MSDS Curriculum

Curriculum

The MSDS is a 32-credit flexible program designed to meet the goals of students looking to pursue either academic or professional careers in data science, and can be completed in as little as 9 months (2 semesters). Students will declare either a Core Methods Focused Concentration or Applied Methods Focused Concentration. In addition to the core curriculum and concentration courses, the MSDS program offers students a unique opportunity to enhance their learning through an optional summer internship or master’s thesis course. As a result, the program can be extended and completed over 16 months. Please note that the optional summer internship course is only available for students graduating in 16 months. All students begin the program once every year in September; Spring entry term is not offered.

Requirements

Eight semester courses (32 credits) approved for graduate study are required.

Course requirements include 5 competency courses, with at least one in each of the following areas:

A1 Modeling and Predictive Analytics
A2 Data-Centric Computing
A3 Machine Learning and AI
A4 Social Impact
A5 Security and Privacy

Plus 3 additional courses:

CDS DS 701 Tools for Data Science (Must be taken in the Fall Semester)
Concentration Elective 1
Concentration Elective 2

CDS DS 701: Tools for Data Science

The goal of the course is to give students exposure to, and practical experience in, formulating data science questions – particularly learning how to ask good questions in a specific domain. The course covers methods of obtaining data and common methods of processing data from a practical standpoint. It is organized around a semester-long group project in which students are placed into teams and engage with “clients” who bring data science questions from a particular domain.

Competency Courses

Below is only a sample list of courses. The actual course list varies each semester. Once enrolled, students will receive an updated list of the available courses that semester.

A1 Modeling and Predictive Analytics - Covers the formulation of statistical models to describe data, methods for fitting models to data, and use of models for prediction and inference. Approved courses that fall into this category are:

CAS MA 575: Linear Models

CAS MA 576: Generalized Linear Models

CAS MA 578: Bayesian Statistics

CAS MA 585: Time Series and Forecasting

CAS MA 585: Time Series

CAS MA 589: Computational Statistics

CDS DS 592: Special Topics in Mathematical and Computational Sciences (Might change to other competencies)

CAS MA 592: Intro to Causal Inference

CAS MA 583: Stochastic Process

CAS MA 568: Statistical Analysis of Point Process Data

CAS MA 582: Mathematical Statistics

QST BA 572 Business Experiments and Causal Methods

A2 Data-Centric Computing - Covers the algorithmic and programming techniques and system designs for processing, analysis, and management of data at scale. Approved courses that fall into this category are:

CDS DS 522: Stochastic Methods for Algorithms

CDS DS 563: Algorithmic Techniques for Taming Big Data

CDS DS 598: Special Topics in Machine Learning - Engineering for Big Data Workloads (Might change to other competencies)

CAS CS 561: Software Engineering Development on Modern Cloud Environments

CAS CS 660: Graduate Introduction to Database Systems

CAS CS 528 /ENG EC 528: Cloud Computing

ENG EC 525: Optimization for Machine Learning

ENG EC 503: Introduction to Learning from Data

CAS CS 551: Streaming and Event-driven Systems

CAS MA 539: Methods of Scientific Computing

A3 Machine Learning and AI - Covers methods for supervised, unsupervised, and reinforcement learning methods applied to structured and unstructured data. Approved courses that fall into this category are:

CDS DS 598: Special Topics in Machine Learning (Might change to other competencies)

CDS DS 542: Deep Learning for Data Science

CDS DS 543: Introduction to Reinforcement Learning

CDS DS 592 Special Topics in Mathematical and Computational Sciences - Intro to Sequential Decision Making

CAS CS 505: Introduction to Natural Language Processing

CAS CS 523/ENG EC 523: Deep Learning

CAS CS 542: Machine Learning

GRS CS 640: Artificial Intelligence

CAS MA 615: DS in R

CAS CS 541: Applied Machine Learning

GRS CS 791: Advanced Topics in Computer Vision

A4 Social Impact - Covers considerations of the social implications from the deployment of data science and AI systems, including issues of ethics, fairness, and bias. Approved courses that fall into this category are:

CDS DS 682: Responsible AI, Law, Ethics, and Society

CDS DS 657: Law for Algorithms

CDS DS 680: Data, Society, and Ethics

CDS DS 587: Data Science in Human Context

A5 Security and Privacy - Covers methods and algorithms that protect user privacy, guarantee information security, and assess system. Approved courses that fall into this category are:

CDS DS 653: Cryptography for Data Science

CDS DS 593: Privacy-Conscious Computer Systems

CDS DS 593 - Special Topics in DS Methodologies - Privacy in Practice (Might change to other competencies)

CAS CS 538: Fundamentals of Cryptography

CAS CS 548: Advanced Cryptography

ENG EC 521: Cybersecurity

Core Methods Concentration

One CDS DS 701: Tools for Data Science course

Plus

Two courses from any of the following Group A areas (see above):

A1 - Modeling and Predictive Analytics

A2 - Data-Centric Computing

A3 - Machine Learning and AI

Applied Methods Concentration

One CDS DS 701: Tools for Data Science

Plus

Two courses from the approved list of applied methods courses (all courses below are 4 credits unless otherwise noted). Students are free to take any two courses from the entire list below. Some courses naturally form pathways but pathways are not a requirement; students may mix and match across applied areas.

Data Science Pathway:

CDS DS 719: Data Science Product Management

CDS DS593: Special Topics in Data Science Methodologies --- Data Engineering at Scale (Might change to other competencies)

GRS CS 630: Graduate Algorithms

Business Pathway:

QST BA 860: Marketing Analytics

QST BA 870: Financial Analytics

QST BA 875: Operations and Supply Chain Analytics

QST BA 880: People Analytics

QST BA 815: Competing with Analytics

QST BA 843 / QST IS 843: Big Data Analytics for Business

QST BA 878: Machine Learning and Data Infrastructure in Health Care

QST BA 885: Advanced Analytics 2

Computational Biology Pathway:

CDS DS 526: Critical Reading in Biological Data Science

CDS DS 630 Intro to Bioinformatics and Computation Biology

CDS DS 596: Special Topics in Natural, Biological and medical science (Might change to other competencies)

ENG BE 562: Computational Biology: Machine Learning Fundamentals

ENG BF 527: Applications in Bioinformatics

ENG BF 768: Biological Database Systems

Social Technical Pathway: