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**.

Advanced machine learning techniques and frameworks, including:
kernel methods, stochastic embeddings, motion tracking,
linear dynamical systems, spectral graph methods, and deep
learning; frameworks include Spark, dask, Keras, and PyTorch.
**Prerequisites: machine learning fundamentals.**

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**

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**