Minor in Business Analytics

Business analytics is focused on data-driven business decision-making and the technical and analytical skills needed to interpret data to gain business insights. It encompasses a range of processes, including acquiring and understanding historical data as well as identifying trends, patterns, and causes in the data. It employs software tools that are accessible to the end user, ultimately aiming to supplement business-related skills with analytical skills in the formation of a business analyst.

The business analytics minor is interdisciplinary and is available to students of all majors. The foundation of the minor is based primarily on analytics-focused business core courses and Business Intelligence courses. Electives in the minor include courses from several business and non-business disciplines.

*Students may not earn both the major in Data Science and the minor in Business Analytics.

Program Learning Goals

1. Data Acquisition. Accurately identify and access data sources and structures to retrieve and prepare data for business analysis.
2. Analytics Techniques. Appropriately apply predictive and prescriptive analytics techniques to business data and problems.
3. Descriptive/Visualization. Effectively describe and visualize business data using analytics and business intelligence tools.
4. Reporting/Communication. Ethically interpret and communicate the results of business data analyses.

Core Courses

19 credit hours.

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Minor Courses

Minor Courses
BI 1100	Business Analysis with Excel How to answer key business questions, analyze company finances, forecast sales, and prepare business cases while improving your Excel skills.
BI 2200	Data-Driven Decision-Making Introduction to business intelligence and analysis concepts, including data management, summarization, and visualization. Database, spreadsheet, and visualization software offer a hands-on perspective of key tools in data analysis and business decision-making. The course goes beyond software tutorials by using lectures, exercises, and assignments to present data modeling and analytical concepts and techniques. Prerequisite: BI 100 or BI 1100.
BI 2202	Business Applications of R Introduction to the R programming language using business analytics problems. Topics include using RStudio, reading data into R, and programming in R. Statistical techniques such as data mining and marketing analysis examples will provide context to the topics. Prerequisite: EC 209 or EC 2209 or EC 210 or EC 2210.
EC 2210	Business Analytics & Statistics Descriptive statistics, probability and probability distributions, sampling, and sampling distributions, hypothesis testing, chi-square analysis, analysis of variance, correlation, bivariate and multivariate regression analysis, time series, and index numbers.
BI 2201	Business Data Acquisition Using SQL Introduction to Structured Query Language (SQL) and relational databases and modeling as they relate to accessing business data. The primary focus will be on data retrieval statements (i.e., data query language). Multi-tables databases and joins also will be covered. Prerequisite: BI 200 or BI 2200.
BI 2241	Introduction to Data Visualization with Tableau Teaches concepts, theories, and skills, related to data visualization. Students merge, join, and download data from several sources to prepare and interpret usable visualizations. Students learn theory and design principles, how to spot misleading visualizations, as well as best practices for data visualization and dashboard design. Students also sharpen their analytical skills and learn to use Tableau. Prerequisites: (BI 100 or BI 1100) and (EC 210 or EC 2210).
BI 3341	Advanced Data-Driven Decision-Making An in-depth focus on advanced business intelligence (BI) and “power query” tools as they are used in the process of translating data into information and insights. Hands-on classroom work and assignments using BI software provide a learning tool to better understand data access, retrieval, preparation, summarization, and reporting. Data sets enhance a business (and often marketing) context to the concepts and analyses. BI 200 or BI 2200 AND (EC 210 or EC 2210 or Data 122 or Data 1220 or Data 228 or Data 2280 or other equivalent statistics class).
BI 3371	Business Decision Optimization Application of mathematical optimization to decision-making. Uses MS-Excel and several add-ins as tools to find optimal solutions to a wide variety of business problems. Topics include linear programming, network models, non-linear programming, goal programming, decision trees, and simulation. Prerequisites: (EC 210 or EC 2100 or MT 122 or MT 1220 or MT 228 or MT 2280) AND BI 200 or BI 2200.

Alternatives to EC 2210 are EC 2209 plus one of the following: DATA 1122 or DATA 2228 or PO 1050.

Alternatives to BI 2201 and 2202 are DATA 1150 and 1100 respectively.

Alternative to BI 3341 is MK 3382.

Courses

Contextual Elective Courses (3 to 6 credit hours – choose one group)

One course or pair of courses chosen from one of the following three Groups. Students are responsible for completing any pre- or co-requisites for any listed course.

Group 1: Business Data-Intensive Course (3 credit hours)
AC 2210	Business Analytics in Accounting A survey course that addresses the use of data analytic tools and techniques in a general accounting context. Topics to be covered include data collection, storage and sharing through the use of technology tools and the inherent risks and ethical issues involved; the use of business analytics techniques to gain insights from data to solve accounting problems; exposure to some of the most commonly used business intelligence software packages; and approaches to successfully communicate the results of analyses using analytics. Prerequisite: BI 100 or BI 1100.
EC 3310	Empirical Methods I This course is designed to prepare students to handle big data, run quantitative analyses of economic models using computers, interpret regression analysis, and perform several empirical analyses on their own using real-life data. Topics covered include linear regression models, simultaneous-equations models, panel data analysis, discrete choice models, among others. Offered: Fall only. Prerequisites: EC 201 or EC 2201, EC 202 or EC 2202, EC 210 or EC 2210, EC 301 or EC 3301, EC 302 or EC 3302.
FN 3342	Investments Principles in the selection and management of investments, from the viewpoints of large and small investors. Prerequisites: FN 3312 or FN 312 (minimum grade of C), MT 1300 or MT 130 (minimum grade of C).
MK 3381	Digital Marketing Analytics 3 cr. Examines the implications of digital data for businesses and consumers. Focuses on analytics behind planning and evaluating digital marketing efforts. Topics include website analytics, organic search analytics, digital advertising analytics, social media analytics, translating analytical insights in actions, and marketing automation. Offered fall semester only; should be taken in senior year. 3 cr. Prerequisite: MK 301 or MK 3301.
MK 4402	Applied Market Research & Analysis Examination of the quantitative tools marketers use to develop, monitor, and evaluate marketing strategies. Topics include the use of Qualtrics online survey tools, statistical analysis using SPSS, including correlation analysis, difference analysis with t tests, ANOVA, and multiple regression analysis. Senior standing required. Prerequisite: MK 301 or MK 3301, AC 202 or AC 2202, and (EC 210 or EC 2210 or MT 223 or MT 2230 or equivalent level of statistics). Offered fall semester only; should be taken in senior year.
MOL 3343	Technology & People Analytics This course prepares the student to navigate the use of professional technologies and research methodology in management and organizational leadership. Students will learn to identify and compute commonly used people-oriented metrics and analyze data in Microsoft EXCEL. Students will gain analytical decision-making skills by answering practical questions based on an interpretation of the results. They will also be introduced to and become fluent in the language of digital technologies used in organizations. Ethical and legal considerations of using employee and applicant data to make business decisions will be discussed.

One course plus lab chosen from the following:

Group 2: Context-Specific Data-Intensive Course/Lab (4 credit hours)
PO 3520 & PO 3521	Quantitative Research Methods + Political Science Research Methods Lab The principles and tools of political (and social) science research, including variables, hypotheses, measurement, research designs, sampling, data collection, and data analysis. Emphasis on practical application by learning the use of professional analysis software. Should be taken by the end of the junior year. Prerequisite: PO 200. Corequisite: PO 3521. Equivalent to: PO 300.
PS 3011 & PS 3010	Experimental Design & Analysis in Psychology + Experimental Design & Analysis Lab Introduction to the scientific method as it is used to design, conduct, and analyze experiments in psychology. Corequisite: PS 3010
SC 3510 & SC 3520	Sociological Data Analysis + Research Methods Lab How to do quantitative data analysis, including SPSS statistical analysis program coding/recoding variables, levels of measurement, hypothesis testing, estimation, sampling distributions, bivariate relationships, correlations, and regression. Requires an original quantitative research project. Prerequisite: (DATA 122 or DATA 1220) or (PO 105 or PO 1500) and (SC 350 or SC 3500). Corequisite: SC 3520.

Students must take DATA 2600 + one choose from one of the three following:

Group 3: Advanced Statistics Plus Methods Course (6 credit hours)
DATA 2600	Intermediate Statistics with SPSS Power analysis, factorial and repeated measures analysis of variance, nonparametric procedures, contingency tables, introduction to linear regression. Use of SPSS. Note: A grade of C- or higher in DATA 2600 is required to register for any course that has DATA 2600 as a prerequisite. This course was formerly offered as MT 223.Prerequisites: DATA 122 or DATA 1220 or equivalent. Offered: Fall, Spring.
COM 3041	Communication Research Methods Building on the department goal of understanding target audiences, students will explore qualitative and quantitative methodologies for defining and reaching audiences, and demonstrate an understanding of demographics, psychographics, primary and secondary research, survey design, focus groups, benchmark research (pre-and-post campaign assessment), one-on-one interviews, ethnography, narrative analysis, experimental design, case studies, and more.
ESC 2320/SPL 2320	Research Methods in Exercise Science & Sports Studies Research methodology used in exercise science, allied health and sports studies. Emphasis on the individual aspects of the research process, such as the use of research databases, developing reviews of literature, developing research questions. Development of a research proposal is required. Prerequisite: QA course.
SC 3500	Sociological Research Methods I Focuses on the logic of, procedures for, and issues relating to, theory testing in various types of social research. Topics include hypothesis construction, concept operationalization, research design, data collection, instrument construction, sampling techniques, and ethical concerns. Methods include surveys, in-depth interviews, observational field research, and content analysis. Meets the additional writing (AW) requirement for the Integrative Core Curriculum. Prerequisite: (SC 101 or SC 1010) and two additional SC courses.

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