{"id":1182,"date":"2020-10-22T16:55:35","date_gmt":"2020-10-22T20:55:35","guid":{"rendered":"https:\/\/blogs.gcc.edu\/dual\/?page_id=1182"},"modified":"2023-11-09T12:57:12","modified_gmt":"2023-11-09T17:57:12","slug":"m-s-business-analytics","status":"publish","type":"page","link":"https:\/\/blogs.gcc.edu\/dual\/m-s-business-analytics\/","title":{"rendered":"M.S. Business Analytics (Online)"},"content":{"rendered":"

Apply Now!<\/a><\/h3>\n

How to Apply<\/a><\/p>\n

Earn Your Master\u2019s Degree in Business Analytics.<\/a><\/h3>\n

 <\/h4>\n

Online Master’s in Business Analytics – Current Curriculum*<\/h4>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Course<\/strong><\/td>\nSuggested Term<\/strong><\/td>\n<\/tr>\n
MNGT 502<\/a> \u2013 Statistical Models<\/td>\nSummer I<\/td>\n<\/tr>\n
 <\/td>\n <\/td>\n<\/tr>\n
COMP 544<\/a> \u2013 Database Management<\/td>\nFall I<\/td>\n<\/tr>\n
SYSE 503<\/a> \u2013 Linear Optimization<\/td>\nFall I<\/td>\n<\/tr>\n
 <\/td>\n <\/td>\n<\/tr>\n
DSCI 531<\/a> \u2013 Introduction to Big Data<\/td>\nSpring I<\/td>\n<\/tr>\n
MNGT 512<\/a> \u2013 Forecasting<\/td>\nSpring I<\/td>\n<\/tr>\n
 <\/td>\n <\/td>\n<\/tr>\n
MNGT 542<\/a> \u2013 Ethically Architecting Data<\/td>\nSummer II<\/td>\n<\/tr>\n
 <\/td>\n <\/td>\n<\/tr>\n
MNGT 514<\/a> \u2013 Machine Learning (Current Topics)<\/td>\nFall II<\/td>\n<\/tr>\n
MNGT 552<\/a> \u2013 Case Studies<\/td>\nFall II<\/td>\n<\/tr>\n
 <\/td>\n <\/td>\n<\/tr>\n
MNGT 550<\/a> \u2013 Visualizing Data<\/td>\nSpring II<\/td>\n<\/tr>\n
MNGT 582<\/a> \u2013 Capstone<\/td>\nSpring II<\/td>\n<\/tr>\n
 <\/td>\n <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

* MNGT 502 is strongly recommended to be your first course.<\/p>\n

COMP 544 and DSCI 531 are a sequence and must be taken in this order.<\/p>\n

MNGT 582 must be taken near the end of the program.<\/p>\n

The remaining courses can be taken in any order.<\/p>\n

The suggested sequence of courses above represents a part-time schedule.  Contact Dr. Crute via phone at (724) 458-2027 or email at GraduatePrograms@GCC.edu for a full-time schedule.<\/p>\n

Online Master’s in Business Analytics – Course Descriptions:<\/h4>\n

————————–<\/p>\n

 <\/h5>\n

MNGT 502 Statistical Models:<\/strong><\/em> <\/a>Statistical Models such as hypothesis testing (one sample, two samples, and categorical), Analysis of Variance (ANOVA), nonparametric methods, study design, and analysis techniques for statistical studies related to individual student specializations including public health, business, engineering, epidemiologic studies, etc. Pre-requisites: Graduate standing or permission.<\/p>\n

 <\/h5>\n

MNGT 512 Forecasting Models:<\/strong><\/em><\/a> An introduction to creating, solving, analyzing, and interpreting real-world timeseries and forecasting models. Topics include linear, autoregressive, moving average and other forecasting and time-series techniques, transfer functions, multivariate model building, stationary, and nonstationary techniques. Applications include all areas where forecasting is required including transportation, finance, scheduling, networks, and supply chains. Appropriate software tools for analyzing forecasting models. Prerequisites: MNGT 502.<\/p>\n

 <\/h5>\n

MNGT 514 Machine Learning (Current Topics in Business Analytics):<\/strong><\/em><\/a> This course will explore current topics in business analytics as appropriate for the period of time. Research articles useful for currency in the field will be studied. Machine learning using Python. Prerequisite: Graduate standing.<\/p>\n

 <\/h5>\n

MNGT 542 Ethically Architecting Information:<\/strong><\/em> <\/a>Practical guidance on how to implement information management. This course explores the fundamental elements of ethics and provides practical methods for organizations to embed ethical principles and practices into the management and governance of the organization\u2019s information. Will explore the business case for ethical business practices. Prerequisites: Graduate standing or permission of instructor.<\/p>\n

 <\/h5>\n

MNGT 552 Case Studies in Business:<\/strong><\/em><\/a> Business analytics will be explored in a variety of business scenarios using a case study approach. Case studies will be require the appropriate data and analysis to advise senior leadership on solutions to complex business problems. Prerequisites: Graduate standing<\/p>\n

 <\/h5>\n

MNGT 550 Visualizing and Presenting Data:<\/strong> <\/em><\/a>Introduction to the key concepts and technologies for graphing and other visual ways to present data. This course covers modern techniques and software used to understand and explain data quickly through visual presentation. Prerequisites: SYSE 502 or MNGT 502.<\/p>\n

 <\/h5>\n

MNGT 582 Capstone:<\/strong><\/em><\/a> Capstone allows a student to explore a research topic of interest. Students may select research via an exhaustive literature review and analysis of seminal work in the topic. Students choosing this path are expected to either present at a conference or publish their work. Alternately, students may work on a major business project for a business of their choice and deliver the final project to the business via a presentation and hand-off to the business process owner. This course may be taken up to three times. Prerequisites: Graduate standing and permission<\/p>\n

 <\/h5>\n

SYSE 503 Linear Optimization Methods:<\/strong><\/em><\/a> The use of mathematics to describe and analyze large-scale decision problems. Allocation of resources, making decisions in a competitive environment, and dealing with uncertainty are modeled and solved using suitable software packages. Topics include solving linear programming problems via the Simplex Method (including sensitivity analysis), integer programming, transportation problems, and other important Optimization models. Prerequisites: Graduate standing, or ENGR 274 or equivalent and permission of instructor.<\/p>\n

 <\/h5>\n

COMP 544 Database Management Systems:<\/strong><\/em><\/a> A graduate level course in database management systems emphasizing the relational model. Topics include data manipulation languages (SQL, QBE); database design (intuitive design, normalization, and E-R design model); three-tier and multi-tier architecture; database security; and database integrity. Prerequisites: Graduate standing, or COMP 220 and permission of instructor.<\/p>\n

 <\/h5>\n

DSCI 531 Introduction to Big Data:<\/strong> <\/em><\/a>The objective of this course is to introduce key concepts and technologies of big data management. This course covers big data characteristics, storage, and processing. Students learn how to use multiple big data technologies, such as stream processing, in-memory databases, Hadoop MapReduce, NoSQL, and NewSQL systems. Prerequisites: COMP 544.<\/p>\n

 <\/p>\n

Learn More. Apply Now.<\/a><\/h3>\n

Earn your master\u2019s degree in Business Analytics.<\/a><\/h3>\n

 <\/p>\n

—–<\/p>\n

Contact Me!<\/h3>\n

Complete this form<\/a> and we will contact you.<\/p>\n","protected":false},"excerpt":{"rendered":"

Apply Now! How to Apply Earn Your Master\u2019s Degree in Business Analytics.   Online Master’s in Business Analytics – Current Curriculum* Course Suggested Term MNGT 502 \u2013 Statistical Models Summer I     COMP 544 \u2013 Database Management Fall I SYSE 503 \u2013 Linear Optimization Fall I     DSCI 531 \u2013 Introduction to Big Data Spring I MNGT 512 […]<\/p>\n","protected":false},"author":136,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"sfsi_plus_gutenberg_text_before_share":"","sfsi_plus_gutenberg_show_text_before_share":"","sfsi_plus_gutenberg_icon_type":"","sfsi_plus_gutenberg_icon_alignemt":"","sfsi_plus_gutenburg_max_per_row":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"ngg_post_thumbnail":0,"jetpack_post_was_ever_published":false,"footnotes":""},"class_list":["post-1182","page","type-page","status-publish","hentry"],"yoast_head":"\r\nM.S. 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