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
Jacqueline, MSA Class of 2018


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
Clustering Introduction
AA500 – Analytics Tools and Techniques
Orientation
SAS Programming/Viya
Data Cleaning
Problem Solving
Visualization Techniques
Presentation Skills
Programming Concepts
Computer & Data Security
MBTI
Teamwork
Report Writing
Strength Finders
Communications Training

FALL SEMESTER


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
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
Text Analytics
Term Vectors
Similarity
Clustering Text
Sentiment Analysis
Natural Language Processing
Visualization of Text
Logistic Regression
Contingency Table Analysis
Logistic Regression
Odds Ratios
Sensitivity, Specificity
ROC Curves
Ordinal, Nominal Logistic
Simulation and Risk
Risk Analysis
Simulation
Scenario Analysis
Value at Risk / Expected Shortfall
Copulas
Monte Carlo Simulation
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 & Combined Models
Time Series Clustering
Clustering
Distance measures for clustering
K-Means Clustering
Canonical Discriminant Analysis
Mixture Modeling
Cluster Visualization
Introduction to AB testing
Interpretation
Advanced Programming
SQL
Querying
Joins
Subqueries
Set Operators
Creating tables and views
Advanced Proc SQL Features
R
Data Analysis
Functionality and R-Studio
Writing/Reading External Data Sets
Graphics
Loops & Functions
Python
Variables
Operators
Data Types
Conditionals
Files
Functions
NumPy
pandas
Web crawling
SAS Macros
Visualizations
SAS VA
Tableau
Big Data
AWS
Pig
Hive
Google Maps
Survival Analysis
Survival Curves
Hazard Probabilities
Censoring
Accelerated Failure Time Models
Cox Regression Models
Competing Risks

SPRING SEMESTER


AA504 – Analytics Practicum I
Project phases 1 – 3:
Understanding Business Objectives and Problem Framing
Data Wrangling
Exploratory Analysis
Team Building
Basic Project Management
Leadership
Followership
Conflict Management
Negotiation Skills
Presentation Skills
Process Mapping & Problem Solving
Consulting Skills
Project Management for Analytics
Data Security
Data Privacy
Data Ethics
Problem Solving
Practicum Administrative Overview
Professional Development Skills
Business and Networking Etiquette
Professional Social Media
Biographical Profiles and Self-Branding
Resume Writing
Job Search Tips
Effective Interviewing
Case Study Overview
Case Study Preparation
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
Optimization
Simplex Algorithm
Linear Programming
Sensitivity Analysis
Integer & Mixed Integer Programming
Network Models
Nonlinear Optimization
Deep Learning
Object Recognition
Deep Learning Models
Advanced Topics
Multidimensional Scaling
Markov Chains
Option Pricing
Fraud Detection
Financial Analytics
Scorecard Analysis
CAPM models
Portfolio Optimization
Exponentially weighted MA for Bs
ARCH/GARCH models
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
Results
Report and Presentations
Project Management
Teamwork Skills
Visual Communication of Data
Presentation Skills
Technical Writing

students