Dr. Natalia Summerville

MSA Curriculum

Curriculum HighlightsThe 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.

Master of Science in Analytics
Heqing, MSA Class of 2020


Pre-Program Primer
Data Collection
Sampling Distributions
Confidence Intervals
Hypothesis Testing
Simple Linear Regression
Multiple Linear Regression
Linear Algebra
Categorical Data Analysis
Clustering Introduction
AA500 – Analytics Tools and Techniques
SAS Programming/Viya
Data Cleaning
Problem Solving
Visualization Techniques
Programming Concepts
Computer and Data Security
Strength Finders
Technical Writing
Presentation Skills
Ethical Data Storytelling
Communication Training
AA501 – Analytics Foundations
Confidence Intervals
Hypothesis Testing
Exploratory Data Analysis
Linear Regression
Linear Regression Assumptions
Polynomial Regression
Regression Inference
Model Building
Residual Analysis
Outliers & Influential Points
Heteroscedasticity Corrections
Post-hoc Testing
Categorical Analysis

Ellie, Mehak, Savannah, and Cathy, MSA Class of 2020


AA502 – Analytics Methods and Applications I
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
Text Analytics
– Term Vectors
– Similarity
– Clustering Text
– Sentiment Analysis
– Natural Language Processing
– Visualization of Text
Advanced Programming
–– 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
–– Tableau
> Big Data
–– AWS
–– Pig
–– Hive
> SAS Macros
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

Women in Analytics Seminar


AA503 – Analytics Methods and Applications II
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
– Simplex Algorithm
– Linear Programming
– Sensitivity Analysis
– Integer and Mixed Integer Programming
– Network Models
– Nonlinear Optimization
– Prior versus posterior distributions
– Convergence of MCMC
Fraud Detection
– Characteristics of Fraud and Fraud Data
– Outlier Detection – MD, Local Outlier Factor, Isolation Trees, CADE
– 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
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

Austin and Chandni, MSA Class of 2020