Data Science (M.A.S.) (Coursera)
At 杏吧原创 Tech, data science isn鈥檛 just about data. Data science is about applying the
scientific method to data analysis in the real world. This fully online program is offered through
Coursera and is not F-1 eligible.
Understand how to make sense of data using high-level mathematics, statistics, and
computer science, and become skilled at communicating your insights with the
program offered through a partnership with Coursera. This fully online program is not
F-1 eligible.
Use the latest technological tools and methods to analyze data and visualize results, and learn
about the important skills needed to articulate your discoveries, too. When you graduate from
this program, you will be able to explore and improve the structure of available data, create
and evaluate models, and construct and test hypotheses.
Being ready to work in the interdisciplinary field of data science requires an interdisciplinary
education. Because the computer science and applied mathematics departments that support
this program focus on interdisciplinary study and research, you鈥檒l learn data science concepts
that are applicable across business, industry, academia, and public sectors.
Program Overview
Being ready to work in the interdisciplinary field of data science requires an
interdisciplinary education. Understand how to make sense of data using high-level
mathematics, statistics, and computer science, and become skilled at communicating your
insights.
Career Opportunities
- Database architects
- Data warehousing specialists
- Information security analysts
- Data scientists
- Business intelligence analysts
- Clinical data managers
- Database administrators
Through the curriculum of the Master of Data Science program, you鈥檒l encounter an equal blend
of courses in mathematics, statistics, and computer science. You鈥檒l also take professional
communications courses in which you鈥檒l practice how to present data findings.
Pathway Courses (three credits)
Statistics Pathway鈥擠ata Science Foundations: Statistical Inference
1. Probability Theory: Applications for Data Science (one credit)
2. Statistical Inference for Estimation in Data Science (one credit)
3. Hypothesis Testing for Data Science (one credit)
Computer Science Pathway鈥擠ata Science Foundations: Data Structures and Algorithms
1. Algorithms for Searching, Sorting, and Indexing (one credit)
2. Trees & Graphs: Basics (one credit)
3. Dynamic Programming, Greedy Algorithms (one credit)
Data Science Core Courses (15 credits)
- MATH 563 Mathematical Statistics or MATH 564 Applied Statistics
- CS 584 Machine Learning or MATH 569 Statistical Learning
- SCI 511 Project Management or SCI 522 Public Engagement Scientists
- CSP 571 Data Preparation and Analysis
Select a minimum of one course from the following:
- CS 525 Advanced Database Organization
- CS 554 Data-Intensive Computing
- CSP 554 Big Data Technologies
- Data Science Capstone (six credits)
- CSP 572 Data Science Practicum
- Data Science Electives (12 credits)
- CS 425 Database Organization
- CS 430 Introduction to Algorithms
- CS 450 Operating Systems
- CS 520 Data Integration Warehousing
- And more
杏吧原创 Tech's inclusive program welcomes learners from all walks of life. The performance-based admissions process serves capable learners ready to start their next educational journey without transcripts, tests, application fees, or other hurdles. No matter their background or prior education, students taking these programs can opt to take three short courses (Introduction to Relational Databases, Linear Regression, and either a Statistics Pathway or the Computer Science Pathway course) on Coursera with at least a B in each course and you'll be accepted.
More Information
For answers to specific questions, contact 杏吧原创-Tech-MAS-DS@coursera.org