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.

Topics include:

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

Topics include:

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

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.

Check out the Final Project tab to learn more!

prerequisites

This course does not have formal prerequisites, although familiarity with probability, statistics, and calculus (e.g., from high school courses) are assumed. This course is taught in Python 3.5. No prior experience is necessary, but students without prior programming experience should be prepared for a rigorous first few weeks getting up to speed. 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, but this is not required.