Duluth campus
 
Duluth Campus

Business Analytics Minor

Management Studies
Labovitz School of Business and Economics
  • Program Type: Undergraduate minor related to major
  • Requirements for this program are current for Spring 2022
  • Required credits in this minor: 12 to 35
Business analytics combines capabilities in data management, applied statistics, and data analytic techniques to provide insights valuable to any discipline. Among the topics covered include managing databases, visualizing data, descriptive, predictive, and prescriptive analytics.
Program Delivery
This program is available:
  • via classroom (the majority of instruction is face-to-face)
Admission Requirements
All pre-major requirements and completion of Level 2 (Expert) MS Excel Certification or completion of Levels I and II Excel workshops made available by LSBE.
For information about University of Minnesota admission requirements, visit the Office of Admissions website.
Minor Requirements
Program Sub-plans
Students are required to complete one of the following sub-plans.
Business Analytics for BBA/BAcc Students
Business Analytics (12 cr)
Group A (9 cr)
MIS 3220 - Database Management and Design (3.0 cr)
BA 4410 - Data Visualization (3.0 cr)
or BA 5410 - Data Visualization (3.0 cr)
BA 4420 - Data Analytics for Managerial Decision Making (3.0 cr)
or BA 5420 - Data Analytics for Managerial Decision Making (3.0 cr)
Group B (3 cr)
Take 1 or more course(s) totaling 3 or more credit(s) from the following:
· BA 4440 - Spreadsheet Modeling and Decision Analysis (3.0 cr)
· BA 4460 - Big Data Analytics (3.0 cr)
· ECON 3020 - Applied Statistics for Business and Economics II (3.0 cr)
· HCM 4580 - Health Services Data and Analysis (3.0 cr)
· MATH 4180 - Solving Industrial Mathematics Research Problems (3.0 cr)
· MGTS 4825 - Human Resource Analytics (3.0 cr)
· MKTG 3731 - Sales Analytics: An Introduction to Sales Analysis Techniques and Applications (3.0 cr)
· STAT 4050 - Introduction to Statistical Computing (3.0 cr)
· STAT 4060 - Introduction to Biostatistics (3.0 cr)
Business Analytics for Economics BA and non-LSBE Students
Pre-Minor Core (20 - 23 cr)
Mathematics
MATH 1160 - Finite Mathematics and Introduction to Calculus [LE CAT, LOGIC & QR] (5.0 cr)
or MATH 1296 - Calculus I [LE CAT, LOGIC & QR] (5.0 cr)
Computer Science/IT
CS 1511 - Computer Science I [LE CAT, LOGIC & QR] (5.0 cr)
or MIS 2201 - Information Technology in Business (3.0 cr)
Accounting
ACCT 2005 - Survey of Accounting [LE CAT] (3.0 cr)
Business
MGTS 1101 - Introduction to Business [LE CAT8] (3.0 cr)
Statistics
ECON 2030 - Applied Statistics for Business and Economics [LOGIC & QR] (3.0 cr)
or PSY 3020 - Statistical Methods (4.0 cr)
or SOC 3155 - Quantitative Research Methods and Analysis (4.0 cr)
or STAT 1411 - Introduction to Statistics [LE CAT, LOGIC & QR] (3.0 cr)
or STAT 2411 - Statistical Methods [LE CAT, LOGIC & QR] (3.0 cr)
or STAT 3411 - Engineering Statistics (3.0 cr)
or STAT 3611 - Introduction to Probability and Statistics (4.0 cr)
Economics
ECON 1003 - Economics and Society [LE CAT, SOC SCI] (3.0 cr)
or ECON 1022 - Principles of Economics: Macro [LE CAT, SOC SCI] (3.0 cr)
ECON 1023 - Principles of Economics: Micro [LE CAT, SOC SCI] (3.0 cr)
Business Analytics (12 cr)
Group A (9 cr)
MIS 3220 - Database Management and Design (3.0 cr)
BA 4410 - Data Visualization (3.0 cr)
or BA 5410 - Data Visualization (3.0 cr)
BA 4420 - Data Analytics for Managerial Decision Making (3.0 cr)
or BA 5420 - Data Analytics for Managerial Decision Making (3.0 cr)
Group B (3 cr)
Take 1 or more course(s) totaling 3 or more credit(s) from the following:
· BA 4440 - Spreadsheet Modeling and Decision Analysis (3.0 cr)
· BA 4460 - Big Data Analytics (3.0 cr)
· ECON 3020 - Applied Statistics for Business and Economics II (3.0 cr)
· HCM 4580 - Health Services Data and Analysis (3.0 cr)
· MATH 4180 - Solving Industrial Mathematics Research Problems (3.0 cr)
· MGTS 4825 - Human Resource Analytics (3.0 cr)
· MKTG 3731 - Sales Analytics: An Introduction to Sales Analysis Techniques and Applications (3.0 cr)
· STAT 4050 - Introduction to Statistical Computing (3.0 cr)
· STAT 4060 - Introduction to Biostatistics (3.0 cr)
 
More program views..
View college catalog(s):
· Labovitz School of Business and Economics


View checkpoint chart:
· Business Analytics Minor
View PDF Version:
Search.
Search Programs

Search University Catalogs
Related links.

Labovitz School of Business and Economics

Duluth Admissions

Duluth Application

One Stop
for tuition, course registration, financial aid, academic calendars, and more
 
MIS 3220 - Database Management and Design
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Concepts and structures relating to design, implementation, and administration of database management systems. Emphasis on relational databases and development of integrated applications. prereq: FMIS 2201 or MIS 2201 or CS 1121 or CS 1511, LSBE candidate or non-LSBE MIS minor or college consent; credit will not be granted if already received for FMIS 3220
BA 4410 - Data Visualization
Credits: 3.0 [max 3.0]
Course Equivalencies: BA 4410/MIS 3231
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Data visualization is the art and science of presenting data effectively in order to facilitate knowledge sharing and decision making. How to present and visualize data is an important skill for business professions to develop. This course will teach the principles and techniques that empower students to understand and interpret data, as well as make effective decisions based on data. Students will learn the benefits of effective data presentation and visualization, understand the principles and methods of visualization, and apply the principles using popular data visualization technologies. pre-req: FMIS 2201 or MIS 2201, LSBE candidate or Business Analytics minor, no grad credit, credit will not be granted if already received for MIS 3231
BA 5410 - Data Visualization
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Data visualization is the art and science of presenting data effectively in order to facilitate knowledge sharing and decision making. How to present and visualize data is an important skill for business professions to develop. This course will teach the principles and techniques that empower students to understand and interpret data, as well as make effective decisions based on data. Students will learn the benefits of effective data presentation and visualization, understand the principles and methods of visualization, and apply the principles using popular data visualization technologies. Students enrolled in the 5410 version of the course will have to fulfill an extra assignment/project to earn graduate credit. pre-req: FMIS 2201 or MIS 2201, LSBE candidate or Business Analytics minor, credit will not be granted if already received for MIS 3231
BA 4420 - Data Analytics for Managerial Decision Making
Credits: 3.0 [max 3.0]
Course Equivalencies: BA 4420/MIS 4241
Grading Basis: A-F or Aud
Typically offered: Every Spring
This course introduces the basic elements of business analytics and how to analytically think about data and its role in business. The goal of the course is to provide students with the toolset and capabilities as they analyze data to ask the right questions that matter to businesses and help solve business problems. Topics include data preprocessing, exploratory data analysis (EDA), predictive analytics, modeling and model evaluation. The course is designed to trigger passion for analytics, develop data-analytic thinking demonstrate how analytics matter in different business domains, illustrate real-world examples in different business contexts while working hands-on using data analytics is as such an art as it is a science. pre-req: MIS 2201, ECON 2030, LSBE candidate or Business Analytics minor, no grad credit. Credit will not be granted if already received for MIS 3241, MIS 4241, CIA 3760 or CIA 4761 or CIA 5761
BA 5420 - Data Analytics for Managerial Decision Making
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
This course introduces the basic elements of business analytics and how to analytically think about data and its role in business. The goal of the course is to provide students with the toolset and capabilities as they analyze data to ask the right questions that matter to businesses and help solve business problems. Topics include data preprocessing, exploratory data analysis (EDA), predictive analytics, modeling and model evaluation. The course is designed to trigger passion for analytics, develop data-analytic thinking demonstrate how analytics matter in different business domains, illustrate real-world examples in different business contexts while working hands-on using data analytics is as such an art as it is a science. Students enrolled in the 5420 version of the courses will have to fulfill an extra assignment/project to earn graduate credit. pre-req: MIS 2201, ECON 2030, LSBE candidate or Business Analytics minor. Credit will not be granted if already received for MIS 3241, MIS 4241, CIA 3760 or CIA 4761 or CIA 5761
BA 4440 - Spreadsheet Modeling and Decision Analysis
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
This course is a practical introduction to mathematical spreadsheet models with an emphasis on predictive and prescriptive analytics for making business decisions. Concepts covered include data exploration and slicing and diving data using spreadsheets, optimization, sensitivity analysis, network modeling, simulation, regression, decision analysis, cluster analysis, and time series forecasting. Students are expected to communicate insights from the analysis in written and oral formats appropriate for a general audience. pre-req: MIS 2201, ECON 3020, LSBE candidate or Business Analytics minor or instructor approval, no grad credit
BA 4460 - Big Data Analytics
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
This course is a practical introduction to managing big data in the enterprise and covers aspects of technology infrastructure, data warehousing and structuring data for use in the organization. Using state-of-the-art open source big data ecosystems and cloud resources for data acquisition, extraction, cleansing, transformation and loading, the course demonstrates how the ecosystem integrates with other analytic tools to provide solutions for practical use cases. pre-req: MIS 3220 or equivalent, LSBE candidate or Business Analytics minor or instructor consent, no grad credit
ECON 3020 - Applied Statistics for Business and Economics II
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall & Spring
A second introductory statistics course including more advanced topics. Topics include hypothesis testing, analysis of variance, and introduction to correlation and regression. pre-req: LSBE Candidate and one of the following courses: ECON 2030, POL 2700, PSY 3020, SOC 3155, STAT 1411, STAT 2411, STAT 3411 or STAT 3611.
HCM 4580 - Health Services Data and Analysis
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Introduction to the types, use, and analysis of data in health services delivery and research. This includes electronic health record, claims, and patient satisfaction data, as well as publicly available data sets. Topics include data organization, data sources available in the health services, conceptualizing analysis, sampling, data validity and reliability, qualitative and quantitative data analysis, applying research results, and communicating findings. prereq: 4520 or instructor consent, no grad credit
MATH 4180 - Solving Industrial Mathematics Research Problems
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
This course is intended for mathematics or statistics majors. The focus of the course is solving industrial mathematics research problems. Students will work in teams of three to five on a semester-long research problem from business, industry or government. Students will acquire specialized mathematical knowledge specific to the research problems posed for the semester. In addition, students will develop problem solving, teamwork, and communication skills as they design and implement a solution strategy for one of the research problems. A business, industry or government partner will serve as a liaison for project teams. Presentation to professional partners will occur throughout the semester. A final solution product will include oral, written and video presentations. pre-req: Minimum 2 courses in MATH or STATS at about about the 3xxx level, with a minimum of 3 credits each, instructor consent; no grad credit
MGTS 4825 - Human Resource Analytics
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Human Resource (HR) analytics is a sector within the field of human resource management that aims at using measurement and analysis techniques to understand, improve, and optimize the people side of the business. HR analytics adds value to businesses by improving vital decisions about talent and how it is organized in organizations. This course will teach the analytical foundations of HR decisions, the connections between data analytics and strategic HRM, and the applications of analytic logic and processes of various HR functions and workplace trends. Students will learn how to gather and analyze pertinent HR metrics and how to properly communicate findings to support HR decisions and drive organizational decisions. pre-req: MGTS 3801, LSBE candidate or HRM minor; no grad credit
MKTG 3731 - Sales Analytics: An Introduction to Sales Analysis Techniques and Applications
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Sales Analytics introduces students to the foundation metrics used in Business to Business and other sales environments. Students use excel to manage and summarize data sets, analyze product category and brand trends, and assess the impacts of various trade promotions. Students develop business insights from the data sets and use these insights to build compelling sales presentations. The course focuses on the use of data sets typical to consumer packaged goods industries but will also integrate data from other sources including: the US Census, other government surveys and Experian Simmons Oneview. pre-req: MKTG 3701 and Professional Sales Major
STAT 4050 - Introduction to Statistical Computing
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Statistical, graphical and numerical data analysis using modern statistical software. Database management and statistical modeling including regression and categorical data analysis. Topics in data generation and simulation. prereq: A grade of at least C- in STAT 3411 or 3611 or instructor consent.
STAT 4060 - Introduction to Biostatistics
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Introduction to statistical methods applicable to biological and biomedical data. Analysis of bioassay, case-control, and disease/expose data. Introduction to statistics in clinical trials. Use of regression and logistic regression in analyzing biological/biomedical data. Categorical data analysis with application to the life sciences. Basic survival analysis. prereq: Math 1290 or 1296 or 1596 and STAT 2411 or 3411 or 3611 with grade of C- or better or consent of instructor.
MATH 1160 - Finite Mathematics and Introduction to Calculus (LE CAT, LOGIC & QR)
Credits: 5.0 [max 5.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall, Spring & Summer
Elementary functions, matrices, graphical and algebraic methods for solving systems of linear equations and inequalities, introduction to linear programming, and abbreviated treatment of calculus with emphasis on business and social science applications. prereq: Math ACT 24 or higher or a grade of at least C- in Math 1005 or department consent; if you have received credit for 1290 or 1296 or 1596, you will not receive credit for Math 1160.
MATH 1296 - Calculus I (LE CAT, LOGIC & QR)
Credits: 5.0 [max 5.0]
Course Equivalencies: Math1290/1296/1596
Grading Basis: A-F or Aud
Typically offered: Every Fall, Spring & Summer
First part of a standard introduction to calculus of functions of a single variable. Limits, continuity, derivatives, integrals, and their applications. prereq: Math ACT 27 or higher or a grade of at least C- in Math 1250 or department consent
CS 1511 - Computer Science I (LE CAT, LOGIC & QR)
Credits: 5.0 [max 5.0]
Course Equivalencies: CS 1511/1581
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
A comprehensive introduction to computer programming using the C++ language. The course covers program design, C++ programming basics, control structures, functions and parameter passing. Students write and implement programs with data structures (arrays), pointers and files. Object-oriented programming is also introduced, along with concepts of abstraction, ADTs, encapsulation and data hiding. prereq: 3 1/2 yrs high school math or instructor consent
MIS 2201 - Information Technology in Business
Credits: 3.0 [max 3.0]
Course Equivalencies: FMIS 2201/1201/3201/CS 1011
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Introduction to information technology (IT) concepts: computer hardware and software; use of personal productivity tools (spreadsheet, database, and presentation software); system development processes; Web technologies; applications of IT in business processes. prereq: LSBE major or minor student or Graphic Design and Marketing major or Graphic Design with Marketing subplan major or Computer Information Systems majors or minors, or Arts Administration, minimum 15 credits or college consent; credit will not be granted if already received for FMIS 2201
ACCT 2005 - Survey of Accounting (LE CAT)
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall
Survey of Accounting provides an overview of fundamental concepts and procedures in financial and managerial accounting. The emphasis is on helping students to develop a basic understanding of the contexts of accounting reports provided to decision makers. Credit cannot be applied toward the BAcc or BBA degree programs or the Accounting minor.
MGTS 1101 - Introduction to Business (LE CAT8)
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall & Spring
Introduction to context, environment, and operation of business and organizations. Study of foundations and functional areas of business and entrepreneurship. Analysis of technological, ethical, diversity, and global issues from business and organizational perspectives.
ECON 2030 - Applied Statistics for Business and Economics (LOGIC & QR)
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall, Spring & Summer
Introduction to modern business statistics, emphasizing problem solving applications through statistical decision making using case studies. Topics include organization and presentation of data, summary statistics, distributions, statistical inference including estimation, and hypothesis testing. prereq: minimum 30 credits, LSBE student, pre-business or pre-accounting or Econ BA major or Graphic Design and Marketing major or Graphic Design with Marketing subplan major or Econ minor or Accounting minor or Business Admin minor or Arts Administration; credit will not be granted if already received for Econ 2020, Stat 1411, Stat 2411, Stat 3611, Soc 3151, Psy 3020
PSY 3020 - Statistical Methods
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall, Spring & Summer
Descriptive statistics; introduction to correlational analysis and regression; sampling techniques and statistical inference; applications of simple and factorial design analysis of variance and other parametric and nonparametric hypothesis-test statistics in the behavioral sciences. prereq: Math ACT 21 or higher or MATH 1005
SOC 3155 - Quantitative Research Methods and Analysis
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Descriptive statistics. Measures of central tendency, deviation, association. Inferential statistics focusing on probability and hypothesis testing. T-tests, Chi-square tests, analysis of variance, measures of association, introduction to statistical control. Statistical software (SPSS) used to analyze sociological data. Lab. prereq: 2155, crim major or soc major or URS major, min 30 cr
STAT 1411 - Introduction to Statistics (LE CAT, LOGIC & QR)
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Statistical ideas involved in gathering, describing, and analyzing observational and experimental data. Experimental design, descriptive statistics, correlation and regression, probabilistic models, sampling, and statistical inference. prereq: Math ACT 21 or higher or a grade of at least C- in MATH 0103 or department approval
STAT 2411 - Statistical Methods (LE CAT, LOGIC & QR)
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Graphical and numerical descriptions of data, elementary probability, sampling distributions, estimations, confidence intervals, one-sample and two-sample t-test. prereq: Math ACT 24 or higher or a grade of at least C- in Math 1005 or higher or department approval
STAT 3411 - Engineering Statistics
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Statistical considerations in data collection and experimentation. Descriptive statistics, least squares, elementary probability distributions, confidence intervals, significance tests, and analysis of variance as applied analysis of engineering data. prereq: MATH 1297 with a grade of C- or better, cannot be applied to a math or statistics major
STAT 3611 - Introduction to Probability and Statistics
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall, Spring & Summer
Basic probability, including combinatorial methods, random variables, mathematical expectation. Binomial, normal, and other standard distributions. Moment-generating functions. Basic statistics, including descriptive statistics and sampling distributions. Estimation and statistical hypothesis testing. prereq: A grade of at least C- in Math 1290 or Math 1296
ECON 1003 - Economics and Society (LE CAT, SOC SCI)
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Spring
General description of U.S. economy and analysis of contemporary economic problems. Introduction to major economic issues and problems of the day, providing a simple framework used by economists for analysis. prereq: Cannot apply credit to economics major or minor or BAc or BBA majors
ECON 1022 - Principles of Economics: Macro (LE CAT, SOC SCI)
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Analyzing overall performance of an economic system. National income accounting and theory, unemployment, inflation, fiscal policy, money, monetary policy, economic growth, international trade, non-U.S. economies, and real-world application of these concepts. prereq: Minimum 15 credits or department consent
ECON 1023 - Principles of Economics: Micro (LE CAT, SOC SCI)
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Analyzing free enterprise system through study of product and resource markets. Supply and demand, utility, production and cost, market structure, resource use, market failures, regulatory role of government, and real-world application of these concepts. prereq: Minimum 15 credits or department consent
MIS 3220 - Database Management and Design
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Concepts and structures relating to design, implementation, and administration of database management systems. Emphasis on relational databases and development of integrated applications. prereq: FMIS 2201 or MIS 2201 or CS 1121 or CS 1511, LSBE candidate or non-LSBE MIS minor or college consent; credit will not be granted if already received for FMIS 3220
BA 4410 - Data Visualization
Credits: 3.0 [max 3.0]
Course Equivalencies: BA 4410/MIS 3231
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Data visualization is the art and science of presenting data effectively in order to facilitate knowledge sharing and decision making. How to present and visualize data is an important skill for business professions to develop. This course will teach the principles and techniques that empower students to understand and interpret data, as well as make effective decisions based on data. Students will learn the benefits of effective data presentation and visualization, understand the principles and methods of visualization, and apply the principles using popular data visualization technologies. pre-req: FMIS 2201 or MIS 2201, LSBE candidate or Business Analytics minor, no grad credit, credit will not be granted if already received for MIS 3231
BA 5410 - Data Visualization
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Data visualization is the art and science of presenting data effectively in order to facilitate knowledge sharing and decision making. How to present and visualize data is an important skill for business professions to develop. This course will teach the principles and techniques that empower students to understand and interpret data, as well as make effective decisions based on data. Students will learn the benefits of effective data presentation and visualization, understand the principles and methods of visualization, and apply the principles using popular data visualization technologies. Students enrolled in the 5410 version of the course will have to fulfill an extra assignment/project to earn graduate credit. pre-req: FMIS 2201 or MIS 2201, LSBE candidate or Business Analytics minor, credit will not be granted if already received for MIS 3231
BA 4420 - Data Analytics for Managerial Decision Making
Credits: 3.0 [max 3.0]
Course Equivalencies: BA 4420/MIS 4241
Grading Basis: A-F or Aud
Typically offered: Every Spring
This course introduces the basic elements of business analytics and how to analytically think about data and its role in business. The goal of the course is to provide students with the toolset and capabilities as they analyze data to ask the right questions that matter to businesses and help solve business problems. Topics include data preprocessing, exploratory data analysis (EDA), predictive analytics, modeling and model evaluation. The course is designed to trigger passion for analytics, develop data-analytic thinking demonstrate how analytics matter in different business domains, illustrate real-world examples in different business contexts while working hands-on using data analytics is as such an art as it is a science. pre-req: MIS 2201, ECON 2030, LSBE candidate or Business Analytics minor, no grad credit. Credit will not be granted if already received for MIS 3241, MIS 4241, CIA 3760 or CIA 4761 or CIA 5761
BA 5420 - Data Analytics for Managerial Decision Making
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
This course introduces the basic elements of business analytics and how to analytically think about data and its role in business. The goal of the course is to provide students with the toolset and capabilities as they analyze data to ask the right questions that matter to businesses and help solve business problems. Topics include data preprocessing, exploratory data analysis (EDA), predictive analytics, modeling and model evaluation. The course is designed to trigger passion for analytics, develop data-analytic thinking demonstrate how analytics matter in different business domains, illustrate real-world examples in different business contexts while working hands-on using data analytics is as such an art as it is a science. Students enrolled in the 5420 version of the courses will have to fulfill an extra assignment/project to earn graduate credit. pre-req: MIS 2201, ECON 2030, LSBE candidate or Business Analytics minor. Credit will not be granted if already received for MIS 3241, MIS 4241, CIA 3760 or CIA 4761 or CIA 5761
BA 4440 - Spreadsheet Modeling and Decision Analysis
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
This course is a practical introduction to mathematical spreadsheet models with an emphasis on predictive and prescriptive analytics for making business decisions. Concepts covered include data exploration and slicing and diving data using spreadsheets, optimization, sensitivity analysis, network modeling, simulation, regression, decision analysis, cluster analysis, and time series forecasting. Students are expected to communicate insights from the analysis in written and oral formats appropriate for a general audience. pre-req: MIS 2201, ECON 3020, LSBE candidate or Business Analytics minor or instructor approval, no grad credit
BA 4460 - Big Data Analytics
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
This course is a practical introduction to managing big data in the enterprise and covers aspects of technology infrastructure, data warehousing and structuring data for use in the organization. Using state-of-the-art open source big data ecosystems and cloud resources for data acquisition, extraction, cleansing, transformation and loading, the course demonstrates how the ecosystem integrates with other analytic tools to provide solutions for practical use cases. pre-req: MIS 3220 or equivalent, LSBE candidate or Business Analytics minor or instructor consent, no grad credit
ECON 3020 - Applied Statistics for Business and Economics II
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall & Spring
A second introductory statistics course including more advanced topics. Topics include hypothesis testing, analysis of variance, and introduction to correlation and regression. pre-req: LSBE Candidate and one of the following courses: ECON 2030, POL 2700, PSY 3020, SOC 3155, STAT 1411, STAT 2411, STAT 3411 or STAT 3611.
HCM 4580 - Health Services Data and Analysis
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Introduction to the types, use, and analysis of data in health services delivery and research. This includes electronic health record, claims, and patient satisfaction data, as well as publicly available data sets. Topics include data organization, data sources available in the health services, conceptualizing analysis, sampling, data validity and reliability, qualitative and quantitative data analysis, applying research results, and communicating findings. prereq: 4520 or instructor consent, no grad credit
MATH 4180 - Solving Industrial Mathematics Research Problems
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
This course is intended for mathematics or statistics majors. The focus of the course is solving industrial mathematics research problems. Students will work in teams of three to five on a semester-long research problem from business, industry or government. Students will acquire specialized mathematical knowledge specific to the research problems posed for the semester. In addition, students will develop problem solving, teamwork, and communication skills as they design and implement a solution strategy for one of the research problems. A business, industry or government partner will serve as a liaison for project teams. Presentation to professional partners will occur throughout the semester. A final solution product will include oral, written and video presentations. pre-req: Minimum 2 courses in MATH or STATS at about about the 3xxx level, with a minimum of 3 credits each, instructor consent; no grad credit
MGTS 4825 - Human Resource Analytics
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Human Resource (HR) analytics is a sector within the field of human resource management that aims at using measurement and analysis techniques to understand, improve, and optimize the people side of the business. HR analytics adds value to businesses by improving vital decisions about talent and how it is organized in organizations. This course will teach the analytical foundations of HR decisions, the connections between data analytics and strategic HRM, and the applications of analytic logic and processes of various HR functions and workplace trends. Students will learn how to gather and analyze pertinent HR metrics and how to properly communicate findings to support HR decisions and drive organizational decisions. pre-req: MGTS 3801, LSBE candidate or HRM minor; no grad credit
MKTG 3731 - Sales Analytics: An Introduction to Sales Analysis Techniques and Applications
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Sales Analytics introduces students to the foundation metrics used in Business to Business and other sales environments. Students use excel to manage and summarize data sets, analyze product category and brand trends, and assess the impacts of various trade promotions. Students develop business insights from the data sets and use these insights to build compelling sales presentations. The course focuses on the use of data sets typical to consumer packaged goods industries but will also integrate data from other sources including: the US Census, other government surveys and Experian Simmons Oneview. pre-req: MKTG 3701 and Professional Sales Major
STAT 4050 - Introduction to Statistical Computing
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Statistical, graphical and numerical data analysis using modern statistical software. Database management and statistical modeling including regression and categorical data analysis. Topics in data generation and simulation. prereq: A grade of at least C- in STAT 3411 or 3611 or instructor consent.
STAT 4060 - Introduction to Biostatistics
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Introduction to statistical methods applicable to biological and biomedical data. Analysis of bioassay, case-control, and disease/expose data. Introduction to statistics in clinical trials. Use of regression and logistic regression in analyzing biological/biomedical data. Categorical data analysis with application to the life sciences. Basic survival analysis. prereq: Math 1290 or 1296 or 1596 and STAT 2411 or 3411 or 3611 with grade of C- or better or consent of instructor.