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
Data Collection Probability Distributions Sampling Distributions Confidence Intervals Hypothesis Testing	Correlation Simple Linear Regression Multiple Linear Regression Linear Algebra ANOVA Categorical Data Analysis

AA500 – Analytics Tools and Techniques
Orientation R Programming SAS Programming Data Wrangling Problem Solving Visualization Techniques Storytelling with Data Technical Writing Data Ethics Career Writing: Profile, LinkedIn Social media writing	Communication Training Giving / Receiving Feedback Intercultural communication Networking Emotional Intelligence Programming Concepts Computer & Data Security MBTI Teamwork Professional Reflection

AA501 – Analytics Foundations
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 Logistic Regression

FALL SEMESTER

AA502 – Analytics Methods and Applications I
Linear Algebra – Linear Independence – Eigenvectors / Eigenvalues – Least Squares – Principal Component Analysis – Factor Analysis – Singular Value Decomposition – Principal Component Regression – Variable Reduction Data Mining – Principles of Modeling – Association Analysis – Sequence Analysis – Classification and Regression Trees – Clustering – Model Evaluation Machine Learning – Random Forests – Gradient Boosting – Regularized Regression – Generalized Additive Models – kNN Models – GA2M – Neural Networks – Model Agnostic Interpretability 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 Regressionn 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 Visualization – Excel – SAS Viya VA – Tableau	Time Series and Forecasting – Time Series Decomposition – Exponential Smoothing Models – Correlation Functions – Stationarity – ARIMA Modeling – Trend/Seasonal Models – Intervention Models – ARIMAX & Transfer Functions – Neural Network Models – UCM – BSTS – Automatic search algorithms – Weighted & Combined Models Text Analytics – Text Representation – Term Representation – Stop Word Removal + Stemming – Term Vectors – Term-Document Matrices – TF-IDF, Cosine Similarity – Concept Similarity (LSA, LDA) – Document Pairwise Similarity – Topic Clustering – Sentiment Advanced Programming – Python – Variables – Operators – Data Types – Conditionals – Files – Functions – numpy – pandas – scikit learn – web scraping Advanced Programming – SQL – Querying – Displaying Query Results – Joins – Subqueries – Set Operators – Creating tables and views Cloud Computing – Advantages of Cloud Computing – Amazon Web Services (AWS) – AWS S3, AWS EC2, AWS RDS – AWS Athena – Open Source Ecosystem – Hadoop (HDFS & MapReduce) – Apache Spark – AWS EMR (Elastic Map Reduce) – Apache Hive – Apache Spark Machine Learning – Apache Spark SQL

AA504 – Analytics Practicum I
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 – Process Mapping and Problem Solving – Conflict Management – Consulting Skills – Business and Networking Etiquette – Job Search Tips – Case Study Overview – Effective Interviewing Technical Communication – Presentation Skills – Storytelling – Resume Writing – Report Writing

SPRING SEMESTER

AA503 – Analytics Methods and Applications II
Big Data – Distributed Services and Platform Overview – SQL and NoSQL Services – Apache Phoenix – Realtime, Streaming Systems – Apache Spark Machine Learning – Serverless Technology 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 Natural Lanaguage Processing – N-gram language modeling – Word Embeddings – Cross-Entropy Loss – RNNs – LSTMs – CNNs for NLP – Batch-processing & padding – BERT & contextual embeddings	Deep Learning – Intro to Deep Learning – Fully connected networks – Convolutional neural networks – Digit recognition – Object recognition – PyTorch – Basics of CNNs – CIFAR FCN Python examples 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 – Graphs and Networks – Network Data Visualization – Community Detection/Clustering – Measures of Centrality – Hypothesis Testing on Networks

AA505 – Analytics Practicum II
Project phases 4 – 7: – Data Modeling – Drawing Insights – Presentation and Reporting Technical Communication – Report Writing – Career Documents – Presenting and Storytelling Techniques	Professional Development Skills – Negotiation Skills – Problem Solving – Case Study Preparation – Data Ethics – Interview Practice

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