Graduate Certificate in Business Analytics

Graduates of the Business Analytics Certificate program will be able to:

• Extract valuable, actionable insights from data to influence strategy and decision-making
• Master statistical software tools and pursue highly-valued vendor certifications
• Implement business intelligence solutions using data warehousing and data mining
• Analyze data for customer acquisition, retention, revenue management, profitability, and other business goals
• Understand and apply principles of information ethics and privacy protection
• Compete in the marketplace with confidence and integrity

The Graduate Business Analytics Certificate Program requires that current Cleveland State University business and non-business graduate students complete at least one course in statistics at the undergraduate level prior to application. Working professionals with a basic knowledge of statistics and an undergraduate degree may also apply.

• Submission and evaluation of official transcripts will be required to determine if any prerequisite courses are needed.
• Admission to the certificate program will be determined by the Monte Ahuja College of Business.
• Graduate students must be in good standing with a minimum 3.0 GPA.

Submit application materials to Graduate Admissions processing at Campus 411, All-In-One, Berkman Hall 116.

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The faculty of the Monte Ahuja College of Business are a diverse group of world-class professors with industry-leading expertise in their disciplines and related business experience. Nearly every faculty member holds a terminal degree in their field, many from the world's leading business schools. Faculty are actively involved in the community as business consultants, members of professional organizations, and civic leaders, bringing practical application to every course they teach.

[3 credit(s)]

Prerequisite: Completion of undergraduate level statistics or its equivalent is required. Permission is required to register for this course. Information has come to be recognized as a critical resource, and business analytics tools play an increasingly critical role in deploying this resource in organizing and structuring information so that it can be used more productively. The ability to manage ‘Big Data’ has become a critical capability for organizations. This course discusses business analytics tools and their application to management problems. Topics discussed include: the need for business analytics in today’s dynamic business environments, data warehousing strategies, technologies, designs, and architectures, data mining techniques and algorithms (e.g., clustering, classification, predictive modeling, decision trees, neural networks, and visualization). Sample applications of these technologies and techniques will be discussed.

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[3 credit(s)]

Prerequisite: BUS 575. Business analytics provides solutions for the needs of finance, marketing, management, operations, research and development, and many other functional areas of the business enterprise. This course provides a comprehensive foundation of the statistical models and methods needed for applied business analytics. The software tools used in this course include but are not limited to SAS Enterprise Miner, SAS Enterprise guide, SAS Visual Analytics, and Tableau. The case study method is applied throughout the course for hands-on problem solving to develop quantitative skills to interpret and results/outcomes.

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[3 credit(s)]

Prerequisite: BUS 575. This course explores the use of databases for Business Analytics. The course is designed to provide individuals with a complete introduction to database concepts and the relational database model. Topics include principles of database systems, database design, database schemas, and database manipulation using SQL. The course introduces students to using relational databases for data mining and statistical analysis.

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[3 credit(s)]

Prerequisite: BUS 575. This course integrates business analytics models and methods with SAS software tools to examine data from the functional business disciplines of accounting, finance, management, operations, marketing and information technology. The course is organized using an applied, problem solving format and is focused on prescriptive analytics and includes data mining, text mining, clustering and use of statistical software. Business analytics for social media is introduced using SAS text Miner. The case study method is emphasized throughout the course. Each student will participate in a team-based, case study practicum project, in which the teams will use business analytics and statistical decision-making skills to develop strategic solutions to address real world business and policy challenges.

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[3 credit(s)]

Prerequisite: OSM 503 or equivalent. Intended for students with no previous course work in forecasting. Includes predictions of sales and inventory; examination of criteria for selection of forecasting models, including stage-in-life-cycle of the product; study of smoothing and decomposition methods, leading indicators, multiple regression, and introduction to ARIMA modeling through the use of computer packages.

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[3 credit(s)]

Prerequisite: Completion of the MIS preparatory program or permission from the department. This course introduces the basic concepts of business analytics, data warehousing, and data mining. Topics discussed include: the need for business analytics in today’s dynamic business environments, data warehousing strategies, technologies, designs, and architectures (e.g., star schemas), data mining techniques and algorithms (e.g., clustering, classification, predictive modeling, decision trees, neural networks, and visualization). Sample applications of these technologies and techniques will be discussed. Textbook will be supplemented with current articles on data mining technology and applications.

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[3 credit(s)]

Prerequisite: None. This course introduces data science analytics tools and techniques for geographic data (e.g., maps, geosensors, satellite and GPS location data). A comprehensive coverage of the issues with handling space-time data from the perspective of DBMS, GIS, Data Analytics, and Big Data Systems. Students will learn spatial database concepts, algorithm, statistical tools to model and analyze problems by hands-on approach using programming languages. Cover spatial and temporal data representation, exploratory analysis, and prediction models. Also, introduces software to perform geographic query, analysis, visualization and custom application development for decision support. Emphasizes issues related to spatial data mining, integration of machine learning techniques in spatio-temporal analysis and prediction.

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[4 credit(s)]

Pre-requisite: PSY 511. Simultaneous, sequential, and hierarchical multiple regression and other advanced statistical topics are considered. Transforming non-linear data and detecting multicollinearity are discussed. Students analyze data using statistical software and interpret results. (Credit may not be earned in both Psychology 597 and Psychology 611).

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[3 credit(s)]

Prerequisites: ECN 610 and ECN 622 or equivalents. This course will cover topics on time-series regression analysis including (i) stationary time-series models for forecasting: ARMA models; (ii) modelling volatility; (iii) nonstationarity and tests for nonstationarity due to unit roots and structural breaks; (iv) Vector Autoregressions (VAR) and structural VAR; (v) cointegration and vector error correction models (VECM); and (vi) Other selected topics on Dynamic Causal Effects and Bayesian estimation methods. Cross-listed with ECN 725. Candidates for the M.A. in Economics should register for ECN 625.

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[4 credit(s)]

Prerequisites: CIS 530. Must be admitted to the College of Engineering as a degree-seeking graduate student to be eligible for this course. This course will examine data mining methods, technologies, techniques and algorithms. The course will also cover data quality issues, data reduction, data preparation, data pre-processing, model creation, model selection, and model evaluation. Sample data sets will be used to illustrate the course concepts by hands-on experimentation with data mining algorithms implementations and/or by using existing data mining software.

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[4 credit(s)]

Prerequisite: STA 567 or departmental approval. The course will cover techniques of modeling data that are collected sequentially. Topics to be covered include a review of basic ideas of modeling a continuous variable, time series regression, autocorrelation, decomposition methods, ARMA (Autoregressive Moving Average) models, and ARIMA (Autoregressive Integrated Moving Average) models. The course will use a statistical programming language. The course will also require the completion of a time series analysis project. Data from a variety of fields will be studied. Credit cannot be earned for this course if a student has already taken STA 421.

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[4 credit(s)]

Prerequisite: STA 536 or STA 567 or departmental approval. The course will cover techniques of modeling data for data that are categorical rather than continuous in nature. Topics to be covered include joint, marginal, and conditional probabilities, relative risk, odds ratios, generalized linear models, logistic regression, multi-category logit models, and loglinear models. The course will utilize data examples from the fields of biology, medicine, health, epidemiology, environmental science, and psychology. The course will use a statistical programming language. The course will also require the completion of a categorical data analysis project. Credit cannot be earned for this course if a student has already taken STA 431.

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[4 credit(s)]

Linear programming, including the simplex method, sensitivity analysis, duality, and integer programming. Additional topics were selected from LU decomposition, dual simplex algorithm, game theory, Karmarkar’s algorithm, as well as topics from nonlinear programming, such as steepest descent and Kuhn-Tucker conditions. Part one of a two-part sequence.

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