Master Assessment Plan: Analytics

Transcription

1 Master Assessment Plan: Analytics Outcomes Analysis Years: Biennial Report Year/Semester: 2015/Spring Program(s): All programs Objective: Data mining and machine learning Graduates should be able to effectively use the SAS Enterprise Miner interface. Graduates should be able to recognize and develop association and sequence analyses. Graduates should be able to explain the role of statistical tests in forming decision trees. Graduates should be able to fit decision trees to binary data and interpret the results. Graduates should be able to fit regression trees. Graduates should be able to explain discriminant analysis. Graduates should be able to recognize and interpret regression for binary responses. Graduates should be able to divide data using clustering techniques. Graduates should be able to account for oversampling, profits, and losses. Graduates should be able to identify and use techniques for choosing the best model from many. Graduates should be able to build models using variable selection techniques. Graduates should be able to recognize and build neural networks. Objective: Data visualization understanding, the main goals of data visualization. Graduates should be able to recognize the main types of charts and plots (line, bar, pie, scatter, etc.). Graduates should be able to Select the type of chart or plot best suited to a particular type of data and visualization goal. Graduates should be able to recognize the main types of maps (choropleth, contour, dot, dasymetric, etc.). Graduates should be able to select the type of map best suited to a particular type of data and visualization goal. Objective: Text Analytics understanding the main goals of text analytics and text mining. Graduates should be able to recall the strengths and limitations of different methods of systematically representing text and understand how to apply these methods to a text corpus. Graduates should be able to identify different approaches to computing text similarity, and how to use these measures to organize text based on similarity clustering. Graduates should be able to recognize and describe different models of emotion or sentiment. Graduates should be able to employ different approaches to using emotional models to estimate and represent sentiment contained in a text corpus. Objective: Python Graduates should be able to demonstrate a basic understanding of computer programming with a common procedural programming language. Graduates should be able to design, implement, and test small programs written in Python. Graduates should be able to perform basic analytic operations with Python using common external libraries (nltk, numpy, pandas, etc.). Objective: Customer segmentation and positioning Graduates should be able to recognize real world applications of segmentation theory. Graduates should be able to employ different techniques and methods for segmenting various types of data using different statistical software (SAS, SPSS, R). Graduates should be able to apply different techniques for variable reduction including principal components, common factor analysis, etc. Graduates should be able to solve a segmentation problem using statistical software (SAS, SPSS, R) and real world data. Objective: Design of experiments Graduates should be able to explain the concept of designing experiments and its applications beyond laboratories in a business setting. Graduates should be able to control and vary parameters to get the desired outcome in direct marketing experiments. Graduates should be able to use SAS and JMP software and sample industry data to demonstrate understanding of techniques used in designing and analyzing experiments. Objective: Marketing mix and web analytics Graduates should be able to identify different marketing mix models and market basket models for use in business settings. Graduates should be able to develop a pricing model using customer data to demonstrate understanding of marketing mix and market basket models. Graduates should be able to apply market based models to provide recommendations to the customer for product, promotion and pricing changes to its offerings.

2 Graduates should be able to recognize web data reporting tools (e.g., Google Analytics), ad and campaign testing tools (e.g., optimizely), visualization tools for big data, and strategies for integrating web and off line data. Graduates should be able to complete Google Analytics certification to demonstrate understanding of web analytics basics and applications. Objective: Logistic regression Graduates should be able to identify the key differences between logistic regression and linear regression. Graduates should be able to distinguish between nominal and ordinal variables and the different statistical tests between them. Graduates should be able to build logistic regression models (in all their forms binary, ordinal, nominal) using the statistical Graduates should be able to interpret the output from logistic regression models (in all their forms binary, ordinal, nominal) using the statistical Graduates should be able to interpret the meaning of odds ratios. Objective: Times series and forecasting Graduates should be able to decompose a time series into its three basic components trend, seasonality, and remainder. Graduates should be able to distinguish between the three different correlation functions ACF, PACF, and IACF. Graduates should be able to explain the difference between a stationary and non stationary time series. Graduates should be able to build the different classes of time series models (Exponential Smoothing, ARIMA, and Neural Network) using the statistical Graduates should be able to interpret the output of different classes of time series models (Exponential Smoothing, ARIMA, and Neural Network) using the statistical Graduates should be able to diagnose different classes of time series models for accuracy and reliability. Graduates should be able to forecast different types of time series models. Graduates should be able to cluster different time series into hierarchical clusters using one of the three common techniques bottom up, top down, middle out. Objective: Survival analysis Graduates should be able to build survival curves using both common techniques Kaplan Meier and Life Table. Graduates should be able to interpret the survival and hazard probability of a data series. Graduates should be able to design a data set that contains both censored and uncensored observations. Graduates should be able to build the different classes of survival analysis models (Accelerated Failure Time, Cox Regression). Graduates should be able to identify the proper distributional assumption in an Accelerated Failure Time model. Graduates should be able to identify cases where competing risks are occurring. Objective: Exploratory data analytics/fraud detection Graduates should be able to build and analyze a social network data set. Graduates should be able to identify subgroups, centers, closeness, brokers, bridges, diffusion, and adoption in a social network. Graduates should be able to transform transactional data into a usable format for typical forms of analysis. Graduates should be able to identify common characteristics of fraud in the insurance industry. Objective: Optimization Graduates should be able to apply integer and mixed integer optimization techniques to identify the product mix and transportation schedules that maximize profitability while satisfying demand constraints, capacity constraints and transportation cost caps. Graduates should be able to perform Data Envelopment Analysis to identify the most efficient unit from a set of many candidates who produce different final products using different inputs mix. Graduates should be able to identify the optimal stock portfolio allocation that minimizes risk and at the same time achieves a target return. Graduates should be able to use nonlinear optimization techniques (Gauss, Gauss Newton, Newton Raphson) to fit the best non linear model in a set of data. Objective: Simulation and risk analysis Graduates should be able to verify the properties of statistical models by simulating their behavior and identify the impact of violating key modeling assumptions. Graduates should be able to use the expected value approach to calculate the expected net present value and potential losses for an investment that runs across multiple periods. Graduates should be able to utilize the Kolmogorov Smirnov, Anderson Darling and other non parametric statistics to identify and fit the appropriate distribution of different real datasets (e.g. oil prices across time, daily oil production, lease costs). Graduates should be able to perform scenario and sensitivity analysis to identify the most sensitive decision/control variables in a project. Graduates should be able to use simulation techniques to calculate the expected Net Present Value, Value at Risk and Expected Shortfall of a project that extends across many years; assess the risks and provide recommendations for their reduction. Graduates should be able to use simulation to value real options, such as "option to abandon project"; identify the optimal option's price and suggest

3 alternative option contracts that minimize the risk of shareholders. Objective: Financial Analytics Graduates should be able to evaluate the performance of a portfolio through the usage of single factor models and build the optimal (risk vs return) portfolio allocation CAPM, portfolio's alpha, portfolio's beta. Graduates should be able to analyze portfolio risk and return from the standpoint of a risk manager, utilizing a time varying beta estimation (EWMA). Graduates should be able to analyze the time varying volatility of a portfolio through the usage of ARCH and GARCH (symmetric and asymmetric) models. Graduates should be able to build credit scorecards to rank different credit applicant; utilize clustering, decision trees and logistic regression models. Graduates should be able to identify the optimal cutoff point for a scorecard in a way that maximizes the profitability of a credit institution. Graduates should be able to identify the optimal cutoff point for a scorecard in a way that maximizes the approved credit customers while keeping default rates the same. Objective: Linear algebra Graduates should be able to manipulate and simplify matrix equations using the properties of matrix multiplication, addition, inversion, transposition, symmetry. Graduates should be able to compute and interpret common vector norms and similarity metrics. Graduates should be able to solve systems of equations using the methods of Gaussian Elimination and Least Squares. Graduates should be able to define (mathematically) and describe (geometrically) the notions of linear independence, vector span, vector spaces, basis vectors, eigenvalues, eigenvectors and projections. Graduates should be able to use software to find eigenvalues and eigenvectors of a matrix. Graduates should be able to apply Principal Components Analysis to data for clustering, variable clustering, dimension reduction, and biased regression. Graduates should be able to determine when Biased Regression is appropriate. Graduates should be able to apply Biased Regression techniques such as Principal Component Regression to solve problems of severe multicollinearity. Graduates should be able to find dominant topics/themes in text data using Nonnegative Matrix Factorization and the Singular Value Decomposition. Objective: Project management Graduates should be able to demonstrate project management planning and execution by developing and maintaining a work breakdown structure identifying all sub tasks required to plan and complete a project form start to finish, assign and track individual accountability for each subtask, schedule and track work progress as it occurs, and estimate completion of future work based on previous rate of progress. Graduates should be able to demonstrate project management competency by successfully performing scope feasibility analysis on original project scope and subsequent scope development/refinement. Objective: Teamwork/problem solving/conflict resolution in team based settings. in resolving conflict associated with creative differences in approaching analytic problems/projects and with workload distribution and management. Graduates should be able to demonstrate leadership skills in planning and articulating a vision for homework team projects, organizing workload assignments, controlling for performance variation amongst teammates, and motivating teammates to succeed. Graduates should be able to demonstrate followership in supporting and enabling team leads by contributing effectively to the work plan, vision development and organization, subordinating/aligning individual goals with those of the team, executing assignments, and motivating teammates and team leader. Graduates should be able to demonstrate problem solving competencies by being able to effectively resolve project issues focused on understanding underlying datasets to be analyzed, understanding context of the underlying business problem, articulating refining bounding assumptions and project problem statements, and then addressing scope feasibility and development. Objective: Communication skills Graduates should be able to prepare documents for use in business settings that are direct, concise, professional, easily skimmable, and grammatically correct; e.g. apply conventions and strategies to business s, design and write appropriate memos, create an audience centered executive summary, create an audience centered, persuasive resume. Graduates should be able to apply strategies for editing and proofreading that demonstrate an understanding of grammar. Graduates should be able to identify, understand, and use the components of an effective presentation. Graduates should be able to assess the audience for a presentation and use a variety of modes to present. Graduates should be able to develop strategies for preparing and structuring presentations to communicate, motivate, and/or persuade listeners. Graduates should be able to use techniques for connecting with an audience, including voice projection, pacing, body language, eye contact, gestures, humor, personality and other performance skills. Graduates should be able to use media and visual aids effectively in presentations. Graduates should be able to develop strategies to handle fear of public speaking, minimize distracting mannerisms, e.g. verbal pauses, and project confidence. Graduates should be able to handle audience questions. Program(s): Analytics Objective: To meet enrollment and admissions targets

4 Meet student enrollment target, which is set at 100 percent of operating capacity for the program (currently 80 students/year) Enrollment statistics DGP Annually Meet application pool target of 4:1 (applicants to available seats), or better Application statistics DGP Annually Meet acceptance rate target of below 30 percent Enrollment statistics DGP Annually Meet enrollment rate target of greater than 80 percent Enrollment statistics DGP Annually Meet student attrition target of below 10 percent Enrollment statistics DGP Annually Meet admissions target for average undergraduate GPA (UGPA) of 3.50 or greater Meet admission rank index target of less than 75 percent (ratio of the average undergraduate institution rank to NCSU rank a ratio below 100 percent is better) Maintain an admissions profile (acceptance rate, enrollment rate, and UGPA) that is equivalent to, or better than comparably sized MBA programs at public universities ranked in the Top 10 Objective: To achieve an excellent placement of graduates and a return on investment for the analytics degree equivalent to, or better than comparably sized MBA programs at public universities ranked in the Top 10. equivalent to, or better than comparably sized, 1 year quant based MS programs at four benchmark schools (Berkeley, MIT, Cornell and Carnegie Mellon Attain a job placement rate of 90 percent or higher by graduation Attain a average ROI payback period of less than 36 months ROI statistics DGP Annually Attain an average ROI payback period that is better (shorter) than comparably sized MBA programs at public universities ranked in the Top 10 ROI statistics DGP Annually Attain a ratio of job offers per candidate of 2.0 or greater Job offer statistics DGP Annually Attain a ratio of job interviews per candidate of 10.0 or greater Job interview data DGP Annually Objective: To maintain the program as effective, efficient, and competitive with similar programs Maintain the program as a self contained and sustainable business model (i.e. operate within the budgetary cost constraints provided by tuition revenue generated for the program) Maintain resident and non resident tuition at or below the average for other similar MS degree programs Budget statistics DGP Annually Tuition comparisons DGP Annually Outcomes Analysis Years: Biennial Report Year/Semester: 2016/Spring Program(s): All programs Objective: Data mining and machine learning Graduates should be able to effectively use the SAS Enterprise Miner interface. Graduates should be able to recognize and develop association and sequence analyses. Graduates should be able to explain the role of statistical tests in forming decision trees. Graduates should be able to fit decision trees to binary data and interpret the results. Graduates should be able to fit regression trees. Graduates should be able to explain discriminant analysis. Graduates should be able to recognize and interpret regression for binary responses. Graduates should be able to divide data using clustering techniques. Graduates should be able to account for oversampling, profits, and losses. Graduates should be able to identify and use techniques for choosing the best model from many. Graduates should be able to build models using variable selection techniques. Graduates should be able to recognize and build neural networks. Objective: Data visualization understanding, the main goals of data visualization. Graduates should be able to recognize the main types of charts and plots (line, bar, pie, scatter, etc.). Graduates should be able to Select the type of chart or plot best suited to a particular type of data and visualization goal. Graduates should be able to recognize the main types of maps (choropleth, contour, dot, dasymetric, etc.). Graduates should be able to select the type of map best suited to a particular type of data and visualization goal. Objective: Text Analytics understanding the main goals of text analytics and text mining. Graduates should be able to recall the strengths and limitations of different methods of systematically representing text and understand how to apply these methods to a text corpus. Graduates should be able to identify different approaches to computing text similarity, and how to use these measures to organize text based on similarity clustering. Graduates should be able to recognize and describe different models of emotion or sentiment. Graduates should be able to employ different approaches to using emotional models to estimate and represent sentiment contained in a text corpus. Objective: Python Graduates should be able to demonstrate a basic understanding of computer

5 programming with a common procedural programming language. Graduates should be able to design, implement, and test small programs written in Python. Graduates should be able to perform basic analytic operations with Python using common external libraries (nltk, numpy, pandas, etc.). Objective: Customer segmentation and positioning Graduates should be able to recognize real world applications of segmentation theory. Graduates should be able to employ different techniques and methods for segmenting various types of data using different statistical software (SAS, SPSS, R). Graduates should be able to apply different techniques for variable reduction including principal components, common factor analysis, etc. Graduates should be able to solve a segmentation problem using statistical software (SAS, SPSS, R) and real world data. Objective: Design of experiments Graduates should be able to explain the concept of designing experiments and its applications beyond laboratories in a business setting. Graduates should be able to control and vary parameters to get the desired outcome in direct marketing experiments. Graduates should be able to use SAS and JMP software and sample industry data to demonstrate understanding of techniques used in designing and analyzing experiments. Objective: Marketing mix and web analytics Graduates should be able to identify different marketing mix models and market basket models for use in business settings. Graduates should be able to develop a pricing model using customer data to demonstrate understanding of marketing mix and market basket models. Graduates should be able to apply market based models to provide recommendations to the customer for product, promotion and pricing changes to its offerings. Graduates should be able to recognize web data reporting tools (e.g., Google Analytics), ad and campaign testing tools (e.g., optimizely), visualization tools for big data, and strategies for integrating web and off line data. Graduates should be able to complete Google Analytics certification to demonstrate understanding of web analytics basics and applications. Objective: Logistic regression Graduates should be able to identify the key differences between logistic regression and linear regression. Graduates should be able to distinguish between nominal and ordinal variables and the different statistical tests between them. Graduates should be able to build logistic regression models (in all their forms binary, ordinal, nominal) using the statistical Graduates should be able to interpret the output from logistic regression models (in all their forms binary, ordinal, nominal) using the statistical Graduates should be able to interpret the meaning of odds ratios. Objective: Times series and forecasting Graduates should be able to decompose a time series into its three basic components trend, seasonality, and remainder. Graduates should be able to distinguish between the three different correlation functions ACF, PACF, and IACF. Graduates should be able to explain the difference between a stationary and non stationary time series. Graduates should be able to build the different classes of time series models (Exponential Smoothing, ARIMA, and Neural Network) using the statistical Graduates should be able to interpret the output of different classes of time series models (Exponential Smoothing, ARIMA, and Neural Network) using the statistical Graduates should be able to diagnose different classes of time series models for accuracy and reliability. Graduates should be able to forecast different types of time series models. Graduates should be able to cluster different time series into hierarchical clusters using one of the three common techniques bottom up, top down, middle out. Objective: Survival analysis Graduates should be able to build survival curves using both common techniques Kaplan Meier and Life Table. Graduates should be able to interpret the survival and hazard probability of a data series. Graduates should be able to design a data set that contains both censored and uncensored observations. Graduates should be able to build the different classes of survival analysis models (Accelerated Failure Time, Cox Regression). Graduates should be able to identify the proper distributional assumption in an Accelerated Failure Time model. Graduates should be able to identify cases where competing risks are occurring. Objective: Exploratory data analytics/fraud detection Graduates should be able to build and analyze a social network data set. Graduates should be able to identify subgroups, centers, closeness, brokers, bridges, diffusion, and adoption in a social network. Graduates should be able to transform transactional data into a usable format

6 for typical forms of analysis. Graduates should be able to identify common characteristics of fraud in the insurance industry. Objective: Optimization Graduates should be able to apply integer and mixed integer optimization techniques to identify the product mix and transportation schedules that maximize profitability while satisfying demand constraints, capacity constraints and transportation cost caps. Graduates should be able to perform Data Envelopment Analysis to identify the most efficient unit from a set of many candidates who produce different final products using different inputs mix. Graduates should be able to identify the optimal stock portfolio allocation that minimizes risk and at the same time achieves a target return. Graduates should be able to use nonlinear optimization techniques (Gauss, Gauss Newton, Newton Raphson) to fit the best non linear model in a set of data. Objective: Simulation and risk analysis Graduates should be able to verify the properties of statistical models by simulating their behavior and identify the impact of violating key modeling assumptions. Graduates should be able to use the expected value approach to calculate the expected net present value and potential losses for an investment that runs across multiple periods. Graduates should be able to utilize the Kolmogorov Smirnov, Anderson Darling and other non parametric statistics to identify and fit the appropriate distribution of different real datasets (e.g. oil prices across time, daily oil production, lease costs). Graduates should be able to perform scenario and sensitivity analysis to identify the most sensitive decision/control variables in a project. Graduates should be able to use simulation techniques to calculate the expected Net Present Value, Value at Risk and Expected Shortfall of a project that extends across many years; assess the risks and provide recommendations for their reduction. Graduates should be able to use simulation to value real options, such as "option to abandon project"; identify the optimal option's price and suggest alternative option contracts that minimize the risk of shareholders. Objective: Financial Analytics Graduates should be able to evaluate the performance of a portfolio through the usage of single factor models and build the optimal (risk vs return) portfolio allocation CAPM, portfolio's alpha, portfolio's beta. Graduates should be able to analyze portfolio risk and return from the standpoint of a risk manager, utilizing a time varying beta estimation (EWMA). Graduates should be able to analyze the time varying volatility of a portfolio through the usage of ARCH and GARCH (symmetric and asymmetric) models. Graduates should be able to build credit scorecards to rank different credit applicant; utilize clustering, decision trees and logistic regression models. Graduates should be able to identify the optimal cutoff point for a scorecard in a way that maximizes the profitability of a credit institution. Graduates should be able to identify the optimal cutoff point for a scorecard in a way that maximizes the approved credit customers while keeping default rates the same. Objective: Linear algebra Graduates should be able to manipulate and simplify matrix equations using the properties of matrix multiplication, addition, inversion, transposition, symmetry. Graduates should be able to compute and interpret common vector norms and similarity metrics. Graduates should be able to solve systems of equations using the methods of Gaussian Elimination and Least Squares. Graduates should be able to define (mathematically) and describe (geometrically) the notions of linear independence, vector span, vector spaces, basis vectors, eigenvalues, eigenvectors and projections. Graduates should be able to use software to find eigenvalues and eigenvectors of a matrix. Graduates should be able to apply Principal Components Analysis to data for clustering, variable clustering, dimension reduction, and biased regression. Graduates should be able to determine when Biased Regression is appropriate. Graduates should be able to apply Biased Regression techniques such as Principal Component Regression to solve problems of severe multicollinearity. Graduates should be able to find dominant topics/themes in text data using Nonnegative Matrix Factorization and the Singular Value Decomposition. Objective: Project management Graduates should be able to demonstrate project management planning and execution by developing and maintaining a work breakdown structure identifying all sub tasks required to plan and complete a project form start to finish, assign and track individual accountability for each subtask, schedule and track work progress as it occurs, and estimate completion of future work based on previous rate of progress. Graduates should be able to demonstrate project management competency by successfully performing scope feasibility analysis on original project scope and subsequent scope development/refinement. Objective: Teamwork/problem solving/conflict resolution in team based settings. in resolving conflict associated with creative differences in approaching analytic problems/projects and with workload distribution and management. Graduates should be able to demonstrate leadership skills in planning and articulating a vision for homework team projects, organizing workload

7 assignments, controlling for performance variation amongst teammates, and motivating teammates to succeed. Graduates should be able to demonstrate followership in supporting and enabling team leads by contributing effectively to the work plan, vision development and organization, subordinating/aligning individual goals with those of the team, executing assignments, and motivating teammates and team leader. Graduates should be able to demonstrate problem solving competencies by being able to effectively resolve project issues focused on understanding underlying datasets to be analyzed, understanding context of the underlying business problem, articulating refining bounding assumptions and project problem statements, and then addressing scope feasibility and development. Objective: Communication skills Graduates should be able to prepare documents for use in business settings that are direct, concise, professional, easily skimmable, and grammatically correct; e.g. apply conventions and strategies to business s, design and write appropriate memos, create an audience centered executive summary, create an audience centered, persuasive resume. Graduates should be able to apply strategies for editing and proofreading that demonstrate an understanding of grammar. Graduates should be able to identify, understand, and use the components of an effective presentation. Graduates should be able to assess the audience for a presentation and use a variety of modes to present. Graduates should be able to develop strategies for preparing and structuring presentations to communicate, motivate, and/or persuade listeners. Graduates should be able to use techniques for connecting with an audience, including voice projection, pacing, body language, eye contact, gestures, humor, personality and other performance skills. Graduates should be able to use media and visual aids effectively in presentations. Graduates should be able to develop strategies to handle fear of public speaking, minimize distracting mannerisms, e.g. verbal pauses, and project confidence. Graduates should be able to handle audience questions. Program(s): Analytics Objective: To meet enrollment and admissions targets Meet student enrollment target, which is set at 100 percent of operating capacity for the program (currently 80 students/year) Enrollment statistics DGP Annually Meet application pool target of 4:1 (applicants to available seats), or better Application statistics DGP Annually Meet acceptance rate target of below 30 percent Enrollment statistics DGP Annually Meet enrollment rate target of greater than 80 percent Enrollment statistics DGP Annually Meet student attrition target of below 10 percent Enrollment statistics DGP Annually Meet admissions target for average undergraduate GPA (UGPA) of 3.50 or greater Meet admission rank index target of less than 75 percent (ratio of the average undergraduate institution rank to NCSU rank a ratio below 100 percent is better) Maintain an admissions profile (acceptance rate, enrollment rate, and UGPA) that is equivalent to, or better than comparably sized MBA programs at public universities ranked in the Top 10 Objective: To achieve an excellent placement of graduates and a return on investment for the analytics degree equivalent to, or better than comparably sized MBA programs at public universities ranked in the Top 10. equivalent to, or better than comparably sized, 1 year quant based MS programs at four benchmark schools (Berkeley, MIT, Cornell and Carnegie Mellon Attain a job placement rate of 90 percent or higher by graduation Attain a average ROI payback period of less than 36 months ROI statistics DGP Annually Attain an average ROI payback period that is better (shorter) than comparably sized MBA programs at public universities ranked in the Top 10 ROI statistics DGP Annually Attain a ratio of job offers per candidate of 2.0 or greater Job offer statistics DGP Annually Attain a ratio of job interviews per candidate of 10.0 or greater Job interview data DGP Annually Objective: To maintain the program as effective, efficient, and competitive with similar programs Maintain the program as a self contained and sustainable business model (i.e. operate within the budgetary cost constraints provided by tuition revenue generated for the program) Maintain resident and non resident tuition at or below the average for other similar MS degree programs Budget statistics DGP Annually Tuition comparisons DGP Annually Outcomes Analysis Years: Biennial Report Year/Semester: 2017/Spring Program(s): All programs Objective: Data mining and machine learning Graduates should be able to effectively use the SAS Enterprise Miner interface. Graduates should be able to recognize and develop association and sequence analyses. Graduates should be able to explain the role of statistical tests in forming decision trees. Graduates should be able to fit decision trees to binary data and interpret the results. Graduates should be able to fit regression trees. Graduates should be able to explain discriminant analysis. Graduates should be able to recognize and interpret regression for binary responses. Graduates should be able to divide data using clustering techniques. Graduates should be able to account for oversampling, profits, and losses.

8 Graduates should be able to identify and use techniques for choosing the best model from many. Graduates should be able to build models using variable selection techniques. Graduates should be able to recognize and build neural networks. Objective: Data visualization understanding, the main goals of data visualization. Graduates should be able to recognize the main types of charts and plots (line, bar, pie, scatter, etc.). Graduates should be able to Select the type of chart or plot best suited to a particular type of data and visualization goal. Graduates should be able to recognize the main types of maps (choropleth, contour, dot, dasymetric, etc.). Graduates should be able to select the type of map best suited to a particular type of data and visualization goal. Objective: Text Analytics understanding the main goals of text analytics and text mining. Graduates should be able to recall the strengths and limitations of different methods of systematically representing text and understand how to apply these methods to a text corpus. Graduates should be able to identify different approaches to computing text similarity, and how to use these measures to organize text based on similarity clustering. Graduates should be able to recognize and describe different models of emotion or sentiment. Graduates should be able to employ different approaches to using emotional models to estimate and represent sentiment contained in a text corpus. Objective: Python Graduates should be able to demonstrate a basic understanding of computer programming with a common procedural programming language. Graduates should be able to design, implement, and test small programs written in Python. Graduates should be able to perform basic analytic operations with Python using common external libraries (nltk, numpy, pandas, etc.). Objective: Customer segmentation and positioning Graduates should be able to recognize real world applications of segmentation theory. Graduates should be able to employ different techniques and methods for segmenting various types of data using different statistical software (SAS, SPSS, R). Graduates should be able to apply different techniques for variable reduction including principal components, common factor analysis, etc. Graduates should be able to solve a segmentation problem using statistical software (SAS, SPSS, R) and real world data. Objective: Design of experiments Graduates should be able to explain the concept of designing experiments and its applications beyond laboratories in a business setting. Graduates should be able to control and vary parameters to get the desired outcome in direct marketing experiments. Graduates should be able to use SAS and JMP software and sample industry data to demonstrate understanding of techniques used in designing and analyzing experiments. Objective: Marketing mix and web analytics Graduates should be able to identify different marketing mix models and market basket models for use in business settings. Graduates should be able to develop a pricing model using customer data to demonstrate understanding of marketing mix and market basket models. Graduates should be able to apply market based models to provide recommendations to the customer for product, promotion and pricing changes to its offerings. Graduates should be able to recognize web data reporting tools (e.g., Google Analytics), ad and campaign testing tools (e.g., optimizely), visualization tools for big data, and strategies for integrating web and off line data. Graduates should be able to complete Google Analytics certification to demonstrate understanding of web analytics basics and applications. Objective: Logistic regression Graduates should be able to identify the key differences between logistic regression and linear regression. Graduates should be able to distinguish between nominal and ordinal variables and the different statistical tests between them. Graduates should be able to build logistic regression models (in all their forms binary, ordinal, nominal) using the statistical Graduates should be able to interpret the output from logistic regression models (in all their forms binary, ordinal, nominal) using the statistical Graduates should be able to interpret the meaning of odds ratios. Objective: Times series and forecasting Graduates should be able to decompose a time series into its three basic components trend, seasonality, and remainder. Graduates should be able to distinguish between the three different

9 correlation functions ACF, PACF, and IACF. Graduates should be able to explain the difference between a stationary and non stationary time series. Graduates should be able to build the different classes of time series models (Exponential Smoothing, ARIMA, and Neural Network) using the statistical Graduates should be able to interpret the output of different classes of time series models (Exponential Smoothing, ARIMA, and Neural Network) using the statistical Graduates should be able to diagnose different classes of time series models for accuracy and reliability. Graduates should be able to forecast different types of time series models. Graduates should be able to cluster different time series into hierarchical clusters using one of the three common techniques bottom up, top down, middle out. Objective: Survival analysis Graduates should be able to build survival curves using both common techniques Kaplan Meier and Life Table. Graduates should be able to interpret the survival and hazard probability of a data series. Graduates should be able to design a data set that contains both censored and uncensored observations. Graduates should be able to build the different classes of survival analysis models (Accelerated Failure Time, Cox Regression). Graduates should be able to identify the proper distributional assumption in an Accelerated Failure Time model. Graduates should be able to identify cases where competing risks are occurring. Objective: Exploratory data analytics/fraud detection Graduates should be able to build and analyze a social network data set. Graduates should be able to identify subgroups, centers, closeness, brokers, bridges, diffusion, and adoption in a social network. Graduates should be able to transform transactional data into a usable format for typical forms of analysis. Graduates should be able to identify common characteristics of fraud in the insurance industry. Objective: Optimization Graduates should be able to apply integer and mixed integer optimization techniques to identify the product mix and transportation schedules that maximize profitability while satisfying demand constraints, capacity constraints and transportation cost caps. Graduates should be able to perform Data Envelopment Analysis to identify the most efficient unit from a set of many candidates who produce different final products using different inputs mix. Graduates should be able to identify the optimal stock portfolio allocation that minimizes risk and at the same time achieves a target return. Graduates should be able to use nonlinear optimization techniques (Gauss, Gauss Newton, Newton Raphson) to fit the best non linear model in a set of data. Objective: Simulation and risk analysis Graduates should be able to verify the properties of statistical models by simulating their behavior and identify the impact of violating key modeling assumptions. Graduates should be able to use the expected value approach to calculate the expected net present value and potential losses for an investment that runs across multiple periods. Graduates should be able to utilize the Kolmogorov Smirnov, Anderson Darling and other non parametric statistics to identify and fit the appropriate distribution of different real datasets (e.g. oil prices across time, daily oil production, lease costs). Graduates should be able to perform scenario and sensitivity analysis to identify the most sensitive decision/control variables in a project. Graduates should be able to use simulation techniques to calculate the expected Net Present Value, Value at Risk and Expected Shortfall of a project that extends across many years; assess the risks and provide recommendations for their reduction. Graduates should be able to use simulation to value real options, such as "option to abandon project"; identify the optimal option's price and suggest alternative option contracts that minimize the risk of shareholders. Objective: Financial Analytics Graduates should be able to evaluate the performance of a portfolio through the usage of single factor models and build the optimal (risk vs return) portfolio allocation CAPM, portfolio's alpha, portfolio's beta. Graduates should be able to analyze portfolio risk and return from the standpoint of a risk manager, utilizing a time varying beta estimation (EWMA). Graduates should be able to analyze the time varying volatility of a portfolio through the usage of ARCH and GARCH (symmetric and asymmetric) models. Graduates should be able to build credit scorecards to rank different credit applicant; utilize clustering, decision trees and logistic regression models. Graduates should be able to identify the optimal cutoff point for a scorecard in a way that maximizes the profitability of a credit institution. Graduates should be able to identify the optimal cutoff point for a scorecard in a way that maximizes the approved credit customers while keeping default rates the same. Objective: Linear algebra Graduates should be able to manipulate and simplify matrix equations using the properties of matrix multiplication, addition, inversion, transposition, symmetry.

10 Graduates should be able to compute and interpret common vector norms and similarity metrics. Graduates should be able to solve systems of equations using the methods of Gaussian Elimination and Least Squares. Graduates should be able to define (mathematically) and describe (geometrically) the notions of linear independence, vector span, vector spaces, basis vectors, eigenvalues, eigenvectors and projections. Graduates should be able to use software to find eigenvalues and eigenvectors of a matrix. Graduates should be able to apply Principal Components Analysis to data for clustering, variable clustering, dimension reduction, and biased regression. Graduates should be able to determine when Biased Regression is appropriate. Graduates should be able to apply Biased Regression techniques such as Principal Component Regression to solve problems of severe multicollinearity. Graduates should be able to find dominant topics/themes in text data using Nonnegative Matrix Factorization and the Singular Value Decomposition. Objective: Project management Graduates should be able to demonstrate project management planning and execution by developing and maintaining a work breakdown structure identifying all sub tasks required to plan and complete a project form start to finish, assign and track individual accountability for each subtask, schedule and track work progress as it occurs, and estimate completion of future work based on previous rate of progress. Graduates should be able to demonstrate project management competency by successfully performing scope feasibility analysis on original project scope and subsequent scope development/refinement. Objective: Teamwork/problem solving/conflict resolution in team based settings. in resolving conflict associated with creative differences in approaching analytic problems/projects and with workload distribution and management. Graduates should be able to demonstrate leadership skills in planning and articulating a vision for homework team projects, organizing workload assignments, controlling for performance variation amongst teammates, and motivating teammates to succeed. Graduates should be able to demonstrate followership in supporting and enabling team leads by contributing effectively to the work plan, vision development and organization, subordinating/aligning individual goals with those of the team, executing assignments, and motivating teammates and team leader. Graduates should be able to demonstrate problem solving competencies by being able to effectively resolve project issues focused on understanding underlying datasets to be analyzed, understanding context of the underlying business problem, articulating refining bounding assumptions and project problem statements, and then addressing scope feasibility and development. Objective: Communication skills Graduates should be able to prepare documents for use in business settings that are direct, concise, professional, easily skimmable, and grammatically correct; e.g. apply conventions and strategies to business s, design and write appropriate memos, create an audience centered executive summary, create an audience centered, persuasive resume. Graduates should be able to apply strategies for editing and proofreading that demonstrate an understanding of grammar. Graduates should be able to identify, understand, and use the components of an effective presentation. Graduates should be able to assess the audience for a presentation and use a variety of modes to present. Graduates should be able to develop strategies for preparing and structuring presentations to communicate, motivate, and/or persuade listeners. Graduates should be able to use techniques for connecting with an audience, including voice projection, pacing, body language, eye contact, gestures, humor, personality and other performance skills. Graduates should be able to use media and visual aids effectively in presentations. Graduates should be able to develop strategies to handle fear of public speaking, minimize distracting mannerisms, e.g. verbal pauses, and project confidence. Graduates should be able to handle audience questions. Program(s): Analytics Objective: To meet enrollment and admissions targets Meet student enrollment target, which is set at 100 percent of operating capacity for the program (currently 80 students/year) Enrollment statistics DGP Annually Meet application pool target of 4:1 (applicants to available seats), or better Application statistics DGP Annually Meet acceptance rate target of below 30 percent Enrollment statistics DGP Annually Meet enrollment rate target of greater than 80 percent Enrollment statistics DGP Annually Meet student attrition target of below 10 percent Enrollment statistics DGP Annually Meet admissions target for average undergraduate GPA (UGPA) of 3.50 or greater Meet admission rank index target of less than 75 percent (ratio of the average undergraduate institution rank to NCSU rank a ratio below 100 percent is better) Maintain an admissions profile (acceptance rate, enrollment rate, and UGPA) that is equivalent to, or better than comparably sized MBA programs at public universities ranked in the Top 10 Objective: To achieve an excellent placement of graduates and a return on investment for the analytics degree equivalent to, or better than comparably sized MBA programs at public universities ranked in the Top 10. equivalent to, or better than comparably sized, 1 year quant based MS programs at four benchmark schools (Berkeley, MIT, Cornell and Carnegie Mellon