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    Syracuse University
   
 
  Sep 21, 2017
 
 
    
2017-2018 Graduate Course Catalog

Data Science, CAS


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Contact:

Carsten Oesterlund, Program Director, 309 Hinds Hall, (315) 443-2911, igrad@syr.edu

Website:

https://ischool.syr.edu/future/cas/datascience.aspx

Overview:

The Certificate of Advanced Study (CAS) in Data Science program requires 15 credit hours and prepares students to work with large amounts of data using information technologies as tools to gain knowledge and insight. The 2 Core Courses (6 credits) focus on handling data through its full lifecycle: architecting, acquiring, analyzing, and archiving data. The remaining elective credits enable specializations in data analytics, data storage and management or other areas such as data visualization.

All candidates should have a bachelor’s degree or equivalent. In addition, it is recommended that potential students have a strong background in science, statistics, research, and/or information technology. Applicants should have an interest in interdisciplinary work focused on managing big data using information technologies as tools. Prospective students who have an interest in data science, but lack the recommended undergraduate background, are encouraged to inquire. Individual consultations are available for such prospective students to explore their potential candidacy.

 

Student Learning Outcomes


1.Document, analyze, and translate needs into technical designs and informatics solutions

2.Explain the general data lifecycle and relevant techniques from data acquisition, transformation, storage, retrieval, analysis, visualization, preservation, and publishing/sharing. 

3.Apply various mathematical concepts, algorithms, technical standards, and principles to small and big data sets.  

4.Develop and deliver professional communications in the field.   Liaise with a range of people, including business managers, scientists, and IT developers.

5.Apply privacy and ethics principles in data management and analysis

6.Employ data storytelling and dive into the data, find useful patterns, and articulate what patterns have been found, how they are found, and why they are valuable and trustworthy

Curriculum:


This certificate requires 15 graduate credits. All courses are 3 graduate credits unless specified otherwise.

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