M.S. in Business Analytics Online

The skills you will acquire as part of our MSBA program can be put to use in everything from small businesses and start-ups to Fortune 100 companies, so you will just need to determine your best fit. Additionally, research from the McKinsey Global Institute and the U.S. Bureau of Labor Statistics shows that talent for the field of data analytics is sorely needed, so you can be confident in knowing that the education you will receive through our data analytics courses will open numerous doors for your career.

Data Mining / Machine Learning

Principles of Machine Learning and Data Modeling

• Data Structures and Types of Variables
• Supervised vs. Unsupervised Machine Learning Modeling
• Data Preparation Techniques
• Feature Engineering
• Evaluation of Machine Learning Models
• Optimizing Machine Learning Models
• Ensemble Learning
• Common Mistakes in Modeling

Regression Modeling

• Concepts and Definitions
• Performance Metrics
• Linear Regression
• Generalized Linear Models (GLM)

Classification Modeling

• Concepts and Definitions
• Performance Metrics
• Logistic Regression
• k-Nearest Neighbor (k-NN)
• Naïve Bayes  
• Decision Trees (applied to Regression as well)
• Random Forrest (applied to Regression as well)
• Gradient Boosted Machines (applied to Regression as well)
• Support Vector Machines (applied to Regression as well)
• Neural Networks (applied to Regression as well)

Recommendation Systems

• Concepts and Definitions
• Performance Metrics
• Apriori algorithm for association data mining

Time Series Analysis 

• Concepts and Definitions
• Performance Metrics
• Stationarity, casuality and invertibility
• Autoregressive Integrated Moving Average (ARIMA) 

Graph Analytics

• Concepts and Definitions
• Centrality and Connectivity Measures
• Application to Social Network Analysis

Text Analytics 

• Concepts and Definitions
• Feature Extration
• Topic Modeling
• Sentiments Analysis

Data Mining, Machine Learning and Quantitative Programming: R

Implementation of the following Data Mining/ Machine Learning methods:

• Linear Regression 

• Generalized Linear Models  

• Logistic Regression 

• Decision Trees 

• Random Forrest 

• Gradient Boosted Machines 

• Support Vector Machines 

• Neural Networks 

Implementation of the following Quantitative methods:

• Linear Programming 

• Integer Programming 

• Goal Programming 

• Simulated Annealing 

• Network Models 

• Genetic Algorithms/ Programming 

Open Source Data Mining Tool: WEKA 

Implementation of the following Data Mining/ Machine Learning methods:

• k-Nearest Neighbor (k-NN) 
• Naïve Bayes  
• Decision Trees 
• Bagged/Boosted Trees 
• Association Mining 

Data Preparation General Purpose Programming: R

• Calculating Various Statistics and Math Calculations 
• Calculating Probability Values 
• Data Input/ Export 
• Data Cleansing 
• Data Wrangling and Data Subsetting 
• Feature Engineering 
• Applying summarization and Aggregate functions 

Database: SQL 

• Principals of Database Design 
• Using SQL to Create, Update and Delete Tables 
• Using SQL to Select a subset of Data 
• Using SQL to Join Tables 
• Using SQL to perform various Aggregate Functions 

Visualization: R/ Tableau

• Using R ‘ggplot’ for explanatory analysis and communicating the insights 
• Using R ‘Shiny’ for interactive visualization and dash boarding 
• Using Tableau for explanatory analysis and communicating the insights

Software Repository and Development Platforms: Github/Git

• Creating a new repository 
• Fork and Push changes to a repository 
• Clone a public project 
• Send a pull request/ Merge changes from a pull request 

Big Data and High performance Computing: Spark, Hadoop, AWS, Azure, MLlib, R

• Spark and Big Data Ecosystem 
• Using Spark's MLlib for Machine Learning
• Scale up Spark jobs using Amazon Web Services
• Using R in Azure Machine Learning Studio
• Parallel computing using R 

Applied Probability and Statistics

Probability:

• Distributing Functions
• Normal Distribution 
• Uncertainty and Confidence Intervals  
• Conditional Probabilities 
• Bayesian Probability 
• Information Entropy 

Statistics: 

• Measures of Central Tendencies 
• Measures of Dispersion
• Measures of Skewness 
• Measures of Dependence 
• Statistical Significance 
• A/B Testing 

Databases and Data Processing 

Relational Databases 

• Concepts and Definitions 
• Entity-Relationship Diagrams 
• Structured Query Language (SQL)
• Normalization, Transaction management and Concurrency Control 
• SQL as an Analytical Tool 
• Intro to NoSQL Databases and Applications 

Big Data Platforms

• Big Data Paradigms (e.g. MapReduce) 
• Big Data Platforms (e.g. Hadoop) 
• Big Data Extraction/Integration 
• Big Graph Processing 
• Big Data Stream Techniques and Algorithms 

• Linear Programming 
• Duality in Linear Programming 
• Integer Programming
• Goal Programming 
• Simulated Annealing 
• Network Models 
• Genetic Algorithms/ Programming 

• Practical Case Studies Based on Real-World Data from Different Industries 
• Formulation of Business Problems to Solve Using Analytics Group Projects 
• Data Collection and Communication of Findings 
• Operationalizing Analytical Models in Practice 
• Common Mistakes in Analytical Modeling