### 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 and Data Security MBTI Teamwork Report Writing Strength Finders Communications Training |
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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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**FALL SEMESTER**

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 Logistic Regression– Binary Logistic Regression – Odds and Probability Ratios – Maximum Likelihood Estimation – Convergence Problems – Sensitivity, Specificity, Precision, Recall – ROC Curves, K-S Statistics – Classification Selection (Youden, Profit) – Ordinal Logistic Regression – Multinomial Logistic Regression Survival Analysis– Survival Curves – Hazard Probabilities – Censoring – Accelerated Failure Time Models – Failure Time Distributions – Cox Regression Models – Model Diagnostics – Time Varying Covariates / Coefficients – Competing Risks – Repeating Events |
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 and Combined Models – UCM – TBATS Text Analytics– Term Vectors – Similarity – Clustering Text – Sentiment Analysis – Natural Language Processing – Visualization of Text Advanced Programming> SQL –– Querying –– Joins –– Subqueries –– Set Operators –– Creating tables and views –– Advanced Proc SQL Features > R Programming –– Data Analysis –– Functionality and R-Studio –– Writing/Reading External Data Sets –– Graphics –– Loops and Functions > Python –– Variables –– Operators –– Data Types –– Conditionals –– Files –– Functions –– NumPy –– pandas –– Web crawling > Visualizations –– SAS VA –– Tableau > Big Data –– AWS –– Pig –– Hive > SAS Macros |
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AA504 – Analytics Practicum I |
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Project phases 1 – 3– Understanding Business Objectives and Problem Framing – Data Wrangling – Exploratory Analysis Professional Development Skills– Practicum Administrative Overview – Team Building – Emotional Intelligence – Project Management – Project Management for Analytics – Practicum Data Security/Confidentiality – Self-Branding |
– Leadership/Followership – Presentation Skills – Resume Writing – Process Mapping and Problem Solving – Conflict Management – Consulting Skills – Business and Networking Etiquette – Reporting – Job Search Tips – Case Study Overview – Effective Interviewing |
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**SPRING SEMESTER**

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 and Mixed Integer Programming – Network Models – Nonlinear Optimization Bayesian– Prior versus posterior distributions – STAN – MCMC – Convergence of MCMC Fraud Detection– Characteristics of Fraud and Fraud Data – Outlier Detection – MD, Local Outlier Factor, Isolation Trees, CADE – SMOTE – Niave Bayes – NOT Fraud Models, LIME |
Deep Learning– Object Recognition – Deep Learning Models Simulation and Risk– Monte Carlo Simulations – Kernel Density Estimation – Target Shuffling – Risk Management – Scenario Analysis – Value at Risk – Expected Shortfall – Extreme Value Theorem Financial Analytics– Scorecard Analysis – Discrete vs. Continuous Time – Weight of Evidence Binning – Reject Inference – Model Selection – ARCH / GARCH Models – CAPM Factor Model – Portfolio Optimization 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 – Drawing Insights – Presentation and Reporting |
Professional Development Skills– Negotiation Skills – Problem Solving – Case Study Preparation – Data Ethics – Professsional Writing – Career Documents – Interview Practice – Report Writing |
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