Minor in Business Analytics

This course covers the fundamental skills for business analytics: compiling summary statistics, data visualization, descriptive data mining and statistical methods such as hypothesis testing and linear regression. Students gain experience with widely used software tools and learn to report analysis in a presentable format. Topics covered will be useful for further analytical studies in financial analytics, econometrics, marketing analytics, HR analytics, health care analytics and supply chain analytics. Pre-requisite: MA-131/MA-133/MA-331
Prerequisite: Take MA-131or MA-331

This course is a primer on Python programming language for students with little or no prior programming knowledge. In this course, students will start from the fundamentals of Python programming, including developing environment, language syntax, variable types, data structures, functions and control flow. Along the way, students will work with dedicated libraries for data science, such as Pandas, NumPy, SciPy, MatPlotLib, that allow students to develop analytics on data to solve challenging business problems. This course places emphasis on hands-on learning experiences that allow students to experiment with trial and error, and learn from their mistakes.

This is an introductory course on Database concepts and Structured Query Language (SQL) with a focus on business applications. This course provides a step-by-step overview and instructions on a variety of topics, including database and database management system (DBMS), data types, table structure and relationships, field definitions and naming conventions, data access queries using SQL, data definition language (DDL) and data manipulation language (DML) commands; table creation using SQL statements, database security and evolution of data models. No prior database or programming experience is required.

Visualizations are graphical depictions of data that can improve comprehension, communication, and decision making. This course is an introduction to the principles and techniques that increase the understanding of complex data and models. Emphasis is placed on the identification of patterns, trends and difference from data sets across categories, space, and time. Tools used will be Tableau and Python.

This course is geared toward liberal arts, science, business, and health science majors." It introduces descriptive statistics, probability distributions (both discrete and normal), confidence intervals, hypothesis testing, and correlation. Real-world applications are offered and computer statistical software may be used.