Business Analytics Graduate Certificate


Program Description

Joint SAS Certificate ProgramThe certificate for Business Analytics addresses analytics and data mining from a business perspective. Recent advances in information technology have led to generation of humungous data, leading to 'data rich, information poor' (DRIP) scenario in different business sectors. Regardless of the industry (e.g., healthcare, retail, financial), business are generating an unprecedented amounts of data and are recognizing the strategic importance of leveraging such resources for sustained competitive advantage by effectively translating this data to information, knowledge, and ultimately improved decision making. The curriculum is designed to respond to distinct need by industry and government for professionals with the 'right' combination of analytics, computer and business skills.

Program Eligibility

The ideal candidates for this certificate program are IT professionals or business analysts (regardless of industry) who aim to augment their skill set by acquiring the technical and programming skills needed to manipulate and analyze large amounts of data to solve business problems. Other likely candidates are students with a strong quantitative background (statistics, mathematics, computer science, operations research) who are interested to apply their skills in an organizational context.

Program Admission

  1. A non-refundable application fee of $35, drawn on a U.S. bank, must accompany the form. The check should be made payable to Dakota State University. If the application fee is not included, the application will not be processed. The application fee cannot be waived or deferred and is non-refundable.
  2. Baccalaureate degree from an institution of higher education with full regional accreditation for that degree.
  3. Official transcripts should be mailed directly to the Graduate Office in a sealed envelope. The registrar's signature and the school seal must be across the sealed flap. Neither photocopies nor transcripts marked "student copy" are acceptable. If you have received your degree from a South Dakota Regental institution, you will not need to submit an official transcript from that university.
  4. Completed Graduate Certificate Application.


Program Delivery

All courses are delivered via distance thereby appealing to professionals regardless of location. Distance classes use a combination of both live and/or encoded streaming videos of classes, interactive course web boards, course web sites, and e-mail. All courses are web-enhanced. 

Program Requirements and Coursework

The Business Analytics certificate is a 12 credit hour graduate certificate. A graduate certificate will be awarded upon successful completion of the program.

  • INFS 762 Data Warehousing and Data Mining 3 cr. hrs.
  • INFS 768 Predictive Analytics for Decision Making 3 cr. hrs.
  • INFS 770 Advanced Data Mining Applications 3 cr. hrs.
  • INFS 764 Information Retrieval 3 cr. hrs. OR INFS 766 Advanced Database 3 cr. hrs.

Course Rotation

Courses SU 14 FA 14 SP 15 SU 15 FA 15 SP 16 SU 16 FA 16 SP 17
INFS 762 Data Warehousing & Data Mining X     X     X    
INFS 768 Predictive Analytics for Decision Making    X          X  
INFS 770 Advanced Data Mining Applications      X        
  AND                  
INFS 764 Information Retrieval   X     X     X  
 OR                   
INFS 766 Advanced Database     X     X     X

Course Descriptions

INFS 762 Data Warehousing and Data Mining 3 cr. hrs.

The main concepts, components, and various architectures of Data Warehouse. Advanced data analysis and optimization of Data Warehouse Design. Data Warehousing and OLAP tools. Applying data mining algorithms to retrieve highly specialized information or knowledge about the data stored in the Data Warehouse. Prerequisites: INFS 605 and INFS 760.

INFS 764 Information Retrieval 3 cr. hrs.

Provides hands-on experience with procedural extensions to the SQL language for retrieval and manipulation of data. Topics include data control languages, control structures, looping and branching, local and global variables, exception handling, stored procedures and database triggers, cursors and cursor processing. Prerequisites: INFS 605 and INFS 760. 

INFS 766 Advanced Database 3 cr. hrs.

This course is designed to give the student a strong foundation in the theoretical underpinnings of current database systems. Emphasis will be placed on database theory and will cover such issues as distributed databases, concurrency control, security, optimization, and specialized data models. It will also explore emerging database methodologies and their impact on current practices. Prerequisite: INFS 760.

INFS 768 Predictive Analytics for Decision Making 3 cr. hrs.

This course provides a broad understanding of the role of predictive analytics for decision-making in different application domains. Students will be exposed to a number of predictive analytics techniques originated in related fields of statistics, machine learning, and artificial intelligence. Techniques covered will include statistical techniques such as linear and logistic regression, classification techniques such as decision trees and neural networks, association analysis techniques such as market basket analysis, and cluster analysis techniques such as K-means clustering. Applications of each of the techniques for decision-making applications will be emphasized. Utilization of predictive analytics software is incorporated.

INFS 770 Advanced Data Mining Applications 3 cr. hrs.

This course provides an understanding of data mining methodology as well as hands-on experience with applying the methodology in data mining applications. Throughout the course, students will work closely with data analysis following the data mining methodology. Different aspects of data mining such as data import, data partitioning, variable transformation, model building, and model comparison will be covered. Students will participate in one or more major data mining projects in the course. Through formal presentations students will gain experience in delivering the findings to an audience in an effective manner. Utilization of predictive analytics software is incorporated.
Prerequisite: INFS 768.

 

 
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Last Updated: 4/9/14