https://msdatascience.miami.edu/

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, 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 from a regionally accredited institution or foreign equivalent.

3. A minimum cumulative undergraduate GPA of 3.0.

4. Three letters of recommendation. 

5. Official transcripts from each post-secondary institution attended. Official transcripts in languages other than English must also be submitted with a certified English translation.

6. Introduction to Probability and Statistics, Linear Algebra, and Computer Programming I (or equivalents).  Students who require prerequisite courses will be admitted as non-degree seeking.  Upon passing any required prerequisite courses with a grade of "B" or better, the student would then be eligible for admission to the M.S. program the following semester.

7.  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

8.  A personal statement of intent in which the applicant details their reasons for pursuing this degree.

Curriculum Requirements - General

Core Courses
Machine Learning and Data Mining (choose 1 course)3
Introduction to Machine Learning with Applications
Machine Learning
Data Mining
Data Visualization (choose 1 course)3
Introduction to Computer Graphics
Introduction to Infographics and Data Visualization
Statistics (choose 1 course)3
Quantitative Methods I
Statistical Analysis
Applied Regression Analysis
Data Science Tools12
Programming (at least 3 Data Science Tool credits have to be in Programming)
Introduction to Python Programming for Graduate Students
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
Object-Oriented and Distributed Database Management Systems
Special Topics in Electrical Engineering (Advanced Big Data Analysis)
Data Visualization
Introduction to Computer Graphics
Spatial Data Analysis II
Geographic Information Systems I
Introduction to Infographics and Data Visualization
Advanced Infographics and Data Visualization
Machine Learning and Data Mining
Introduction to Machine Learning with Applications
Neural Networks and Deep Learning
Neural Networks
Machine Learning
Data Mining
Statistical Learning
Pattern Recognition and Neural Networks
Mathematics and Statistics
Quantitative Methods II
Applied Multivariate Statistics
Measurement and Psychometric Theory
Categorical Data Analysis
Item Response Theory
An Introduction to Structural Equation Modeling for Multivariable Data
Structural Equation Modeling
Introduction to Multilevel Modeling
Multilevel Modeling
Introduction to Probability Theory
Introduction to Mathematical Statistics
Statistical Analysis
Applied Regression Analysis
Data Science Applications 6-9
This is a sample list. Other electives may be chosen with approval of the MSDS Director.
Special Problems
Geographic Information Systems in Urban Design
Interactive Multimedia in Design
Designing for the Internet of Things
Advanced Studies
Introduction to Artificial Intelligence
Directed Reading
Topics in Computer Science
Principles of Artificial Intelligence
Advanced Topics in Research, Measurement, and Evaluation
Spatial Data Analysis I
Spatial Data Analysis II
Geographic Information Systems I
Remote Sensing of the Environment
Geographic Information Systems II
Web GIS
Special Topics in Journalism and Media Management
Introduction to Ocean Remote Sensing
Advanced Ocean Remote Sensing
Capstone3-6
Total Credit Hours30

Curriculum Requirements - Technical Data Science Track

Core 9
Data Science Tools (choose one course from each domain)12
Programming
Introduction to Python Programming for Graduate Students
Introduction to Parallel Computing
Algorithm Design and Analysis
Database Systems
Theory of Relational Databases
Object-Oriented and Distributed Database Management Systems
Data Analysis
Special Topics in Electrical Engineering
Data Mining
Statistics
Introduction to Probability Theory
Introduction to Mathematical Statistics
Data Science Applications3-6
Capstone3-6
Computer Science Graduate Internship
Directed Reading
Total Credit Hours30

Curriculum Requirements - Smart Cities Track 

Core 9
Data Science Tools (3 credits must be taken in Programming)9
Programming Courses
Introduction to Python Programming for Graduate Students
Introduction to Parallel Computing
Algorithm Design and Analysis
Students may choose from other courses throughout the MSDS curriculum to satisfy the Data Science Tools requirement, with advisor approval.
Data Science Applications6-9
Geographic Information Systems in Urban Design
Advanced Topics
Designing for the Internet of Things
Capstone3-6
Computer Science Graduate Internship
Special Problems
Total Credit Hours30

Curriculum Requirements - Data Visualization Track

Core 9
Data Science Tools (3 credits must be taken in Programming)9
Programming Courses
Introduction to Python Programming for Graduate Students
Introduction to Parallel Computing
Algorithm Design and Analysis
Students interested in spatial visualization may also take any of the following electives:
Data Science Applications6-9
Introduction to Computer Graphics
Spatial Data Analysis II
Geographic Information Systems I
Remote Sensing of the Environment
Geographic Information Systems II
Introduction to Infographics and Data Visualization
Special Topics in Journalism and Media Management
Capstone3-6
Computer Science Graduate Internship
Special Topics in Journalism and Media Management
Total Credit Hours30

Curriculum Requirements - Marine and Atmospheric Sciences Track

Core 9
Data Science Tools (3 credits must be taken in Programming)3
Introduction to Python Programming for Graduate Students
Introduction to Parallel Computing
Algorithm Design and Analysis
Data Science Applications12-15
Includes the remaining 6 credits of Data Science Tools material.
Applied Data Analysis
Applied Data Analysis
Introduction to Ocean Remote Sensing
Advanced Ocean Remote Sensing
Introduction to Marine Geographic Information Systems
Introduction to Marine Geographic Information Systems - Laboratory
Advanced Studies
Or any other courses selected from the concentration course lists for the RSMAS Master of Professional Science (MPS), with advisor approval
Capstone3-6
Computer Science Graduate Internship
Advanced Studies
Total Credit Hours30

Curriculum Requirements - Educational Measurement and Statistics Track

Core9
Data Science Tools (3 credits must be taken in Programming)
Programming Courses3
Introduction to Python Programming for Graduate Students
Introduction to Parallel Computing
Algorithm Design and Analysis
Mathematics and Statistics Courses9
Quantitative Methods I
Introduction to Research Methods
Quantitative Methods II
Applied Multivariate Statistics
Measurement and Psychometric Theory
Categorical Data Analysis
An Introduction to Structural Equation Modeling for Multivariable Data
Structural Equation Modeling
Introduction to Multilevel Modeling
Multilevel Modeling
Meta-Analytic Methods for Research Synthesis
Data Science Applications3-6
Computer Applications in Educational and Behavioral Science Research
Item Response Theory
Advanced Topics in Research, Measurement, and Evaluation
Capstone3-6
Computer Science Graduate Internship
Advanced Individual Study
Total Credit Hours30

Curriculum Requirements - Marketing Track

Please note: The MSDS Marketing Track requires 31 credits.

Core9
Data Science Tools (3 credits must be taken in Programming)
Programming Courses3
Introduction to Python Programming for Graduate Students
Introduction to Parallel Computing
Algorithm Design and Analysis
Marketing Courses8
Foundations of Marketing Management
Marketing Research and Decision Making
Consumer Behavior
Marketing Analytics
Data Science Applications6-8
includes the remaining 1 credit of Data Science Tools material.
Advertising and Communications Management
New Product Development
Strategic Brand Marketing
Strategic Marketing
Strategic Digital Media Management
Capstone3-6
Computer Science Graduate Internship
Directed Study
Total Credit Hours31

Sample Plan of Study - General

Plan of Study Grid
Year One
FallCredit Hours
CSC 615 Introduction to Python Programming for Graduate Students (or another approved Programming course) 3
JMM 622 Introduction to Infographics and Data Visualization (or another approved Data Visualization course) 3
CSC 646 Introduction to Machine Learning with Applications (another approved Data Science Tools course) 3
MTH 642 Statistical Analysis (or another approved statistics 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 712 Computer Science Graduate Internship 6
 Credit Hours6
 Total Credit Hours30

Sample Plan of Study - Technical Data Science

Plan of Study Grid
Year One
FallCredit Hours
CSC 615 Introduction to Python Programming for Graduate Students 3
JMM 622 Introduction to Infographics and Data Visualization (or another approved Data Visualization course) 3
CSC 646 Introduction to Machine Learning with Applications (another approved Data Science Tools course) 3
MTH 642 Statistical Analysis (or another approved statistics 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
CSC 712 Computer Science Graduate Internship 6
 Credit Hours6
 Total Credit Hours30

Sample Plan of Study - Smart Cities

Plan of Study Grid
Year One
FallCredit Hours
CSC 615 Introduction to Python Programming for Graduate Students (or another approved Programming course) 3
JMM 622 Introduction to Infographics and Data Visualization (or another approved Data Visualization course) 3
CSC 646 Introduction to Machine Learning with Applications (another approved Data Science Tools course) 3
MTH 642 Statistical Analysis (or another approved statistics 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
CSC 712 Computer Science Graduate Internship 6
 Credit Hours6
 Total Credit Hours30

Sample Plan of Study - Data Visualization

Plan of Study Grid
Year One
FallCredit Hours
CSC 615 Introduction to Python Programming for Graduate Students (or another approved Programming course) 3
JMM 622 Introduction to Infographics and Data Visualization (or another approved Data Visualization course) 3
CSC 646 Introduction to Machine Learning with Applications (or another approved Data Science Tools course) 3
MTH 642 Statistical Analysis (or another approved statistics 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
CSC 712 Computer Science Graduate Internship 6
 Credit Hours6
 Total Credit Hours30

Sample Plan of Study - Marine and Atmospheric Science

Plan of Study Grid
Year One
FallCredit Hours
CSC 615 Introduction to Python Programming for Graduate Students (or another approved Programming course) 3
JMM 622 Introduction to Infographics and Data Visualization (or another approved Data Visualization course) 3
CSC 646 Introduction to Machine Learning with Applications (another approved Data Science Tools course) 3
MPO 606 Introduction to Ocean Remote Sensing 3
 Credit Hours12
Spring
MES 660 Introduction to Marine Geographic Information Systems (or another approved Marine & Atmospheric Science elective) 3
ATM 624 Applied Data Analysis 3
MPO 707 Advanced Ocean Remote Sensing 3
MTH 642 Statistical Analysis (or another approved statistics course) 3
 Credit Hours12
Summer
CSC 712 or ATM 774 Computer Science Graduate Internship
or Advanced Studies
6
 Credit Hours6
 Total Credit Hours30

Sample Plan of Study - Educational Measurement and Statistics

Plan of Study Grid
Year One
FallCredit Hours
CSC 615 Introduction to Python Programming for Graduate Students 3
CSC 646 Introduction to Machine Learning with Applications 3
EPS 700 Quantitative Methods I 3
EPS 701 Introduction to Research Methods 3
 Credit Hours12
Spring
CSC 629 Introduction to Computer Graphics 3
EPS 705 Measurement and Psychometric Theory 3
EPS 711 Advanced Topics in Research, Measurement, and Evaluation 3
EPS 703 or 704 Applied Multivariate Statistics
or Computer Applications in Educational and Behavioral Science Research
3
 Credit Hours12
Summer
EPS 703 or 704 Applied Multivariate Statistics
or Computer Applications in Educational and Behavioral Science Research
3
CSC 712 or EPS 798 Computer Science Graduate Internship
or Advanced Individual Study
3
 Credit Hours6
 Total Credit Hours30

Sample Plan of Study - Marketing

Plan of Study Grid
Year One
FallCredit Hours
Full Term Fall A/B  
CSC 615 Introduction to Python Programming for Graduate Students 3
JMM 622 Introduction to Infographics and Data Visualization 3
Fall A 1  
MKT 640 Foundations of Marketing Management 2
MKT 641 Marketing Research and Decision Making 2
Fall B  
MKT 646 Consumer Behavior 2
MKT 647 Advertising and Communications Management 2
 Credit Hours14
Spring
Full Term Spring A/B  
CSC 646 Introduction to Machine Learning with Applications 3
EPS 702 Quantitative Methods II 3
Spring A  
MKT 649 Strategic Brand Marketing 2
MKT 677 Strategic Digital Media Management 2
Spring B  
MKT 650 Strategic Marketing 2
MKT 675 Marketing Analytics 2
 Credit Hours14
Summer
CSC 712 or MKT 699 Computer Science Graduate Internship
or Directed Study
3
 Credit Hours3
 Total Credit Hours31

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.

(e) For the Educational Measurement & Statistics track:

  1. Demonstrate adequate mastery in the advanced statistical and measurement methodology in social and behavioral sciences.
  2. Demonstrate adequate mastery for conducting statistical analyses and database management in social and behavioral sciences using the R and SAS programs.

(f) For the Marketing track:

  1. Develop models to assess the sales impact of advertising and promotions.
  2. Use models to optimize media spend on both traditional channels (TV, radio), online channels (search engines) and social media channels as well as monitor brand equity, customer satisfaction, and customer needs. 
  3. Develop models for Customer Relationship Management, dynamic pricing, revenue management, sales forecasting, sales force optimization, linking marketing actions to firm value, and balancing the trade-offs between e-Commerce and brick and mortar distribution system