Course Descriptions — School of Data Science

Overview

Foundation and Core courses are divided into 6 sequences, each with between 2 and 4 courses:

Computational Methods (3 courses) - Covering data science programming, numerical optimization and big data analytic methods
Machine Learning (4 courses) – Covering data analysis methods, including but not limited to linear models, neural networks, ensemble methods, clustering, etc.
Theory (3 courses) – Covering probability, mathematical statistics, and estimation theory
Data Engineering (1 course) – Covering applied data management and ML ops
Value (2 courses) – Covering ethical considerations of data science
Research Methods (1 course) – Covering professional development and research practices

Note: The topics listed below are meant to be representative of the topics covered in the course. The Graduate Record represents the official repository for academic program requirements.

Computational Methods

These courses cover data science programming, numerical optimization and big data analytic methods.

Computation I: Fundamentals
Year 1, Fall Term (Foundation)

Basic and advanced data structures
Searching and sorting
Greedy algorithms
Linear programming
Basics of databases

Computation II: Numerical Analysis & Optimization
Year 1, Spring Term (Foundation)

Numerical errors
Root finding algorithms
Numerical Linear Algebra
Numerical optimization

Computation III: Distributed Computing
Year 2, Fall Term (Core)

Spark for large-scale analytics
Resilient distributed datasets
Tools for data processes, storage, and retrieval (AWS)

Machine Learning

These courses cover a wide range of applied data analysis methods.

Machine Learning I
Introduction
Year 1, Fall Term (Foundation)

Generalized Linear Model focused
Regularization
Model diagnostics/ evaluation
Inference, both Bayesian and Frequentist
Decision trees/ Random forests

Machine Learning II
Methods and Applications
Year 1, Spring Term (Foundation)

Clustering methods
SVMs
Shallow neural nets
Ensemble methods
Dimension reduction
Anomaly detection
Recommender systems

Machine Learning III
Deep Learning
Year 2, Fall Term (Core)

Deep neural networks
Convolution and recurrent NNs
Transformers
Encoder/decoder
Generative networks
GANs
LSTM

Machine Learning IV
Advanced Topics
Year 2, Spring Term (Core)

Reinforcement Learning
Natural Language Processing
Bayesian Machine Learning

Theory

This sequence covers mathematical statistics, probability theory, and advanced linear algebra for statistics/data science applications.

Theory I
Probability and Stochastic Processes
Year 1, Fall Term (Foundation)

Probability theory
Random variables
Univariate estimation
Limit theorems
Stochastic processes

Theory II
Inference and Prediction
Year 1, Spring Term (Foundation)

Frequentist, likelihood & Bayesian Inference
MLE & Method of Moments estimation
Information theory
Resampling theory
Computational algorithms (e.g. EM, MCMC)

Theory III
Linear Models
Year 2, Fall Term (Core)

Linear algebra for linear models
Projections, null spaces, SVD, eigenvalues/vectors.
Linear model fitting, estimation and prediction.

Data Engineering

This sequence covers “practical” considerations for data scientists.

Data Engineering I
Data Management & Visualization
Year 1, Fall Term (Foundation)

Local environments
Containers
Using cloud compute
Webscraping
Dashboard development
Data wrangling /management
Data visualization

Data Engineering II
ML Ops
Year 2, Spring Term (Core)

Scalability, performance & security
Handling resource constraints
Streaming data collection
Cutting-edge database technologies

Value

This sequence covers ethical considerations for data scientists.

Value I
Data Ethics, Policy & Governance
Year 1, Fall Term (Core)

Fairness, accountability & transparency
Digital rights & regulatory frameworks
Harm, bias & discrimination
Policy design, implementation & evaluation
International/multistakeholder agreements
Open movement
Technical debt
Data justice

Value II
Data and Society
Year 2, Spring Term (Core)

Applied research and case studies re: data ethics/policy/governance
Auditing algorithms & ADS
Risk assessment tools
Civic tech & community engagement
Evidence-based policymaking
Alternative data governance models, data trusts and data stewardships
Responsible innovation

Research Methods

Data Science Research Methodology
Year 2, Spring Term (Core)

This course helps students transition into principal investigators and generators of data science-based knowledge by developing practical skills necessary to conduct high quality data science research, advance development into producers and critical consumers of research, and further development into professional data scientists broadly defined.