6.1 Understand the applications of prescriptive analytics techniques in combination with reporting and predictive analytics 6.2 Understand the basic concepts of analytical decision modeling 6.3 Understand the concepts of analytical models for selected decision problems, including linear programming and simulation models for decision support 6.4 Describe how spreadsheets can be used for analytical modeling and solutions 6.5 Explain the basic concepts of optimization and when to use them 6.6 Describe how to structure a linear programming model 6.7 Explain what is meant by sensitivity analysis, what-if analysis, and goal seeking 6.8 Understand the concepts and applications of different types of simulation 6.9 Understand potential applications of discrete event simulation
5.1 Describe text mining and understand the need for text mining 5.2 Differentiate among text analytics, text mining, and data mining 5.3 Understand the different application areas for text mining 5.4 Know the process of carrying out a text mining project 5.5 Appreciate the different methods to introduce structure to text-based data 5.6 Describe sentiment analysis 5.7 Develop familiarity with popular applications of sentiment analysis 5.8 Learn the common methods for sentiment analysis 5.9 Become familiar with speech analytics as it relates to sentiment analysis
4.1 Define data mining as an enabling technology for business analytics 4.2 Understand the objectives and benefits of data mining 4.3 Become familiar with the wide range of applications of data mining 4.4 Learn the standardized data mining processes 4.5 Learn different methods and algorithms of data mining 4.6 Build awareness of the existing data mining software tools 4.7 Understand the privacy issues, pitfalls, and myths of data mining
