Dr. Natalia Summerville

MSA Curriculum

Curriculum: Practical + Relevant + EvolvingThe 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.

Master of Science in Analytics Curriculum Map
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
AA500 – Analytics Tools and Techniques
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
Confidence intervals
Hypothesis testing
Exploratory data analysis
Linear regression
Linear regression assumptions
Polynomial regression
Regression inference
Model building
Residual analysis
Outliers and influential points
Heteroscedasticity corrections
Post-hoc testing
Categorical analysis
Logistic regression

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


AA502 – Analytics Methods and Applications I
Data Mining
Issues in Data Manipulation
– Association Analysis
– Classification and Regression Trees
– Clustering
– kNN
– 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
– 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)
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

Color Seminar


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
– Simplex Algorithm
– Linear Programming
– Integer and Mixed Integer Programming
– Network Models
– Nonlinear Optimization
– Prior Inference
– Convergence
Fraud Detection
– Characteristics of Fraud and Fraud Data
– Outlier Detection – MD, Local Outlier Factor, Isolation Trees, CADE
– 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:
– 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

Austin and Chandni, MSA Class of 2020