Courses materials, notebooks, autograders, assignments

Applications of data science through a series of team projects solving real-world problems. Teams compete with each other to design and implement the top-performing computational pipelines. Prerequisites: machine learning fundamentals.

Spring 2019, Spring 2018, Fall 2016

Introduction to programming in Python for life scientists. Students learn the basics of Python and its language constructs (variables, types, lists, loops, conditionals, functions, arrays) to answer questions in biology (genome sequence alignment, molecular dynamics trajectories, dynamical systems modeling, and bioimage analysis). Preqrequisites: none

Fall 2018, Spring 2017

Introduction to statistical machine learning using Python. Students are instructed in both the theoretical formulations and Python implementations of machine learning algorithms that comprise classification, clustering, and dimensionality reduction, including foundational probability and linear algebra. Preqrequisites: none

 

Introduction to programming with Python. Students will learn the basic concepts of programming (variables, types, data structures, conditionals, loops, functions, modules) and how these are used in Python. Students will also be introduced to fundamental data science questions and work through them using Python. Preqrequisites: none

Fall 2016

Fully online introduction to programming with Python. Students will learn the basic concepts of programming (variables, types, data structures, conditionals, loops, functions, modules) and how these are used in Python. Students will also be introduced to fundamental data science questions and work through them using Python. Preqrequisites: none

Summer 2018, Summer 2017, Summer 2016