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 2019
  • Required credits in this minor: 18
  • 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 - Introduction to Information Technology in Business (3.0 cr)
Business Statistics: Data Sources, Presentation, and Analysis
SCO 2550 - Business Statistics: Data Sources, Presentation, and Analysis (4.0 cr)
or STAT 3011 - Introduction to Statistical Analysis [MATH] (4.0 cr)
or STAT 3021 - Introduction to Probability and Statistics (3.0 cr)
or STAT 3022 - Data Analysis (4.0 cr)
or PSY 3801 - Introduction to Psychological Measurement and Data Analysis [MATH] (4.0 cr)
or SOC 3811 - Social Statistics [MATH] (4.0 cr)
or IE 3521 - Statistics, Quality, and Reliability (4.0 cr)
or EE 3025 - Statistical Methods in Electrical and Computer Engineering (3.0 cr)
or CEGE 3102 - Uncertainty and Decision Analysis (3.0 cr)
or ANSC 3011 - Statistics for Animal Science (4.0 cr)
or STAT 4101 - Theory of Statistics I (4.0 cr)
STAT 4102 - Theory of Statistics II (4.0 cr)
or STAT 5101 - Theory of Statistics I (4.0 cr)
STAT 5102 - Introduction to Statistical Learning (4.0 cr)
or MATH 5651 - Basic Theory of Probability and Statistics (4.0 cr)
MATH 5652 - Introduction to Stochastic Processes (4.0 cr)
Minor Requirements
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:
· 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)
· 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)
· FINA 5422 - Financial Econometrics and Computational Methods I (2.0 cr)
· FINA 5423 - Financial Econometrics and Computational Methods II (2.0 cr)
 
More program views..
View college catalog(s):
· Curtis L. Carlson School of Management


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

Search University Catalogs
Related links.

Curtis L. Carlson School of Management

TC Undergraduate Admissions

TC Undergraduate Application

One Stop
for tuition, course registration, financial aid, academic calendars, and more
 
IDSC 3001 - Introduction to Information Technology in Business
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall, Spring & Summer
Developing/using IS to support business processes, managerial decision making, and organizational strategy. Technology components of IS. Impact on organizations. Creation/change processes. Managerial issues. Techniques for designing, developing, and implementing IS. Databases and user interfaces. Computer/communications network platforms. Internet, e-business, and e-commerce applications.
SCO 2550 - Business Statistics: Data Sources, Presentation, and Analysis
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall, Spring & Summer
Data analysis, basic inferential procedures, statistical sampling/design, regression/time series analysis. How statistical thinking contributes to improved decision making. prereq: [Math 1031 or equiv], at least 30 cr
STAT 3011 - Introduction to Statistical Analysis (MATH)
Credits: 4.0 [max 4.0]
Course Equivalencies: (Select a set)
Typically offered: Every Fall, Spring & Summer
Standard statistical reasoning. Simple statistical methods. Social/physical sciences. Mathematical reasoning behind facts in daily news. Basic computing environment.
STAT 3021 - Introduction to Probability and Statistics
Credits: 3.0 [max 3.0]
Typically offered: Every Fall, Spring & Summer
This is an introductory course in statistics whose primary objectives are to teach students the theory of elementary probability theory and an introduction to the elements of statistical inference, including testing, estimation, and confidence statements. prereq: Math 1272
STAT 3022 - Data Analysis
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Practical survey of applied statistical inference/computing covering widely used statistical tools. Multiple regression, variance analysis, experiment design, nonparametric methods, model checking/selection, variable transformation, categorical data analysis, logistic regression. prereq: 3011 or 3021 or SOC 3811
PSY 3801 - Introduction to Psychological Measurement and Data Analysis (MATH)
Credits: 4.0 [max 4.0]
Course Equivalencies: 01316 - Psy 3801/Psy 3801H
Typically offered: Every Fall, Spring & Summer
Descriptive/basic inferential statistics used in psychology. Measures of central tendency, variability, t tests, one-way ANOVA, correlation, regression, confidence intervals, effect sizes. Psychological measurement. Graphical data presentation. Statistical software. prereq: High school algebra, [PSY 1001 or equiv]; intended for students who plan to major in psychology
SOC 3811 - Social Statistics (MATH)
Credits: 4.0 [max 4.0]
Course Equivalencies: 02148 - Soc 3811/Soc 5811
Typically offered: Every Fall & Summer
This course will introduce majors and non-majors to basic statistical measures and procedures that are used to describe and analyze quantitative data in sociological research. The topics include (1) frequency and percentage distributions, (2) central tendency and dispersion, (3) probability theory and statistical inference, (4) models of bivariate analysis, and (5) basics of multivariate analysis. Lectures on these topics will be given in class, and lab exercises are designed to help students learn statistical skills and software needed to analyze quantitative data provided in the class. prereq: Credit will not be granted if credit has been received for Soc 5811 (Soc 5811 offered Fall terms only). Undergraduates with strong math background are encouraged to register for 5811 in lieu of 3811. Soc Majors/Minors must register A-F.
IE 3521 - Statistics, Quality, and Reliability
Credits: 4.0 [max 4.0]
Typically offered: Every Fall, Spring & Summer
Random variables/probability distributions, statistical sampling/measurement, statistical inferencing, confidence intervals, hypothesis testing, single/multivariate regression, design of experiments, statistical quality control, quality management, reliability, maintainability. prereq: MATH 1372 or equiv
EE 3025 - Statistical Methods in Electrical and Computer Engineering
Credits: 3.0 [max 3.0]
Typically offered: Every Fall, Spring & Summer
Notions of probability. Elementary statistical data analysis. Random variables, densities, expectation, correlation. Random processes, linear system response to random waveforms. Spectral analysis. Computer experiments for analysis and design in random environment. prereq: [3015, CSE upper division] or instr approval
CEGE 3102 - Uncertainty and Decision Analysis
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Stochastic models, their usefulness in reasoning about uncertainty in civil, environmental, and geo-engineering. Techniques for identifying, fitting, and validating models using data samples. Testing hypotheses about, and bounding uncertainty attached to, engineering parameters. Applications to civil, environmental, and geo-engineering. prereq: MATH 1372 or equiv
ANSC 3011 - Statistics for Animal Science
Credits: 4.0 [max 4.0]
Course Equivalencies: 00258 - AnSc 3011/ESPM 3012/Stat 3011/
Typically offered: Every Fall & Spring
Basic statistical concepts. Develop statistical reasoning/critical thinking skills. Descriptive statistics, probability, sampling and sampling distributions, hypothesis testing, experimental design, linear correlation, linear regression and multiple regression. How to make sound arguments/decisions based on statistics when reviewing news articles or scientific publications with statistical content. Explore/draw conclusions from data using a basic statistical software package.
STAT 4101 - Theory of Statistics I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Random variables/distributions. Generating functions. Standard distribution families. Data summaries. Sampling distributions. Likelihood/sufficiency. prereq: Math 1272 or Math 1372 or Math 1572H
STAT 4102 - Theory of Statistics II
Credits: 4.0 [max 4.0]
Course Equivalencies: 00260
Typically offered: Every Spring
Estimation. Significance tests. Distribution free methods. Power. Application to regression and to analysis of variance/count data. prereq: STAT 4101
STAT 5101 - Theory of Statistics I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Logical development of probability, basic issues in statistics. Probability spaces. Random variables, their distributions and expected values. Law of large numbers, central limit theorem, generating functions, multivariate normal distribution. prereq: [Math 2263 or Math 2374 or Math 2573H], [CSCI 2033 or Math 2373 or Math 2243]
STAT 5102 - Introduction to Statistical Learning
Credits: 4.0 [max 4.0]
Course Equivalencies: 00260
Typically offered: Every Spring
Sampling, sufficiency, estimation, test of hypotheses, size/power. Categorical data. Contingency tables. Linear models. Decision theory. prereq: [5101 or Math 5651 or instr consent]
MATH 5651 - Basic Theory of Probability and Statistics
Credits: 4.0 [max 4.0]
Course Equivalencies: 00259 - MATH 4653/Math 5651/Stat 5101
Typically offered: Every Fall & Spring
Logical development of probability, basic issues in statistics. Probability spaces, random variables, their distributions/expected values. Law of large numbers, central limit theorem, generating functions, sampling, sufficiency, estimation. prereq: [2263 or 2374 or 2573], [2243 or 2373]; [2283 or 2574 or 3283] recommended.
MATH 5652 - Introduction to Stochastic Processes
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Random walks, Markov chains, branching processes, martingales, queuing theory, Brownian motion. prereq: 5651 or Stat 5101
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
Data mining/personalization techniques. Exploratory/ predictive data mining techniques. Data preparation, data visualization, online analytical processing (OLAP), recommender systems. How business analytics techniques are applied in variety of business applications/organizational settings. prereq: 3001
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.
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 SCO 2550 or equivalent statistics course; OR CSOM Major OR NonMajor OR Acct Cert, Housing Studies, Retail Merch, Bio Prod (Mktg subplan), OR Journalism
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 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 3010
FINA 5422 - Financial Econometrics and Computational Methods I
Credits: 2.0 [max 2.0]
Course Equivalencies: 02503
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: 02504
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.