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.

- Instruction:
**practicum** - Preqrequisites:
**CSCI 6360, CSCI 6380, CSCI 8950** - Target audience:
**advanced**

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.

- Instruction:
**lecture** - Preqrequisites:
**CSCI 3360, CSCI 4380/6380, CSCI 8950** - Target audience:
**intermediate**

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).

- Instruction:
**lecture** - Preqrequisites:
**none** - Target audience:
**beginners**

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.

- Instruction:
**lecture** - Preqrequisites:
**none** - Target audience:
**beginners**

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.

- Instruction:
**lecture** - Preqrequisites:
**none** - Target audience:
**beginners**

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.

- Instruction:
**online** - Preqrequisites:
**none** - Target audience:
**beginners**

**This course is currently retired.**

Machine learning with big data.

- Instruction:
**lecture** - Preqrequisites:
**none** - Target audience:
**intermediate**