation

• Concurrency & Multiprocessing modules

• High Performance Computing using Clusters & GPUs

• Introduction to big data files, text data, and web scraping

• Data manipulation and management via Bash, Pandas, Relational Data Management Systems

• Introduction to big data systems: Hadoop, Spark

• Introduction to convex optimization

• Introduction to (interactive) visualization

Textbook

·         High Performance Python: Practical Performant Programming for Humans, by Micha Gorelick and Ian Ozsvald

·         Convex Optimization, by Stephen Boyd and Lieven Vandenberghe

·         Python for Data Analysis: Data Wrangling with Pandas, Numpy, and IPython, by Wes McKinney

·         Lecture notes

Recommended books:

-          Effective Python: 59 Specific Ways to Write Better Python

-          Introduction to High Performance Computing for Scientists and Engineers, Georg Hager and Gerhard Wellein

-          Deep Learning, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

-          Deep Learning with PyTorch, by Eli Stevens and Luca Antiga