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. 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 courseAs a result, the program can be extended and completed over 12 or 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

ENG EK 500: Probability with Statistical Applications

CAS MA 583: Stochastic Processes

ENG EC 505: Stochastic Processes

  • 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

CAS CS 561: Data Systems Architecture

CAS CS 660: Graduate Introduction to Database Systems

CAS CS/ENG EC 528: Cloud Computing

CDS DS 563: Algorithmic Techniques for Taming Big Data

CAS CS 565: Algorithmic Data Mining

CAS CS 562: Advanced Database Applications

ENG EC 500: Optimization for Machine Learning

  • 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 644: Machine Learning for Business Analytics

CAS CS 505: Introduction to Natural Language Processing

CAS CS/ENG EC 523: Deep Learning

CAS CS 542: Machine Learning

GRS CS 640: Artificial Intelligence

ENG EC 503: Introduction to Learning from Data

ENG EC 700: Introduction to Reinforcement Learning

ENG EC 500: Online Learning

  • 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

  • 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

CAS CS 538: Fundamentals of Cryptography

CAS CS 558: Network Security

ENG EC 521: Cybersecurity

ENG EC 700: Advanced Cybersecurity

ENG EC 700: Vulnerability, Defense Systems, and Malware Analysis

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 I ( 2 cr.) New Course

CDS DS 729: Data Science Product Management II ( 2 cr.) New Course 

Business Pathway:

CDS DS 644: Machine Learning for Business Analytics

QST BA 860: Marketing Analytics 

QST BA 879: Financial Analytics

QST BA 875: Operations and Supply Chain Analytics

QST BA 880: People Analytics 

Computational Biology Pathway:

ENG BE 562: Computational Biology

ENG BF 527: Applications in Bioinformatics

ENG BF 550: Foundations of Programming, Data Analytics, and Machine Learning

ENG BF 768: Biological Database Systems

Social Technical Pathway:

CDS DS 574: Algorithmic Mechanism Design

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

CDS DS 657: Law for Algorithms

CDS DS 680 Data, Society, and Ethics

Security and Privacy Pathway:

CDS DS 653: Cryptography for Data Science

CAS CS 538: Fundamentals of Cryptography

ENG EC 521: Cybersecurity

ENG EC 700: Advanced Cybersecurity

ENG EC 700: Vulnerability, Defense Systems, and Malware Analysis

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