Foundation
Research Design and Application for Data and Analysis

Exploring and Analyzing Data

Storing and Retrieving Data

Applied Machine Learning

Data Visualization and Communication

Advanced

Experiments and Causal Inference

Behind the Data: Humans and Values

Scaling Up! Really Big Data

Applied Regression and Time Series Analysis

Maching Learning at Scale

Capstone Course
Synthetic Capstone Course

Curriculum:

Total Units: 32

You must take the following required courses (12 units):

CS 570 - Analysis of Algorithms (4)
CS 585 - Database Systems (4)
CS 561 - Foundations of Artificial Intelligence (4)
Group Electives (3 course - minimum of 1 course from each of the two groups, 9-12 units):

Group 1 - Data Systems:

CSCI 548 - Information Integration on the Web (4)
CSCI 572 - Information Retrieval and Web Search Engines (4)
CSCI 586 - Database Systems Interoperability (3)
CSCI 587 - Geospatial Information Management (4)
CSCI 653 - High Performance Computing and Simulation (4)
CSCI 685 - Advanced Topics in Database Systems (4)

Group 2 - Data Analysis:

CSCI 567 - Machine Learning (4)
CSCI 573 - Probabilistic Reasoning (3)
CSCI 686 - Advanced Big Data Analytics (4)
ISE 520 - Optimization: Theory and Algorithms (3)
MATH 467 - Theory and Computational Methods for Optimization (4)
MATH 574 - Applied Matrix Analysis (3)

Additional Electives (8-11 units):

Any 500 or 600 level course in CSCI
MATH 458 - Numerical Methods (4)
MATH 501 - Numerical Analysis and Computation (3)
MATH 502ab - Numerical Analysis (3-3)
MATH 505a - Applied Probability (3)
MATH 601 - Optimization Theory and Techniques (3)
MATH 650 - Seminar in Statistical Consulting (3)
CSCI 598 - Engineering Writing and Communication (1) AND*
ENGR 596 - Engineering Internship (1, max 3)
CSCI 590 - Directed Research (1-4, max 4)
CSCI 591 - Computer Science Research Colloquium (1, max 2)
*CSCI 598 must be taken BEFORE a student can be approved for ENGR 596.

NYU Center for Data Science

http://cds.nyu.edu/academics/ms-in-data-science/curriculum/required-courses/

Intro to the MS in DS at NYU video.

DS-GA-1001 Intro to Data Science 3
DS-GA-1002 Statistical and Mathematical Methods for Data Science 3
Data Science Elective 1

DS-GA-1003 Machine Learning and Computational Statistics 3
DS-GA-1004 Big Data 3
Data Science Elective 2 3

DS-GA-1005 Inference and Representation 3
DS-GA-1006 Capstone Project in Data Science 3
Data Science Elective 3 3

Data Science Elective 4 3
Data Science Elective 5 3
Data Science Elective 6 3

http://www.soic.indiana.edu/graduate/degrees/data-science/ms-data-science/ms-requirements.html

Sample residential curriculums
Every course may not yet be offered, and a selection of these course are currently offered online.

Example decision-maker curriculum

Year 1 Semester 1:
I590: Topics in Informatics: Big Data Applications and Analytics
I590: Topics in Informatics: Management, Access, and Use of Big and Complex Data
STAT S520 Introduction to Statistics

Year 1 Semester 2:
B661: Database Theory and System Design
Z637: Information Visualization
B669: Scientific Data Management and Preservation

Year 1 Summer:
Z605: Internship in Library and Information Science

Year 2 Semester 1:
Z604: Data Curation
I525: Organizational Informatics and Economics of Security
I590: Topics in Informatics: Big Data Open Source Software and Projects

Example technical curriculum

Year 1 Semester 1:
B503: Analysis of Algorithm;
B561: Advanced Database Concepts
S520: Introduction to Statistics

Year 1 Semester 2:
B649: Cloud Computing
Z534: Search
B555: Machine Learning

Year 1 Summer:
Z605: Internship in Library and Information Science

Year 2 Semester 3:
B565: Data Mining
I520: Security For Networked Systems
Z637: Information Visualization

Example: Computational and Analytic Data Science Track

B649: Cloud Computing
I590: Topics: Projects on Big Data Software
I590: Topics: Data Science for Drug Discover
I590: Topics: Perspectives in Data Science
Z636: Data Semantics
Z637: Information Visualization

Course requirements for the MSDS program

Summer Term (approximately 6 weeks, starting mid-July):

• CS 5010: Programming and Systems for Data Science
• STAT 6430: Statistical Computing for Data Science

Fall Term:

• STAT 6021: Linear Models for Data Science
• CS 5012: Foundations of Computer Science
• SYS 6018: Data Mining
• DS 6001: Practice and Application of Data Science
• DS 6002a: Ethics of Big Data
• DS 6003a: Capstone Project

Spring Term:

• SYS 6016: Machine Learning
• DS 6002b: Ethics of Big Data
• DS 6003b: Capstone Project
• Elective 1
• Elective 2

Selection of elective courses is done in consultation with the program director. There are a variety of possible electives available, including the following:

• CS 6501: Special Topics in Computer Science
• CE 6400: Traffic Operations
• STAT 6130: Applied Multivariate Statistics
• STAT 5390: Exploratory Data Analysis
• SYS 6001: Introduction to Systems Engineering
• SYS 6003: Optimization I
• SYS 6005: Stochastic Systems I
• CS 6750: Database Systems
• STAT 5170: Applied Time Series
• STAT 5340: Bootstrap and Other Resampling Methods
• MATH 5110: Stochastic Processes

Statistics Track 6
Algorithmics Track 6
Infrastructure Track 6
Elective Credits 6
Capstone Credits 6
Colloquium Credits 6

CSCI 5523 - Introduction to Data Mining 3
CSCI 5707 - Principles of Database Systems 3
STAT 5302 - Applied Regression Analysis 3
Capstone Project (First Half) 3

CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming 3
EE 5239 - Introduction to Nonlinear Optimization 3
STAT 5401 - Applied Multivariate Methods 3
Elective 3
Capstone Project (Second Half) 3

http://datasciencemasters.org
Foundation

• Intro to Data Science UW / Coursera:
• Data Science / Harvard Video Archive & Course:
• Data Science with Open Source Tools Book $27:

Math

• Linear Algebra & Programming
• Statistics
• Differential Equations & Calculus
• Problem Solving (Problem-Solving Heuristics “How To Solve It”)

Computing

• Algorithms
• Distributed Computing Paradigms
• Databases
• Data Mining
• Machine Learning
• Probabilistic Modeling
• Deep Learning (Neural Networks)
• Social Network & Graph Analysis
• Natural Language Processing
• Analysis

Data Design

• Visualization
• Data Journalism

Python (Libraries)

• Data Structures & Analysis Packages
• Machine Learning Packages
• Networks Packages
• Statistical Packages
• Natural Language Processing & Understanding
• Live Data Packages
• Visualization Packages
• iPython Data Science Notebooks

Data Science as a Profession
Capstone Project

https://www.udacity.com/course/intro-to-data-science--ud359