Supervise/Teach the Following Courses
DATA 250: Analytics Programming
This course introduces the essential general programming concepts and techniques to analytics students. The goal is to equip the students with the necessary programming skill in analytics problem-solving. Topics include boolean, numbers, loops, function, debugging, Python’s specifics (such as NumPy, Pandas, Jupyter notebook), R’s specifics (such as list, data frame, factor, apply, RMarkdown), the process of data retrieving, cleaning, integrating, transforming, and enriching to support analytics.
Weekly Topics
Week 1 – Python Basics
Week 2 – Python Packages – NumPy and Pandas
Week 3 – Data Preparation by Python
Week 4 – Data Wrangling by Python
Week 5 – Data Visualization by Python
Week 6 – Data Aggregation by Python
Week 7 – Midterm Exam
Week 8 – R Basics and Data Visualization
Week 9 – Data Transformation by R
Week 10 – Data Tidying in R
Week 11 – Data Import and Workflow in R
Week 12 – Final Exam
Exams
There is a proctored midterm exam in Week 7 and a proctored final exam in Week 12.
Tools
Python (NumPy, Pandas), R (tidyverse), EdStem
DATA 300: Introduction to Analytics
This course introduces the fundamentals of Business and Data Analytics. Students will learn the fundamentals of business problem framing, data wrangling, descriptive and inferential statistics, data visualization, and data storytelling in analytics. Not open to students with credit for INFA 300.
Weekly Topics
- Week 0 – Pre-course Preparation
- Week 1 – Introduction to Business Analytics
- Week 2 – Introduction to Analytics Programming I
- Week 3 – Introduction to Analytics Programming II
- Week 4 – Data Management and Wrangling
- Week 5 – Summary Measures
- Week 6 – Data Visualization
- Week 7 – Probability and Probability Distributions
- Week 8 – Statistical Inference
- Week 9 – Regression Analysis I
- Week 10 – Regression Analysis II
- Week 11 – Introduction to Prescriptive Analysis
- Week 12 – Final Exam
Exams: There is a proctored final exam in Week 12, but no midterm exam.
Tools: Excel, R
DATA 400: Principles of Machine Learning
Students will learn the basic concepts behind major machine learning algorithms, the essential steps for creating a typical machine learning model, the strengths and weaknesses of different algorithms, and the model evaluation using different performance metrics. Eventually students will be able to build a prediction model by machine learning algorithm using Python language. The differences between Java and Python will be reviewed. The common problems in practical machine learning exercises and their solutions also will be discussed.
Weekly Topics
Week 1 – Introduction to Machine Learning Concepts and Python Analytics Programming
Week 2 – Exploratory Data Analysis
Week 3 – Classification Algorithms
Week 4 – Regression Analysis
Week 5 – Model Evaluations
Week 6 – Advanced Classification Algorithms
Week 7 – Advanced Regression Analysis
Week 8 – Manual Feature Engineering
Week 9 – Hyperparameter Tuning and Pipelines
Week 10 – Ensemble Learning
Week 11 – Unsupervised Learning
Week 12 – Final Presentation & Knowledge Check Exam
Exams
There is a final exam and final project in Week 12.
Tools
Python (scikit-learn, pandas), Jupyter Notebook, EdStem
DATA 430: Data Engineering Technologies
This course covers fundamental methods and widely-used technologies in data engineering. Topics include application programming interface (API), web scraping, Extract Transform Load (ETL), and analytics at-scale using PySpark.
DATA 450: Advanced Topics in Analytics
This course covers advanced analytics topics, including big-data analytics using popular platforms, model interpretation strategies, simulations, optimizations, and analytics reporting and presentation methods. A discussion of ethical considerations for model evaluation is also included.
DATA 600: Modern Tools for Stat Analysis
This course offers an in-depth exploration of modern statistical analysis tools, focusing on the integration of modern analytics tools enhanced by artificial intelligence techniques. The goal is to equip students with advanced skills in using these contemporary analytics tools for statistical descriptive data analysis and effective problem-solving across various analytical contexts.
Weekly Topics
Week 1 – Excel Analytics Essentials
Week 2 – Python Basics
Week 3 – Python Packages: NumPy and Pandas
Week 4 – Data Preparation by Python
Week 5 – Data Wrangling and Visualization by Python
Week 6 – Data Aggregation by Python
Week 7 – R Basics and Data Visualization and Midterm Exam
Week 8 – Data Transformation by R
Week 9 – Data Tidying in R
Week 10 – Data Import and Workflow in R
Week 11 – AI-Augmented Analytics
Week 12 – Final Exam
Exams
There is a midterm exam in Week 7 and a final exam in Week 12.
Tools
Excel, Python (NumPy, Pandas), R (tidyverse), AI
DATA 610: Big Data Analytics and Data Mining
This course explores data mining methods and tools, examines the issues in the analytical analysis of massive datasets, and unstructured data. Students will learn the concepts and techniques to discover the patterns in large datasets, which support organizational decision making.
Weekly Topics
Week 1 – Introduction to Data Science, Data Mining, and Big Data Analytics
Week 2 – Python Basics
Week 3 – Python Packages
Week 4 – Data Wrangling
Week 5 – Data Visualization and Exploration
Week 6 – Regression
Week 7 – Evaluating Model Performance
Week 8 – Supervised Learning
Week 9 – Unsupervised Learning
Week 10 – Decision Trees
Week 11 – Artificial Intelligence
Week 12 – Final Exam
Exams
There is a proctored midterm exam in Week 6 and a proctored final exam in Week 12.
Tools
Python (NumPy, Pandas), Jupyter Notebook, EdStem
DATA 611: Applied Machine Learning
This course explores two main areas of machine learning: supervised and unsupervised. Topics include linear and logistic regression, probabilistic inference, Support Vector Machines, Artificial Neural Networks, clustering, dimensionality reduction, and programming.
Weekly Topics
Week 1 – Python Programming Language and Python in Data Science
Week 2 – Introduction to Machine Learning Concepts
Week 3 – Anatomy of a Classification Algorithm
Week 4 – Classification by scikit-learn
Week 5 – Data Processing
Week 6 – Dimension Reduction
Week 7 – Model Evaluation
Week 8 – Regression
Week 9 – Clustering
Week 10 – Natural Language Processing
Week 11 – Neural Network
Week 12 – Final Project Presentation
Exams
There is no midterm or final exam, but students complete a major final project and presentation in Week 12.
Tools
Python, scikit-learn, Google Colab
DATA 630: Applied Database Management
This course teaches data management from an applied perspective. The topics include fundamentals of database management systems, structured query language (SQL) for data analytics, relational database design, and data warehousing.
Weekly Topics
Week 1 – Data Modeling & Database
Week 2 – Relational Database Model
Week 3 – Data Normalization
Week 4 – Introduction to SQL
Week 5 – Conditions
Week 6 – Join Tables
Week 7 – Aggregate Functions
Week 8 – Windows Functions
Week 9 – Date, Time, & Union
Week 10 – Exploratory Data Analysis & Reporting
Week 11 – Data Warehousing
Week 12 – Final Exam
Exams
There is a proctored midterm exam in Week 6 and a proctored final exam in Week 12.
Tools
SQL, EdStem
BUSA 250: SQL for Business
This course introduces data analytics using Structured Query Language (SQL). Students will learn how to apply SQL in data exploration analysis and business problem-solving.
Weekly Topics
Week 1 – Basics of SQL for Analytics
Week 2 – SQL for Data Preparation
Week 3 – Aggregate Functions for Data Analysis
Week 4 – Window Functions for Data Analysis
Week 5 – Analytics Using Complex Data Types and Performant SQL
Week 6 – Final Project
Exams
There is no midterm or final exam, but students complete a major final project in Week 6.
Tools
SQL, EdStem
BUSA 350: Principles Analytics Modeling
This course introduces the principles of analytics modeling. Students will learn exploratory data analytics, regression, classification, clustering, model interpretation, and model evaluation. Not open to students with credit for INFA 420.
Weekly Topics
Week 1 – Advanced Regression Analysis
Week 2 – Introduction to Data Mining
Week 3 – Supervised Data Mining: KNN & Naïve Bayes
Week 4 – Supervised Data Mining: Decision Trees
Week 5 – Unsupervised Data Mining
Week 6 – Forecasting with Time Series Data
Exams
There is no midterm or final exam, but students complete a major final project.
Tools
R
BUSA 603: Marketing Management & Analytics
This course covers the application of analytics tools, techniques, strategies and methods to marketing management. Students learn to analyze market data, enabling management decisions to be based on data-driven facts and customer insights. Using marketing analytics tools to model scenarios, students learn how organizations can measure returns on investment relative to their marketing efforts, drive performance and strengthen the effectiveness of its campaigns.
Weekly Topics
Week 1 – Introduction to Marketing Metrics and Marketing Analytics
Week 2 – Marketing Data Patterns and Fundamentals of Digital Marketing Analytics
Week 3 – Supervised Learning in Marketing Analytics
Week 4 – Unsupervised Learning in Marketing Analytics
Week 5 – Natural Language Processing and Social Network Analysis in Marketing Analytics
Week 6 – Final Exam
Exams
There is a proctored final exam in Week 6.
Tools
Python, Google Analytics
BUSA 695: Capstone in Business Analytics
Students demonstrate an integrative knowledge of analytics in this course by developing a project plan to implement analytics for an important function, unit or department of the organization chosen in the Business Analytics strategy course. Students apply analytics tools, techniques, methods and strategies to drive business outcomes for the chosen company using relevant project-based methodologies. The course allows students to develop a professional portfolio that will highlight the work completed throughout the degree program. This may serve as a relevant employability resource.
Weekly Topics
Week 1 – Introducing the Capstone Project
Week 2 – Defining the Problem
Week 3 – Develop the Plan
Week 4 – Data Analysis
Week 5 – Data Visualization & Reporting
Week 6 – Solutions & Recommendations
Exams
There is no midterm or final exam, but students complete a major capstone project.
Tools
Students select from tools covered in the program, including Excel, Tableau, Python, R, etc.