Program Description   |   Curriculum   |   Course Descriptions
Admission Requirements   |   Application Items
Gainful Employment Disclosure   |   Graduate Degrees

Graduate Certificate Program

Business Analytics and Data Science
Department of Business and Information Technology

Program Description
Data analytics facilitates realization of objectives by identifying trends, creating predictive models for forecasting, and optimizing business processes for enhanced performance. Three main categories of analytics are:

  • Descriptive - the use of data to find out what happened in the past.
  • Predictive - the use of data to find out what could happen in the future.
  • Prescriptive - the use of data to prescribe the best course of action for the future.

Big data is an emerging phenomenon. Computing systems today are generating 15 petabytes of new information every day- eight times more than the combined information in all the libraries in the U.S.; about 80% of the data generated every day is textual and unstructured data. This graduate certificate is one of three graduate certificates offered by cooperating departments at Missouri S&T to fulfill the needs in the area described as "big data." The other two graduate certificates are:

  • Big Data and Security
  • Big Data Management and Analytics

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The big data analytics and data science certificate program consists of four courses. Students will be responsible for prerequisite knowledge as determined by course instructors and listed in the Graduate Catalog. With the approval of the department, appropriate courses may be substituted for a certificate course if that course in not available.

The following courses are required:

  • IST 5420: Introduction to Big Data Analytics - taught spring semester
  • IST 6450: Information Visualization - taught fall semester

Choose one of the following as an elective course:

  • ERP 5410: Use of Business Intelligence - taught fall semester
  • Comp Sci 6304: Cloud Computing and Big Data Management - taught fall and spring semesters
  • Comp Sci 5402: Data Mining and Machine Learning - taught fall and spring semesters
  • Comp Eng 6330: Clustering Algorithms (Co-listed with Elec Eng 6340, Sys Eng 6214, Comp Sci 6405, Stat 6239 - taught spring semester
  • STAT 5814: Applied Time Series Analysis - taught spring semester
  • IST 5001: Data Methodologies Using Python
  • Comp Sci 5204: Regression Analysis

Choose one of the following as an elective course:

  • IST 6443: Information Retrieval and Analysis - taught fall semester
  • IST 6444: Essentials of Data Warehouses - taught spring semester
  • IST 6445: Database Marketing - taught spring semester
  • IST 6448: Building the Data Warehouse
  • ERP 5210: Performance Dashboard, Scorecard and Data Visualization - taught spring semester
  • ERP 6610: Customer Relationship Management in ERP Environment - taught fall semester
  • ERP 6220: Enterprise Performance Dashboard Prototyping - taught fall semester
  • BUS 6425: Supply Chain and Project Management - taught spring semester
  • IST 6887: Research Methods in Business and IS&T (co-listed with BUS 6887)

Note: There is overlap between the course offerings for this graduate certificate and other big data graduate certificates. No course can be used to satisfy the requirements for more than one certificate.

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Course Descriptions

IST 5420: Introduction to Big Data Analytics (on campus and online)
This course addresses the foundations of using predictive statistics on big data sets to impact decision-making. Focus is applied examples using realistic data. Models implemented include regression (parametric/nonparametric), classification, decision trees, and clustering with analytical estimation accomplished using popular software. Prerequisite: Calculus and statistics knowledge

IST 6001: Information Visualization and Analytics (on campus and online)
Develops models of modern information systems using combinatorial constructs to analyze and visualize the underlying structure and related growth dynamics. Potential information models include the massive graph structure of the World Wide Web, clustering in social media, random graph models pf web dynamics, and information flow across random networks. Prerequisites: Statistics and calculus knowledge.

ERP 5410: Use of Business Intelligence (on campus and online)
This course introduces data-oriented techniques for business intelligence. Topics include business intelligence architecture, business analytics and enterprise reporting. SAP business information warehouse, business objects, or similar tools will be used to access and present data, generate reports, and perform analysis. Prerequisite: IST 3423 or equivalent; ERP 2110 or preceded or accompanied by ERP 5110.

Comp Sci 6304: Cloud Computing and Big Data Management (on campus and online)
Covers facets of cloud computing and big data management, including the study of the architecture of the cloud computing model with respect to virtualization, multi-tenancy, privacy, security, cloud data management and indexing, scheduling and cost analysis; it also includes programming models such as Hadoop and MapReduce, crowdsourcing, and data provenance. Prerequisites: A “C” or better grade in both Comp Sci 5800 and either 5300 or Comp Sci 5001- Introduction to Data Mining

Comp Sci 5402: Data Mining and Machine Learning (on campus and online)
Data mining and knowledge discovery utilizes both classical and new algorithms, such as machine learning and neural networks, to discover previously unknown relationships in data. The topics covered will include data preprocessing, mining association rules, classification and prediction methods, and clustering techniques. Prerequisites: Comp Sci 2300 and one of Stat 3113 or Stat 3115 or Stat 3117 or Stat 5643.

Comp Eng 6330: Clustering Algorithms (Co-listed with Elec Eng 6340, Sys Eng 6214, Comp Sci 6405, Stat 6239) (on campus and online)
An introduction to cluster analysis and clustering algorithms rooted in computational intelligence, computer science, and statistics. Topics included are clustering in sequential data, massive data, and high dimensional data. Students will be evaluated by individual or group research projects and research presentations. Prerequisite: At least one graduate course in statistics, data mining, algorithms, computational intelligence, or neural networks, consistent with student's degree program

STAT 5814: Applied Time Series Analysis (on campus and online)
Introduction to time series modeling of empirical data observed over time. Topics include stationary processes, auto covariance functions, moving average, autoregressive, ARIMA, and GARCH models, spectral analysis, confidence intervals, forecasting, and forecast error. Prerequisite: One of STAT 3113, 3115, 3117, 5643 and one of MATH 3103, 3108, or 5108.

IST 6443: Information Retrieval and Analysis (on campus and online)
Covers the applications and theoretical foundations of organizing and analyzing information of textual resources. Topics include information storage and retrieval systems, web search engines, text mining, collaborative filtering, recommender systems. Students will also learn the techniques with the use of interactive tools such as SAS. Prerequisite: ERP 5410 or statistics knowledge.

IST 6444: Essentials of Data Warehouses (Co-listed with ERP 6444) (on campus and online)
This course presents the topic of data warehouses and the value to the organization. It takes the student from the database platform to structuring a data warehouse environment. Focus is placed on simplicity and addressing the user community needs. Prerequisite: IST 3423 or equivalent relational database experience.

IST 6445: Database Marketing (on campus and online)
Intro to methods and concepts used in database marketing: 1) predictive modeling techniques (e.g., regression, decision trees, cluster analysis) and 2) standard processes for mapping business objectives to data mining goals to produce a deployable marketing model. Metrics like lifetime value of a customer and ROI will be covered. Several application areas covered. Prerequisite: Statistics understanding, programming understanding, familiarity with spreadsheets

IST 6448: Enterprise Performance Dashboard Prototyping (on campus and online)
Data modeling and processes needed to populate a data warehouse; tradeoffs among several models and tools; technical issues that are faced, such as security, schemas, Web access, other reporting techniques. Prerequisite: IST 6444

ERP 5210: Performance Dashboard, Scorecard and Data Visualization (on campus and online)
This course will study different performance management systems including dashboards, management cockpit, scorecards, and strategy maps in an organization. SAP's BW, Business Objects Xcelsius, Crystal Reports, Sybase Unwired Platform will be used to develop the applications. Prerequisite: ERP 2110 or preceded or accompanied by ERP 5110.

ERP 6610: Advanced Customer Relationship Management in an ERP Environment (on campus and online)
Identification (targeting), acquisition, retention, and development (expansion) of (profitable) customers. Topics included are effective and efficient management of customers, using IT. SAP CRM, SAS BI tools, and Sybase mobile application development are used. Research paper and presentation required. Prerequisite: ERP 2110 or preceded or accompanied by ERP 5110.

ERP 6220: Enterprise Performance Dashboard Prototyping (on campus and online)
Study of implementation and design practices for enterprise performance management systems with a focus on dashboards, balanced scorecard, and value-based management. SAP's BusinessObjects Ecelsius, Crystal Reports, BW, or similar tools will be used for project implementations. Prerequisites: ERP 5110; ERP 6444 or IST 6444.

BUS 6425: Supply Chain and Project Management (on campus and online)
This course covers supply chain management and its critical role in developing and maintaining effective and efficient processes in the organization, including operations and project management processes and principles. MBA core course. Prerequisite: Graduate standing.

IS&T 6887 Research Methods in Business and IS&T

This course covers quantitative and qualitative research methods for exploring the interaction between people and information technologies. The course covers techniques and tools for carrying out literature reviews, forming research goals, designing research, conducting data analyses; and preparing manuscripts and live presentations. (Co-listed with BUS 6887).



* Curriculum is subject to change. Please contact the department for up-to-date information on courses. Other courses approved by the department may be substituted for any of the above listed courses on a case-by-case basis. The administrative coordinators must approve the substitution prior to enrolling in the course.


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Admission Requirements

The graduate certificate program is open to all individuals holding a BS, MS, or PhD degree in areas such as business, social sciences, sciences, technology, engineering, or related disciplines. The certificate program consists of four courses. In order to receive a graduate certificate, the student must have an average graduate cumulative grade point of 3.0 or better on a 4.0 scale in the certificate courses taken. Students admitted only to the certificate program will have non-degree graduate status but will earn graduate credit for the courses they complete. The core courses will be offered at least once per year. The courses in the certificate program will be offered as such that students can complete the program in a timely manner. If the four-course sequence approved by the graduate advisor is completed with a grade of B or better in each of the courses taken, the student will, upon application, be admitted to the master of business administration or to the master of science in information science and technology. The certificate courses taken by students admitted to the program will count towards the MBA program or the MS in information science and technology degree program. Once admitted to the certificate program, a student will be given three years to complete the program as long as a B or better average is maintained in the courses taken. 


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Gainful Employment Disclosure

Gainful Employment Program Disclosure

Effective July 1, 2011, the Department of Education requires that all certificate programs must disclose particular Gainful Employment information to current and prospective students. The information that is provided in the disclosure includes the estimated cost of the certificate program as well as on ­time graduation and job placement rates for this particular certificate program. The disclosure information is based on data from the 2014-15 school year (defined as the period between July 1, 2014, and June 30, 2015).

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Application Items

Completed Application
$55 Application Fee
Statement of Purpose 

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Graduate Degrees

Graduate certificates were designed as a gateway to a master’s degree. If a student earns a B or better in each certificate course they may continue for the graduate degree (in the corresponding department), without needing to submit GRE/GMAT scores, or letters of recommendation. A student does not need to continue on for the graduate degree, however most do. Graduate certificates were designed for working professionals who have real life work experience and may not have time to take the GRE/GMAT. Admission requirements for the graduate certificate program are also more relaxed than the graduate degree. This graduate certificate may act as a gateway to the following master’s programs:

Business Administration (MBA)
Information Science and Technology (MS)

(Applicants must indicate which program they wish to use the certificate as a gateway for when initially applying for the certificate).

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