Twin Cities campus
 
Twin Cities Campus

Business Analytics Minor

Information & Decision Sciences
Curtis L. Carlson School of Management
  • Program Type: Undergraduate free-standing minor
  • Requirements for this program are current for Fall 2022
  • Required credits in this minor: 19
  • No
The business analytics minor is available to degree-seeking students admitted to the Carlson School of Management at the University of Minnesota. The minor provides an opportunity for students specializing in one of the functional areas in business to gain additional skills that will prepare them for data-driven and analytics-based decision making. Students undertaking this minor will be exposed to courses in descriptive, predictive, and prescriptive analytics. Students will also be able to take electives that will apply analytic tools specialized to various functional areas like finance, marketing, and information systems. Graduates will be prepared to interact with specialized data scientists and bring the insights from the large amounts of data being produced in the market place to their functional areas. As business analytics emerges in the market across a variety of functional areas (information systems, marketing, finance, human capital, etc.), the demand for this skill set is envisioned to cut across all undergraduate business majors, making a minor in business analytics paired with a functional major ideal.
Program Delivery
This program is available:
  • via classroom (the majority of instruction is face-to-face)
Admission Requirements
This minor is only available to students who are pursuing a BSB degree from the Carlson School of Management.
For information about University of Minnesota admission requirements, visit the Office of Admissions website.
Required prerequisites
Prerequisites
IDSC 3001 - Information Systems & Digital Transformation (3.0 cr)
BA 2551 - Business Statistics in R [MATH] (4.0 cr)
Minor Requirements
A minimum of 3 credits in the minor must be taken at the University of Minnesota Twin Cities campus.
Minor Requirements
MKTG 3005 - Introduction to Applying Analytical Tools for Solving Business Problems (2.0 cr)
IDSC 4110 - Data Engineering for Business Analytics (2.0 cr)
IDSC 4444 - Descriptive and Predictive Analytics (2.0 cr)
Take 6 or more credit(s) from the following:
· ACCT 5141 - Financial-Data Analytics (2.0 cr)
· FINA 5422 - Financial Econometrics and Computational Methods I (2.0 cr)
· FINA 5423 - Financial Econometrics and Computational Methods II (2.0 cr)
· HRIR 3111 - Human Resource Analytics (2.0 cr)
· IDSC 3103 - Data Modeling and Databases (2.0 cr)
· IDSC 4210 - Interactive Data Visualization for Business Analytics (2.0 cr)
· IDSC 4310 - Prescriptive Analytics (2.0 cr)
· IDSC 4590 - Business Analytics Special Topics (2.0 cr)
· MILI 3963 - Health Market Analytics (3.0 cr)
· MKTG 4072 - Marketing-in-Action: Marketing Practicum (4.0 cr)
· MKTG 4074 - Data-Driven Marketing (4.0 cr)
· MKTG 4076 - Digital Marketing (2.0 cr)
 
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· Curtis L. Carlson School of Management


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IDSC 3001 - Information Systems & Digital Transformation
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall, Spring & Summer
Digital transformation through new technologies such as artificial intelligence, enterprise systems, electronic commerce, Internet of things, social media; IT strategy and data-driven decision making; privacy and security issues related to the Internet; a must take for students who want to be prepared for the rapidly changing technological landscape as successful professionals
BA 2551 - Business Statistics in R (MATH)
Credits: 4.0 [max 4.0]
Course Equivalencies: BA 2551/SCO 2550
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
The purpose of the course is to develop skills for improving data-driven decision-making using statistical techniques in the powerful statistical software environment R. As an introductory statistics course, the content will include three main areas of statistics: Descriptive Statistics, Statistical Inference, and Analysis of Relationships with Scatterplots, Correlation and Linear Regression. Developing statistical literacy is increasingly important in understanding data and engaging in the complex business world. BA 2551 focuses on statistical reasoning and how to implement statistical methods in a business context using R. Topics include (but are not limited to) descriptive statistics, statistical inference, variability, sampling, distributions, correlation analysis, confidence intervals, hypothesis testing, graphical summaries of data, and introduction to linear regression. Through weekly in-class lab sessions and critical thinking assignments related to statistics in business, the course will train students to become informed consumers of numerical information and provide foundational skills in R to compute statistical procedures in future courses. We use existing packages in R as a tool to enable us to solve business problems that can leverage mathematical and statistical thinking. prereq: [Math 1031 or equiv]
MKTG 3005 - Introduction to Applying Analytical Tools for Solving Business Problems
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Fall
The ability to make intelligent business decisions based on large data and information is becoming increasingly important for businesses and managers. This course provides a practitioner-oriented introduction of applying analytical tools in business setting. This class entails hands-on computer exercises on real data sets to apply various analytical techniques in common business applications. This course assumes that students have knowledge of fundamental analytical tools and statistical methods. The class emphasizes understanding model assumptions to help students with appropriate model selection; interpreting results in order to make optimal business decisions; designing experiments in a business setting and analyzing the experimental data to advance business objectives. prereq: SCO 2550 or equivalent statistics course
IDSC 4110 - Data Engineering for Business Analytics
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Fall
Modern organizations increasingly base their decisions on data which is becoming more abundant by each day. The first step of using data for decision making is to prepare data in a suitable format for analysis, a step commonly known as data engineering. Typical data engineering tasks may include data acquisition, parsing, handling missing data, summarization, augmenting, transformation, subsetting, sampling, aggregation, and merging. Data engineers also frequently use basic data visualization tools to detect and fix data issues. Most recently, there is increasing demand for data engineers to handle big data and unstructured data. A good data engineering process ensures quality, reliability, and usability of data. In fact, data engineering is such a critical and time-consuming step of data-driven decision making that many data scientists and analysts spend more than 60% of their time doing data engineering related tasks.
IDSC 4444 - Descriptive and Predictive Analytics
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Spring
Descriptive and Predictive Analytics exposes students to a number of data mining and machine learning methods, including: exploratory methods (such as association rules and cluster analysis), predictive methods (such as K-NN and decision trees), and text mining methods. The course combines theoretical lectures with lab lectures, where the methods are practically implemented using the software R. prereqs: IDSC 3001; non-MIS majors also need IDSC 4110
ACCT 5141 - Financial-Data Analytics
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Fall & Spring
This is a 2-credit undergraduate-level financial reporting data analytics course for Carlson students. The main learning objective is to introduce students specializing in business (accounting, auditing, tax, finance, marketing, operations, etc.) to data analytics, providing them the necessary knowledge and tools needed to effectively use data analytics in their specialized domain. The goal is thus for students to be able to consume and use available data analytics technologies to complement existing technical skills, rather than to train "data analytics specialists" (although this class is a good jumping-off point for students who wish to pursue a career specializing in data analytics!). Prior coding experience is thus not required, although students should have completed business statistics (SCO 2550 or BA 2551 or equivalent statistics course). After a general overview of data analytics and machine learning, we will dive into the ETL (extract, transform, load) process, covering topics and showcasing applications such as data joins, variable types, formulas, and regular expressions. We will then explore data visualization tools (including pivot tables and dashboards) and conclude the term by modeling data to create business insights via predictions. Students will gain hands-on experience using state-of-the-art data analytics tools and will learn how to conduct basic SQL queries. Students will improve their quantitative and problem-solving skills and learn how to apply scientific research methods to answer questions, present solutions, and discuss limitations. An emphasis will be placed on financial reporting datasets/applications, although the methods and concepts covered are applicable to other business settings/functions. Ultimately, students will enhance their analytical skills and achieve a deeper understanding of issues related to financial reporting specifically and business more generally. prereq: SCO 2550 or BA 2551 or equivalent statistics course and Acct 2050 or 2051
FINA 5422 - Financial Econometrics and Computational Methods I
Credits: 2.0 [max 2.0]
Course Equivalencies: Fina 5422/MSF 6422
Grading Basis: A-F only
Typically offered: Every Fall
This course provides an introduction to the methods used in empirical finance. A review of statistics is followed by intensive instruction on matrix algebra that culminates in a fundamental understanding of linear regression, the basic empirical tool. Asset pricing theories are discussed and developed and then methods are derived to test them. The course will emphasize estimation and inference using computer-based applications.
FINA 5423 - Financial Econometrics and Computational Methods II
Credits: 2.0 [max 2.0]
Course Equivalencies: Fina 5423/MSF 6423
Grading Basis: A-F only
Typically offered: Every Fall
This course builds on Financial Econometrics I and provides instruction on the econometrics used in empirical finance. Topics will include time series analysis, parametric models of volatility, evaluation of asset pricing theories, and models for risk management. The course will emphasize estimation and inference using computer-based applications.
HRIR 3111 - Human Resource Analytics
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Spring
This course is designed to provide students with opportunities to develop data manipulation, analysis, and visualization skills using Excel and Tableau by taking a data driven approach to core HR areas, including recruitment, compensation, talent development, and turnover. In the course students will get exposure to working with a variety of data sources, including administrative records, survey, and text-based data from resumes and interviews as well as an introduction to predictive analytics tools. Emphasis will be placed on data visualization and communication of findings to practitioners. Prerequisites: HRIR3021 or HRIR3021H or IBUS 3021 and SCO 2550 or equivalent statistics course
IDSC 3103 - Data Modeling and Databases
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Concepts for designing, using, and implementing database systems. Normalization techniques. Structured Query Language (SQL). Analyzing a business situation. Building a database application.
IDSC 4210 - Interactive Data Visualization for Business Analytics
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Fall
IDSC 4210 is an elective course for the undergraduate Business Analytics minor at the Carlson School of Management. It focuses on the fundamental and widely used exploratory data analysis technique of interactive visualization that is integral to modern business analytics. The key goal of this course is to prepare students for the rapidly changing digital environment faced by companies as it pertains to data-driven decisions. The students will also have hands?on experience with interactive data visualization using modern, state-of-the-art software on real-world datasets.
IDSC 4310 - Prescriptive Analytics
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Spring
Prescriptive Analytics answer the question "What should I do?" This class of analytical techniques focuses on moving beyond simply analyzing the data to providing an optimal action plan. Prescriptive techniques combine learnings from the descriptive and predictive disciplines with a new layer of insight and computer algorithms that suggests an action plan rather than just describing the data or predicting what might happen. prereq: IDSc 4110 & 4210 recommended.
IDSC 4590 - Business Analytics Special Topics
Credits: 2.0 [max 6.0]
Grading Basis: A-F only
Typically offered: Every Spring
Discussion and analysis of current topics and developments in business analytics.
MILI 3963 - Health Market Analytics
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Spring
This course prepares students to analyze large health care databases with a focus on advanced applications with health insurance claims data. The course is designed to be a STEM offering with the use of statistical programming languages including R, Tableau, and SAS. This course is designed to appeal to students with an interest in developing data science as core skill and already have knowledge of some programming tools, and experience with data manipulation in Excel, SQL, or Access. Prerequisite: We recommended that students have a background in statistics. Consider MKTG 3005 - or STAT 3011 or equivalent course. We also recommend a previously taken class with Excel, R, SAS, SQL, or Access.
MKTG 4072 - Marketing-in-Action: Marketing Practicum
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
This course focuses on what marketers do in real-life. Each week begins with guidance on approaching a typical task, then developing recommendations by working in groups during class with ongoing feedback from the instructor, and concluding with a short presentation. Weekly topics may include identifying marketing challenges (ala Shark Tank), segmenting customers, pricing a product, and developing an advertising plan. The course concludes with a multi-week, interactive simulation in which students compete in groups as they manage a product. prereq: MKTG 3001 and BA 2551 or SCO 2550 or equivalent statistics course
MKTG 4074 - Data-Driven Marketing
Credits: 4.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Spring
This course emphasizes various analytical techniques and statistical models with hands-on applications of marketing data and software tool kits. The course will cover classic marketing topics such as segmentation, positioning, new product development, advertising, and pricing. It will focus on how to choose and apply the most effective statistical tool to analyze questions on marketing topics and then translate the information from analysis into data-driven decisions. The goal is to increase students' comfort level of analyzing large marketing databases and help understand how a scientific approach can enhance marketing decision making by converting data into insights. prereq: Mktg 3011 (or 3010)
MKTG 4076 - Digital Marketing
Credits: 2.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Spring
The Internet and digital technologies have continued to alter the way consumers search information, make transactions, and share experiences, as well as the way firms market towards and engage with consumers. In today's digital era, it is imperative for marketers to understand how to gain a competitive edge by leveraging digital media to set targeting strategies and implement the marketing mix. This course will provide a structured framework to introduce students to the most up-to-date tactics, applications, and trends in digital marketing. The course is organized around three main sections developed by the instructor: - Internet marketing, which explores the impact of Internet on (1) consumer behaviors and (2) advertising strategies. - Social marketing, which focuses on (1) the formation of online social networks and (2) social media analytics. - Mobile marketing, which examines (1) location-based targeting and (2) the management of omni-channel marketing. prereq: Mktg 3011 (or 3010)