Course Offerings in Analytics Programs
π Undergraduate Courses
Dr. Li as Lead Faculty
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, Pythonβs specifics (such as NumPy, Pandas, Jupyter notebook), Rβs specifics (such as list, data frame, factor, apply, RMarkdown), the process of data retrieving, cleaning, integrating, transforming, and enriching to support analytics.
Exams: There are proctored midterm and final exams.
Analytics Tools: Python (NumPy, Pandas), R (tidyverse)
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.
Exams:: There is a proctored final 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.
Exams: There is a proctored final exam.
Analytics Tools: Python (scikit-learn, pandas), Jupyter Notebook
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.
Analytics Tools: Python, 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.
Analytics Tools: Python, R
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.
Analysis Tools: SQL
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.
AnalyticsTools: R
Other Key Offerings
BUSA 200: Database Fundamentals
This introductory course focuses on applying information technology to business strategies using databases. The student will gain a working knowledge of current database technology, including relational database concepts, database design, data extraction, and data warehousing while working with database applications.
Exams: There is a proctored final exam.
Analysis Tools: SQL
π― Graduate Courses
Dr. Li as Lead Faculty
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.
Exams: There are proctored midterm and final exams.
Analytics Tools: Python (NumPy, Pandas), Jupyter Notebook
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.
Exams: There are proctored midterm and final exams.
Analytics Tools: SQL
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.
Exams: There are proctored midterm and final exams.
Analytics Tools: Excel, Python (NumPy, Pandas), R (tidyverse), AI
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.
Analytics Tools: Python, scikit-learn, Google Colab
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.
Exams: There is a proctored final exam.
Analytics 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.
Tools
Students select from tools covered in the program, including Excel, Tableau, Python, R, etc.
Other Key Offerings
BUSA 604: Financial Decision Modeling
This course is built on the theory, strategy and practice of financial management, emphasizing computer-based modeling and forecasting. Students learn to model financial scenarios using analytics tools. The impact of financial decisions relative to financial statements analysis, cash budgeting, cost of capital determination, capital budgeting, and capital structure choices are covered. A variety of techniques, such as sensitivity and scenario analysis, optimization methods, Monte Carlo simulation, and regression analysis are also covered.
Analytics Tools: Excel
BUSA 605: Business Analytics Strategy
This course allows students to apply the fundamental analytics principles in the development of an analytics strategy for a business of choice. Given a range of options, students will research and choose the best analytics strategy under given scenarios. The course uses case studies, employing a problem and project-based approach to the development of a strategy.
Analytics Tools: Selected by students if needed.