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


SUMMER SEMESTER


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
  • Presention Skills
  • Programming Concepts
  • Computer & Data Security
  • MBTI
  • Teamwork
  • Report Writing
  • Strength Finders
  • Communications Training

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

FALL SEMESTER


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

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

SPRING SEMESTER


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