2019-2020 Academic Catalog 
    
    Aug 09, 2022  
2019-2020 Academic Catalog [ARCHIVED CATALOG]

Data Analytics, MSDA


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Master of Science in Data Analytics Overview


Program will be offered Spring 2020

The well-publicized proliferation of data across all sectors of the global economy has fueled demand for professionals with deep skills in accessing and analyzing data and communicating the resulting information to drive data driven decision-making. The Master’s of Science in Data Analytics (MSDA) is a STEM credential enabling our students to access this large, growing, and financially rewarding career.  It is clear that the emergence of data infused computer applications, internet-based financial and consumer transactions, the need for increased out-come analysis and attention to industry and consumer trends that careers in data analytics are here to stay. It is hard to identify a sector of our society that is not being touched by big data.

Master of Science in Data Analytics Requirements


The Master’s of Science in Data Analytics, MSDA, has a curriculum map of 10 (1 unit) courses.  Nine (9) courses are required and 1 course may be taken from numerous options. Incoming students with a strong math or programming background (i.e., candidates holding a B.S. in Computer Science) may waive up to two of the required courses and replace them with two other course options. Required courses follow:

  • MSIT 3030 Mathematical Statistics  This course will introduce students, through lecture and problem solving, to basic statistical analysis.
  • MSIT 3055 Linear Regression and Time Series  This course will build on Mathematical Statistics and introduce students to applied statistical analysis.
  • MSIT 3090 - Python Programming   Python is one one of today’s most prevalent programing languages and it’s beneficial for IT professionals, regardless of their career goals, to have a working knowledge of Python principles and practical applications. Additionally, many of our students seek careers in data management and IT/Cyber data analysis. The cases presented in the course focus on using Python to extract data and import it to industry standard analysis tools.
  • MSIT 3860 - Data Management for Information Technology   Digitized business processes and data analytics are essential to the performance and competitive advantage of a modern corporation. The course is intended to provide insight and an IT leadership perspective to the principles of data management, visualization, and data mining.
  • MSIT 3360 Data Warehouse and Applied SQL  This course will build on MSIT 3860 by examining data warehousing in greater depth through the 1st quarter of the class.  The remaining portion of the class will be dedicated to learning intermediate SQL techniques for the access and manipulation of data plus introduce the use of Github and Jupyter notebooks collaboration and version control.
  • MSIT 3350 - Data Mining With Splunk    The Internet of Things (IoT) is the standard platform for billions of smart devices that generate machine log data. Splunk Enterprise can harness and leverage this valuable machine data (which contains a definitive record of all user transactions, customer behavior, machine behavior, security threats, system health, fraudulent activity and more) to provide enterprises valuable business, operational, and security intelligence. Coursework will cover Business Intelligence key concepts, Splunk Enterprise architecture and hands-on working sessions requiring students to install Splunk to complete the exercises (mine machine data, identify data patterns, create Splunk reports, dashboards, alerts, and applications).
  • MSIT 3870 Applied Machine Learning  This course will be an investigation into the fundamental techniques and practices of machine learning (ML). The primary activities of the course will be a series of lectures and a corresponding series of lab sessions, programming projects, and written assignments. The course will focus on applying ML to practical problems in Data Science using Python and will also introduce the student to some of the fundamental theory of ML.
  • MSIT 3880 - Enterprise Data Architectures    Organizations, private and public, are under pressures forcing them to respond quickly to change and to be innovative in the way they operate. This drives them to be more agile and to make frequent and quick strategic, tactical, and operational decisions, decisions that often require considerable amounts of relevant data, information and knowledge. Processing these information assets, in the framework of the needed decisions, is what business intelligence is all about. This course provides students hands on working experience with Tableau, a leading BI solution and BI solutions which deliver computerized support for managerial decision making.
  • MSIT 3999 - Capstone Practicum   Integrates the course work of the MSIT program into a comprehensive application. While in teams under the supervision of a faculty instructor, students address an actual challenge faced by an organization of a department within an organization. Students study the issues, review industry trends, research the depth of the issue, and make a series of recommendations to key members of an organization. The practicum culminates in a formal written and oral presentation of the team’s work, which is evaluated by faculty and organization professionals.

Optional Courses:

  • MPC 3900  Research and Marketing Analytics  
  • MSIT 3141 Health Data and Records Systems
  • MPA 3110  Applied GIS for Decision Makers 
  • MSIT 3450 Health Informatics 
  • MSIT 3950 Independent Study To be approved by advisor (Sentiment analysis, AI, text recognition, etc…)

 

 

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