M.S. in Data Science

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).  

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 - Individualized (no track)

¹

MSDS students must take this course for 3 credits. 

²

Courses may not be double-counted.  This applies to all MSDS students, regardless of track. For example, if a student takes JMM 622 to satisfy their Data Visualization Core requirement, the same course may not also count towards their Data Science Tools requirement.

³

The Capstone requires 3 credits.  However, students who take 6 credits of the capstone project or internship may count 3 of those credits towards their Data Science Applications requirement (for example, DSC 710 and DSC 711).  This is allowed in any track.

Curriculum Requirements - Technical Data Science Track

Curriculum Requirements - Smart Cities Track 

Curriculum Requirements - Data Visualization Track

Curriculum Requirements - Marine and Atmospheric Sciences Track

Curriculum Requirements - Educational Measurement and Statistics Track

Curriculum Requirements - Marketing Track

Curriculum Requirements - Bioinformatics Track

Sample Plan of Study - Individualized (no track)

Sample Plan of Study - Technical Data Science

Sample Plan of Study - Smart Cities

Sample Plan of Study - Data Visualization

Sample Plan of Study - Marine and Atmospheric Science

Sample Plan of Study - Educational Measurement and Statistics

Sample Plan of Study - Marketing

¹

MKT courses are 2-credit courses offered in 7-week A & B sessions throughout the Fall and Spring terms.

Sample Plan of Study - Bioinformatics

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:

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.

(g) For the Bioinformatics track:

1. Use molecular genetic lab techniques
2. Build pipelines to analyze complex molecular biological data
3. Apply appropriate statistical methods to analyze genetic/genomic data
4. Generate figures using complex biological data
5. Communicate findings with clarity and concision