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
Code | Title | Credit Hours |
---|---|---|
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 | ||
Design of Experiments | ||
Data Science Tools | 12 | |
Programming (at least 3 Data Science Tool credits have to be in Programming) | ||
CSC 615 | ||
Introduction to Parallel Computing | ||
Algorithm Design and Analysis | ||
Computer Applications in Educational and Behavioral Science Research | ||
Database Systems | ||
Theory of Relational Databases | ||
or ECE 672 | 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 | ||
or ECE 653 | 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 | ||
or PSY 633 | Structural Equation Modeling | |
Introduction to Multilevel Modeling | ||
or PSY 634 | Multilevel Modeling | |
Introduction to Probability Theory | ||
Introduction to Mathematical Statistics | ||
Statistical 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 | ||
Capstone | 3-6 | |
Total Credit Hours | 30 |
Curriculum Requirements - Technical Data Science Track
Code | Title | Credit Hours |
---|---|---|
Core | 9 | |
Data Science Tools (choose one course from each domain) | 12 | |
Programming | ||
CSC 615 | ||
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 Applications | 3-6 | |
Capstone | 3-6 | |
Computer Science Graduate Internship | ||
or CSC 670 | Directed Reading | |
Total Credit Hours | 30 |
Curriculum Requirements - Smart Cities Track
Code | Title | Credit Hours |
---|---|---|
Core | 9 | |
Data Science Tools (3 credits must be taken in Programming) | 9 | |
Programming Courses | ||
CSC 615 | ||
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 Applications | 6-9 | |
Geographic Information Systems in Urban Design | ||
Advanced Topics | ||
Designing for the Internet of Things | ||
Capstone | 3-6 | |
Computer Science Graduate Internship | ||
or ARC 686 | Special Problems | |
Total Credit Hours | 30 |
Curriculum Requirements - Data Visualization Track
Code | Title | Credit Hours |
---|---|---|
Core | 9 | |
Data Science Tools (3 credits must be taken in Programming) | 9 | |
Programming Courses | ||
CSC 615 | ||
Introduction to Parallel Computing | ||
Algorithm Design and Analysis | ||
Students interested in spatial visualization may also take any of the following electives: | ||
Data Science Applications | 6-9 | |
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 | ||
Capstone | 3-6 | |
Computer Science Graduate Internship | ||
or JMM 692 | Special Topics in Journalism and Media Management | |
Total Credit Hours | 30 |
Curriculum Requirements - Marine and Atmospheric Sciences Track
Code | Title | Credit Hours |
---|---|---|
Core | 9 | |
Data Science Tools (3 credits must be taken in Programming) | 3 | |
CSC 615 | ||
Introduction to Parallel Computing | ||
Algorithm Design and Analysis | ||
Data Science Applications | 12-15 | |
Includes the remaining 6 credits of Data Science Tools material. | ||
Applied Data Analysis | ||
or MPO 624 | Applied Data Analysis | |
Introduction to Ocean Remote Sensing | ||
Advanced Ocean Remote Sensing | ||
MES 660 | ||
MES 661 | ||
Advanced Studies | ||
Or any other courses selected from the concentration course lists for the RSMAS Master of Professional Science (MPS), with advisor approval | ||
Capstone | 3-6 | |
Computer Science Graduate Internship | ||
or ATM 774 | Advanced Studies | |
Total Credit Hours | 30 |
Curriculum Requirements - Educational Measurement and Statistics Track
Code | Title | Credit Hours |
---|---|---|
Core | 9 | |
Data Science Tools (3 credits must be taken in Programming) | ||
Programming Courses | 3 | |
CSC 615 | ||
Introduction to Parallel Computing | ||
Algorithm Design and Analysis | ||
Mathematics and Statistics Courses | 9 | |
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 | ||
or PSY 633 | Structural Equation Modeling | |
Introduction to Multilevel Modeling | ||
or PSY 634 | Multilevel Modeling | |
Meta-Analytic Methods for Research Synthesis | ||
Data Science Applications | 3-6 | |
Computer Applications in Educational and Behavioral Science Research | ||
Item Response Theory | ||
Advanced Topics in Research, Measurement, and Evaluation | ||
Capstone | 3-6 | |
Computer Science Graduate Internship | ||
or EPS 798 | Advanced Individual Study | |
Total Credit Hours | 30 |
Curriculum Requirements - Marketing Track
Please note: The MSDS Marketing Track requires 31 credits.
Code | Title | Credit Hours |
---|---|---|
Core | 9 | |
Data Science Tools (3 credits must be taken in Programming) | ||
Programming Courses | 3 | |
CSC 615 | ||
Introduction to Parallel Computing | ||
Algorithm Design and Analysis | ||
Marketing Courses | 8 | |
Foundations of Marketing Management | ||
Marketing Research and Decision Making | ||
Consumer Behavior | ||
Marketing Analytics | ||
Data Science Applications | 6-8 | |
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 | ||
Capstone | 3-6 | |
Computer Science Graduate Internship | ||
or MKT 699 | Directed Study | |
Total Credit Hours | 31 |
Sample Plan of Study - General
Year One | ||
---|---|---|
Fall | Credit Hours | |
CSC 615 | 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 Hours | 12 | |
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 Hours | 12 | |
Summer | ||
CSC 712 | Computer Science Graduate Internship | 6 |
Credit Hours | 6 | |
Total Credit Hours | 30 |
Sample Plan of Study - Technical Data Science
Year One | ||
---|---|---|
Fall | Credit Hours | |
CSC 615 | 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 Hours | 12 | |
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 Hours | 12 | |
Summer | ||
CSC 712 | Computer Science Graduate Internship | 6 |
Credit Hours | 6 | |
Total Credit Hours | 30 |
Sample Plan of Study - Smart Cities
Year One | ||
---|---|---|
Fall | Credit Hours | |
CSC 615 | 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 Hours | 12 | |
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 Hours | 12 | |
Summer | ||
CSC 712 | Computer Science Graduate Internship | 6 |
Credit Hours | 6 | |
Total Credit Hours | 30 |
Sample Plan of Study - Data Visualization
Year One | ||
---|---|---|
Fall | Credit Hours | |
CSC 615 | 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 Hours | 12 | |
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 | or another approved Data Visualization elective | 3 |
Credit Hours | 12 | |
Summer | ||
CSC 712 | Computer Science Graduate Internship | 6 |
Credit Hours | 6 | |
Total Credit Hours | 30 |
Sample Plan of Study - Marine and Atmospheric Science
Year One | ||
---|---|---|
Fall | Credit Hours | |
CSC 615 | 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 Hours | 12 | |
Spring | ||
MES 660 | 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 Hours | 12 | |
Summer | ||
CSC 712 or ATM 774 | Computer Science Graduate Internship or Advanced Studies | 6 |
Credit Hours | 6 | |
Total Credit Hours | 30 |
Sample Plan of Study - Educational Measurement and Statistics
Year One | ||
---|---|---|
Fall | Credit Hours | |
CSC 615 | 3 | |
CSC 646 | Introduction to Machine Learning with Applications | 3 |
EPS 700 | Quantitative Methods I | 3 |
EPS 701 | Introduction to Research Methods | 3 |
Credit Hours | 12 | |
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 Hours | 12 | |
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 Hours | 6 | |
Total Credit Hours | 30 |
Sample Plan of Study - Marketing
Year One | ||
---|---|---|
Fall | Credit Hours | |
Full Term Fall A/B | ||
CSC 615 | 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 Hours | 14 | |
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 Hours | 14 | |
Summer | ||
CSC 712 or MKT 699 | Computer Science Graduate Internship or Directed Study | 3 |
Credit Hours | 3 | |
Total Credit Hours | 31 |
- 1
MKT courses are 2-credit courses offered in 7-week A & B sessions throughout the Fall and Spring terms.
Mission
Drawing upon the University of Miami's strategic priority to foster interdisciplinary opportunities across the STEM fields, and leveraging the resources and collaboration of the Miami Institute for Data Science and Computing (IDSC), the mission of the Master of Science in Data Science is to enable data science training and research, and provide applied data science and computing opportunities, for students across all disciplines.
Program Goals:
1. To teach students programming skills not only for understanding the computer programs they use but also for getting started in developing their own programs.
2. To teach students mathematical and statistical foundations sufficient for understanding the underlying algorithms and the models developed.
3. To teach students how to turn domain questions into scientific investigations and how to interpret the results in their respective domain.
4. To teach practical problem-solving skills through an internship or project.
Student Learning Outcomes
Upon completion of the MS in Data Science, students will be able to:
- 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.
- Write computer programs for accomplishing the aforementioned analysis tasks and the analysis results obtained.
- Interpret domain data appropriately, and provide insights into the data at hand.
- Communicate the results of their analysis clearly to the relevant people, including decision-makers, stakeholders, and managers.
- Generalize data analysis skills to problems in a real-world setting.
Specific to the individual tracks.
(a) For the Technical Data Science track
- Use machine learning to discover the underlying structures and relationships in large datasets.
- Apply data analysis and data mining to identify patterns in large datasets and develop classification/prediction models.
- Deploy appropriate tools for visualizing data and their analysis results.
(b) For the Smart Cities track:
- Use data science techniques to collect and analyze data form buildings and infrastructure.
- Use data analysis and visualization skills to inform the design, development, and management of sustainable and resilient environments.
(c) For the Data Visualization track:
- Use interactive and static visualization techniques for communication and dissemination to audiences with diverse levels of technological background/sophistication.
- Use visualization techniques for advocacy.
(d) For the Marine and Atmospheric Science track:
- Use public, private data sets, and their aggregates for domain-specific inquiries.
- Analyze data that covers large areas over time.
- Use data science skills to develop plans for analysis and execute them.
- Apply appropriate technologies to analyze marine and atmospheric data.
(e) For the Educational Measurement & Statistics track:
- Demonstrate adequate mastery in the advanced statistical and measurement methodology in social and behavioral sciences.
- 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:
- Develop models to assess the sales impact of advertising and promotions.
- 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.
- 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