Business Analytics Certificate Program
The Business Analytics Certificate Program provides working professionals with the knowledge and skills needed for contemporary business analytics.
Business Analytics is the combination of skills, technologies, applications and processes used by organizations to gain data-driven insights. These insights can be used to aid decision-making across functions including finance, marketing, and operations.
Who should apply?
This certificate is geared towards entry to middle level management and technology professionals.
Program Structure and Delivery
The program will be delivered in a web based blended format with live instruction.
- Typical program duration is 12 months
- The program consists of four courses, taught sequentially
- Duration of each course is 12 weeks
- No in-residence required
Certificate Credits - 12 Credit Hours
- The BA certificate will be issued by Indiana University
- Up to 12 credits could be transferred to the KSB Executive MBA or an MS in Business Analytics (subject to admission requirements).
All four required courses are three-graduate credit hours and twelve weeks in duration
1. Introduction to Business Analytics
- Define and explain the business analytics process (problem definition; data preparation; technical analysis and modeling; evaluation of results; implementation and deployment).
- Understand and describe the functionality and role of analytic techniques in data mining and predictive analytics.
- Perform basic and advanced analytics tasks with JMP and Excel.
- Construct, validate, and interpret data mining and predictive analytics models using large multivariate data sets.
- Apply data mining and predictive analytic techniques to problems in areas such as fund raising, retailing, direct marketing, market segmentation, bankruptcy prediction, credit scoring, and fraud detection.
- Perform data exploration to evaluate variables for data mining and to suggest and implement approaches to handle data problems such as missing values, outliers, and skewed distributions.
- Compute and interpret key predictive accuracy measures and methods including lift charts, gain charts, and ROC curves.
2. Data Warehousing & Visualization
- Understand and utilize unsupervised models including principal components analysis, cluster analysis, and market basket analysis. Understand business intelligence-related concepts
- The notion of corporate information factory.
- Dashboards and scorecards that support the businesses.
- Different types of problems related to data quality including a methodology for maintaining data quality.
- An overview of variety of software tools that are employed in the development of a data warehouse: ETL (extraction, transformation and loading) and analytic tools.
- Recognize some of the key issues for managerial considerations
- The tools of metrics in decision making
- Types of business risks along with how to alleviate the risks associated with implementing informational systems
- Issues related to data governance
- Be cognizant of a variety of tools and techniques
3. Simulation and Optimization for Business Analytics
- Develop analytical models using simulation and optimization to analyze and recommend sound solutions to complex business problems
- Develop models to provide solutions to operational problems in various business functional areas including finance, economics, operations, and marketing
- Solve complex problems using various tools on spreadsheets; including Excel solver for linear and integer programming problems, @RISK for probabilistic simulations, and risk analysis
- Undertake input and output statistical analysis for simulation models
- Solve complex optimization problems using the ILOG-CPLEX package
4. Developing Value through Business Analytics Applications
- Understand how various analytical techniques and tools are used to analyze complex business problems and derive business value for applications
- Apply analytical techniques to various retail and marketing problems; including determining customer value and using the concept to aid in business decision making by allocating marketing expenditures between customer acquisition and customer retentions (Marketing Analytics)
- Apply EXCEL financial functions (XIRR, XNPV, FV, PV, PMT, CUMPRINC, and CUMIPMT) and develop models to solve financial problems on spreadsheets (Financial Analytics)
- Develop inventory models under uncertainty including service level and reorder point models for supply chains (Supply Chain Analytics)
- Deploy analytics, such as aggregate planning models in production planning and scheduling (Operations Analytics)
- Use Data Envelopment Analysis (DEA) in solving various healthcare problems (Healthcare Analytics)