https://msdatascience.miami.edu/

Program Overview

The Master of Science in Data Science is an interdisciplinary graduate program that combines the teaching of domain-specific and technical skills for analyzing large data sets.  Built upon a core of foundational data science courses in Computer Science, Electrical Engineering, and Mathematics, and a selection of courses from data science application domains, the program is interdisciplinary in nature.  Students interested in data science tools will be able to focus on tool principles and tool development, and students interested in data science application domains will be able to focus on the application of data science tools with a selection of courses that develop skills in one of three application areas.  The program also provides its students the option of doing an industrial internship, to acquire professional experience.  The program allows the various academic units involved to add courses in their specific application domains, thus keeping the program updated and relevant to current practice and industrial needs.  the program is both academic and professional in nature, providing course that are true to a Master's level degree and courses that reflect the needs of the profession.  

Admission Requirements

1.  Completion of an application.

2.  A Baccalaureate degree for an accredited institution.

3.  A minimum cumulative undergraduate GPA of 3.0.

4.  Introduction to Probability and Statistics and Computer Programming I (or equivalents).  Students may be admitted with deficiencies, which must be completed in addition to the degree requirements.  

5.  GRE general test scores

Applicants must rank in the 65% percentile or higher in the Quantitative Reasoning Test.  There is no minimum score requirement for other parts of the GRE.

6.  Students from non-English speaking countries must send either TOEFL or IELTS

TOEFL minimum score: Internet based - 92; Computer based - 237; Paper based 580

IELTS minimum score: 6.5

7.  A personal statement of intent in which the applicant details reasons for pursuing the degree.

Curriculum Requirements - General

Core Courses
Machine Learning or Data Mining3
Topics in Computer Science
Machine Learning
Data Visualization 3
Topics in Computer Science
Introduction to Infographics and Data Visualization
Statistics3
Applied Regression Analysis
Quantitative Methods II
Electives12
Programming (at least 3 elective credits have to be in Programming)
Topics in Computer Science
Introduction to Parallel Computing
Algorithm Design and Analysis
Computer Applications in Educational and Behavioral Science Research
Database Systems
Theory of Relational Databases
Object-Oriented and Distributed Database Management Systems
Special Topics in Electrical Engineering
Machine Learning and Data Mining
Topics in Computer Science
Neural Networks and Deep Learning
Neural Networks
Machine Learning
Data Mining
Statistical Learning
Pattern Recognition and Neural Networks
Mathematics and Statistics
Applied Multivariate Statistics
Measurement and Psychometric Theory
Categorical Data Analysis
Data Science Applications (at least 6 credits; some tracks may specify additional courses)6-9
Geographic Information Systems in Urban Design
Special Problems
Special Problems
Introduction to Artificial Intelligence
Principles of Artificial Intelligence
Spatial Data Analysis I
Special Topics in Journalism and Media Management
Internship/Capstone3-6
Total Credit Hours30

Curriculum Requirements - Technical Data Science Track

Core 9
Data Science Tools (choose one course from each domain)12
Programming
Topics in Computer Science
Introduction to Parallel Computing
Algorithm Design and Analysis
Database Systems
Theory of Relational Databases
Object-Oriented and Distributed Database Management Systems
Special Topics in Electrical Engineering
Data Analysis
Special Topics in Electrical Engineering
Data Mining
Statistics
Introduction to Probability Theory
Introduction to Mathematical Statistics
Data Science Application/Electives6
Internship/Capstone3
Total Credit Hours30

Curriculum Requirements - Smart Cities Track 

Core 9
Data Science Tools/Electives (3 credits must be taken in Programming)9
Data Science Applications9
Geographic Information Systems in Urban Design
Geographic Information Systems in Urban Design
Special Problems
Internship/Capstone3
Total Credit Hours30

Curriculum Requirements - Data Visualization Track

Core 9
Data Science Tools/Electives (3 credits must be taken in Programming)9
Students interested in spatial visualization may also take any of the following electives:
Geographic Information Systems I
Remote Sensing of the Environment
Spatial Data Analysis I
Spatial Data Analysis II
Data Science Applications9
Topics in Computer Science
Introduction to Infographics and Data Visualization
Special Topics in Journalism and Media Management
Internship/Capstone3
Total Credit Hours30

Curriculum Requirements - Marine and Atmospheric Sciences 

Core 9
Programming3
Topics in Computer Science
Introduction to Parallel Computing
Algorithm Design and Analysis
Data Science Applications15
Physics of Remote Sensing I - Passive Systems
Applied Remote Sensing
Physics of Remote Sensing I - Passive Systems
Applied Radar Remote Sensing
Introduction to Marine Geographic Information Systems
Introduction to Marine Geographic Information Systems - Laboratory
Or any other courses selected from the concentration course lists for the RSMAS Master of Professional Science (MPS), with advisor approval
Internship/Capstone3
Total Credit Hours30

Sample Plan of Study - General

Plan of Study Grid
Year One
FallCredit Hours
CSC 687 Topics in Computer Science (or another approved Data Science Tools course) 3
CSC 688 Topics in Computer Science (or another approved Data Visualization course) 3
MTH 642 Statistical Analysis (or another approved statistics course) 3
CSC 686 Topics in Computer Science (or another approved Programming course) 3
 Credit Hours12
Spring
CSC 632 Introduction to Parallel Computing (or another approved Programming course) 3
CSC 623 Theory of Relational Databases (or another approved Database Systems course) 3
CSC 746 Neural Networks and Deep Learning (or another approved Machine Learning or Data Mining course) 3
EPS 703 Applied Multivariate Statistics (or another approved Statistics course) 3
 Credit Hours12
Summer
CSC 793 Research Project (or internship experience) 3
CSC 794 Research Project (or internship experience) 3
 Credit Hours6
 Total Credit Hours30

Sample Plan of Study - Technical Data Science

Plan of Study Grid
Year One
FallCredit Hours
CSC 687 Topics in Computer Science (or another approved Data Science Tools course) 3
CSC 688 Topics in Computer Science (or another approved Data Visualization course) 3
MTH 642 Statistical Analysis (or another approved statistics course) 3
CSC 686 Topics in Computer Science (or another approved Programming course) 3
 Credit Hours12
Spring
CSC 623 Theory of Relational Databases (or another approved Database Systems course) 3
ECE 697 or 677 Special Topics in Electrical Engineering
or Data Mining
3
MTH 624 or 625 Introduction to Probability Theory
or Introduction to Mathematical Statistics
3
CSC 645 Introduction to Artificial Intelligence (or another approved Data Science Applications course) 3
 Credit Hours12
Summer
GEG 680 Spatial Data Analysis I 3
CSC 793 Research Project (or internship experience) 3
 Credit Hours6
 Total Credit Hours30

Sample Plan of Study - Smart Cities

Plan of Study Grid
Year One
FallCredit Hours
CSC 687 Topics in Computer Science (or another approved Data Science Tools course) 3
CSC 688 Topics in Computer Science (or another approved Data Visualization course) 3
MTH 642 Statistical Analysis (or another approved statistics course) 3
CSC 686 Topics in Computer Science (or another approved Programming course) 3
 Credit Hours12
Spring
ARC 594 Geographic Information Systems in Urban Design 3
ARC 684 Special Problems 3
ARC 685 Special Problems 3
ARC 697 Designing for the Internet of Things (or another approved ARC elective) 3
 Credit Hours12
Summer
ARC 701 or 810 Masters Final Project
or Master's Thesis
6
 Credit Hours6
 Total Credit Hours30

Sample Plan of Study - Data Visualization

Plan of Study Grid
Year One
FallCredit Hours
CSC 687 Topics in Computer Science (or another approved Data Science Tools course) 3
CSC 688 Topics in Computer Science (or another approved Data Visualization course) 3
MTH 642 Statistical Analysis (or another approved statistics course) 3
CSC 686 Topics in Computer Science (or another approved Programming course) 3
 Credit Hours12
Spring
JMM 622 or CSC 688 Introduction to Infographics and Data Visualization
or Topics in Computer Science
3
JMM 692 Special Topics in Journalism and Media Management 3
JMM 663 Applied Data Analytics for Journalism and Media Management (or another approved Data Visualization elective) 3
JMM 696 Special Topics in Visual Journalism (or another approved Data Visualization elective) 3
 Credit Hours12
Summer
JMM 815 Multimedia Project 6
 Credit Hours6
 Total Credit Hours30

Sample Plan of Study - Marine and Atmospheric Science

Plan of Study Grid
Year One
FallCredit Hours
CSC 687 Topics in Computer Science (or another approved Data Science Tools course) 3
CSC 688 Topics in Computer Science (or another approved Data Visualization course) 3
MTH 642 Statistical Analysis (or another approved statistics course) 3
CSC 686 Topics in Computer Science (or another approved Programming course) 3
 Credit Hours12
Spring
MES 660 Introduction to Marine Geographic Information Systems (or another approved Marine & Atmospheric Science elective) 3
OCE 642 Physics of Remote Sensing I - Passive Systems (or another approved Marine & Atmospheric Science elective) 3
OCE 643 Physics of Remote Sensing II - Active Systems (or another approved Marine & Atmospheric Science elective) 3
OCE 686 Applied Remote Sensing (or another approved Marine & Atmospheric Science elective) 3
 Credit Hours12
Summer
OCE 805 MPS Internship 6
 Credit Hours6
 Total Credit Hours30

Student Learning Outcomes

Upon completion of the MS in Data Science, students will be able to:

  1. Use mathematical, statistical, and computational techniques to analyze large datasets, including collecting data, cleaning data, integrating multiple data sets, and applying the analytical techniques to the data.
  2. Write computer programs for accomplishing the aforementioned analysis tasks and the analysis results obtained.
  3. Interpret domain data appropriately, and provide insights into the data at hand.
  4. Communicate the results of their analysis clearly to the relevant people, including decision-makers, stakeholders, and managers.
  5. Generalize data analysis skills to problems in a real-world setting.

Specific to the individual tracks.
(a) For the Technical Data Science track

  1. Use machine learning to discover the underlying structures and relationships in large datasets.
  2. Apply data analysis and data mining to identify patterns in large datasets and develop classification/prediction models.
  3. Deploy appropriate tools for visualizing data and their analysis results.

(b) For the Smart Cities track:

  1. Use data science techniques to collect and analyze data form buildings and infrastructure. 
  2. Use data analysis and visualization skills to inform the design, development, and management of sustainable and resilient environments.

(c) For the Data Visualization track:

  1. Use interactive and static visualization techniques for communication and dissemination to audiences with diverse levels of technological background/sophistication.
  2. Use visualization techniques for advocacy.

(d) For the Marine and Atmospheric Science track:

  1. Use public, private data sets, and their aggregates for domain-specific inquiries.
  2. Analyze data that covers large areas over time.
  3. Use data science skills to develop plans for analysis and execute them.
  4. Apply appropriate technologies to analyze marine and atmospheric data.