### MSA Curriculum

The Master of Science in Analytics (MSA) is a novel curriculum aimed squarely at producing graduates with the multi-faceted skills needed to draw insights from complex data sets, and to be able to communicate those insights effectively. It is the product of a 3-year collaboration by an interdisciplinary group including mathematicians, computer scientists, statisticians, economists, geographers, operations researchers, and faculty with expertise in various fields of business and management.

The MSA is a single, fully-integrated course of study—not a menu of core and elective courses—taught exclusively to students in the program. It is highly interactive. Students work together in teams and receive personalized coaching to improve their productivity. It is an intensive 10-month learning experience designed to immerse students into the acquisition of practical knowledge and application of methods and techniques. The curriculum is carefully calibrated and continuously updated to meet the evolving challenges facing data scientists. The Institute houses classrooms, team rooms, study spaces, and other amenities under one roof, as well as the faculty and staff who are available to interact with students throughout the day.

MSA students hone their skills working on challenging problems with actual data shared from sponsoring organizations. The Practicum spans eight months and culminates with an executive-level report and presentation to the sponsor. Students work with leading industry-standard programming tools. Since the program’s inception, MSA students have engaged in a total 180 projects with 120 sponsors spanning virtually every industry segment.

With a decade of experience and hundreds of graduates, the curriculum has a proven track record in producing superior student outcomes.

**SUMMER SEMESTER**

Pre-Program Primer |
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Data Collection Probability Distributions Sampling Distributions Confidence Intervals Hypothesis Testing Correlation |
Simple Linear Regression Multiple Linear Regression Linear Algebra ANOVA Categorical Data Analysis Clustering Introduction |
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AA500 – Analytics Tools and Techniques |
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Orientation SAS Programming/Viya Data Cleaning Problem Solving Visualization Techniques Presentation Skills Programming Concepts |
Computer & Data Security MBTI Teamwork Report Writing Strength Finders Communications Training |
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**FALL SEMESTER**

AA501 – Analytics Foundations |
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Distributions Confidence Intervals Hypothesis Testing Exploratory Data Analysis Correlation Linear Regression Linear Regression Assumptions Polynomial Regression Multicollinearity |
Regression Inference Model Building Residual Analysis Outliers & Influential Points Heteroscedasticity Corrections ANOVA Post-hoc Testing Interactions Categorical Analysis |
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AA502 – Analytics Methods and Applications I |
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Linear Algebra Linear Combinations Linear Independence Principal Component Analysis Factor Analysis Least Squares Eigenvectors / Eigenvalues Variable Reduction Singular Value Decomposition PC Regression Data Mining Association Analysis (Lift, Confidence, Support) Sequence Analysis Decision Trees Clustering Discriminant Analysis Missing Value Imputation Regression Trees Machine Learning Neural Network Models Clustering Random Forests Ensemble Modeling kNN Models Regularized Regression Gradient Boosting Text Analytics Term Vectors Similarity Clustering Text Sentiment Analysis Natural Language Processing Visualization of Text Logistic Regression Contingency Table Analysis Logistic Regression Odds Ratios Sensitivity, Specificity ROC Curves Ordinal, Nominal Logistic Simulation and Risk Risk Analysis Simulation Scenario Analysis Value at Risk / Expected Shortfall Copulas Monte Carlo Simulation |
Time Series and Forecasting Time Series Decomposition Exponential Smoothing Models Correlation Functions Stationarity ARIMA Modeling Seasonal Models Intervention Models ARIMAX and Transfer Functions Neural Network Models Weighted & Combined Models Time Series Clustering Clustering Distance measures for clustering K-Means Clustering Canonical Discriminant Analysis Mixture Modeling Cluster Visualization Introduction to AB testing Interpretation Advanced Programming SQL Querying Joins Subqueries Set Operators Creating tables and views Advanced Proc SQL Features R Data Analysis Functionality and R-Studio Writing/Reading External Data Sets Graphics Loops & Functions Python Variables Operators Data Types Conditionals Files Functions NumPy pandas Web crawling SAS Macros Visualizations SAS VA Tableau Big Data AWS Pig Hive Google Maps Survival Analysis Survival Curves Hazard Probabilities Censoring Accelerated Failure Time Models Cox Regression Models Competing Risks |
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**SPRING SEMESTER**

AA504 – Analytics Practicum I |
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Project phases 1 – 3: Understanding Business Objectives and Problem Framing Data Wrangling Exploratory Analysis Team Building Basic Project Management Leadership Followership Conflict Management Negotiation Skills Presentation Skills Process Mapping & Problem Solving Consulting Skills Project Management for Analytics |
Data Security Data Privacy Data Ethics Problem Solving Practicum Administrative Overview Professional Development Skills Business and Networking Etiquette Professional Social Media Biographical Profiles and Self-Branding Resume Writing Job Search Tips Effective Interviewing Case Study Overview Case Study Preparation |
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AA503 – Analytics Methods and Applications II |
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Big Data Open Source Ecosystem Real-time Data Ingestion Apache Hive – Distributed SQL Spark Machine Learning Deploying in the Cloud Design of Experiments Randomization Treatments / Factors Factorial Designs and Blocking Blocking Planned & Multiple Comparisons Design Types Power Reporting in clear language Optimization Simplex Algorithm Linear Programming Sensitivity Analysis Integer & Mixed Integer Programming Network Models Nonlinear Optimization |
Deep Learning Object Recognition Deep Learning Models Advanced Topics Multidimensional Scaling Markov Chains Option Pricing Fraud Detection Financial Analytics Scorecard Analysis CAPM models Portfolio Optimization Exponentially weighted MA for Bs ARCH/GARCH models Digital Analytics Web technologies Analytics infrastructure Test strategy, design, and analysis Algorithms in web technology Customer Life Time Value Models Social Networking Analysis Graph Theory Basics Social Network Data Graph/Network Clustering Centrality Scores Exponential Random Graph Models |
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AA505 – Analytics Practicum II |
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Project phases 4 – 7: Data Modeling Results Report and Presentations |
Project Management Teamwork Skills Visual Communication of Data Presentation Skills Technical Writing |
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