What is Data Science?

Data is the core of all domains from material science to healthcare. Mastering big data requires a set of skills spanning a variety disciplines, from distributed systems to statistics to machine learning. This course will provide an overview of the wide area of data science, with a particular focus on to the tools required to store, clean, manipulate, visualize, model, and ultimately extract information from large amounts of data.

Syllabus
Course Calendar
Lecture Slides & Material
Panopto

Topics Covered

• Database Design and SQL
• Web Scraping & Data Cleaning
• Hypothesis Testing
• Machine Learning
• Mapreduce
• Differential Privacy
• Correlation vs Causation

Final Project

Throughout the entire course you will be working on a data science project which seeks to answer an interesting and important real-world question. You will be collecting your own data, cleaning it, modeling it, visualizing it, and finally presenting your results in a poster session at the end of the course. You will work in groups of four, and will be assigned a mentor TA to help you through the process.

Additionally, your project can be used as a capstone with just a few extra requirements, fully integrating what you will have learned in the course, and building a fully-functional data science application.

Prerequisites

The formal prerequisites to this course are CSCI 0160, 0180, or 0190. Additional experience in software engineering is recommended, including CSCI 0320 or 1320. This course is taught in Python 3.7, but no prior experience is necessary. We will provide several resources to get students started with Python at the beginning of the course. It is suggested that students also have experience in statistics (APMA 1650 or CSCI 1450) and linear algebra (MATH 0520, MATH 0540, or CSCI 0530) for the statistics and machine learning portion of this course.