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.

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 Basic statistical programming Python coding Data wrangling Problem solving Data visualization Storytelling with data Presentation skills (in-person, virtual, hybrid) Technical writing	Data ethics Giving/receiving feedback Intercultural communication Personality types Emotional intelligence Computer & data security Team dynamics Project dynamics Professional practice and 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 and influential points Heteroscedasticity corrections ANOVA Post-hoc testing Interactions Categorical analysis Logistic regression

FALL SEMESTER

AA502 – Analytics Methods and Applications I
Data Mining – Issues in Data Manipulation – Association Analysis – Classification and Regression Trees – Clustering – kNN – MDS – Bootstrapping 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 Linear Algebra – Vectors – Matrices – Elimination – Vector Spaces – Factorization – Determinants – Eigen Decomposition – Singular Value Decomposition – Principal Component Analysis – Least Squares Ethical Considerations for Data Scientists – Ethical Frameworks – Data Ethics Challenges – Ethical Data Storytelling	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 – Named Entity Recognition (NER) 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 Simple Cloud Storage (S3) – Amazon EC2 – AWS – Virtual Server Hosting – Amazon Relational Database Service (RDS) Visualization – plotly Dash – R+ Shiny – SAS Viya VA – Tableau

AA504 – Analytics Practicum I
Project phases 1 – 3 – Business Objectives and Problem Framing – Data Wrangling – Exploratory Analysis Professional Development Skills – Business Objectives & Problem Framing – Team Building/Conflict Management – Emotional Intelligence – Business and Networking Etiquette – Project Management Basic – Project Management for Analytics/Process – Mapping & Problem Solving – Administrative Overview	– Data Security/Confidentiality – Ethical Data Storytelling – Presentation Skills – Survey of Leadership Theories – Followership – Presentation Skills – Career Writing: Branding, Resumes – Tech Writing: Homework Reports – Social media writing – Consulting Skills – Case Study Overview – Effective Interviewing – Social media writing

SPRING SEMESTER

AA503 – Analytics Methods and Applications II
Cloud Computing – AWS Athena – Hadoop (HDFS & MapReduce) – Apache Spark – AWS EMR (Elastic Map Reduce) – Apache Hive – Apache Spark Machine Learning – Apache Spark SQL Customer Analytics – Collecting & Analyzing Digital Data – Custom Dashboards in Digital Analytics – Data Driven Experimentation – Introduction to Design of Experiments – Designing an A/B Test – Power Analysis – Multifactorial Designs – Counterfactual Measurement – Attribution – Recommendation Engines Optimization – Simplex Algorithm – Linear Programming – Integer and Mixed Integer Programming – Network Models – Nonlinear Optimization Bayesian – Prior Inference – STAN – MCMC – Convergence 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 Language Processing – N-gram language modeling – Word Embeddings – Transformers & their application – 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

AA505 – Analytics Practicum II
Project phases 4 – 6: Modeling/Visualization – Data Modelling – Negotiation Skills – Problem Solving – Case Study Preparation – Ethical Data Storytelling – Tech Writing: Practicum Report – Career Writing: Cover Letters, Resumes – Social media writing – Storytelling with Data – Presentation Skills – Interview Practice – Interview Presentations	Deliverables Development – Drawing Insights – Tech Writing: Practicum Report – Career Writing: Cover letters – Interview Presentations – Social media writing – Bias Workshop – Ethical Data Storytelling – Presentation Skills Presentation and Reporting – Tech Writing: Practicum Report – Ethical Data Storytelling – Presentation Skills

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