Information technologies have transformed the way businesses operate and the way consumers interact with businesses. They have enabled organizations to increase efficiency, reduce costs, and reach new customers. Their impact goes beyond the business world and affects nearly every aspect of modern society. Along with the benefits they provide, technologies have created new problems around privacy, security, misinformation on social media, algorithmic bias, and potential stifling of competition and innovation. In today's digital age, it is crucial to develop an understanding of information technologies, their impact on business and society, and the challenges they pose for decision making in commercial firms, government agencies, and public policies. This course is designed to cover a broad range of information technology issues in order to prepare students for the knowledge intensive economy of the 21st century. Students will be exposed to not only the technical aspects of information technologies, but also the social, political, and economic factors that shape its development and use. Through a combination of lectures, discussions, videos, in-class exercises and talks by guest speakers, students will gain an in-depth understanding of how information technologies are shaping businesses and the society as a whole. Students will also develop critical thinking skills to analyze and evaluate the impact of technology on society. Topics include business strategy and disruptive technologies, enterprise systems such as those for Customer Relationship Management, Supply Chain Management and Human Resource Management, electronic and mobile commerce, social media applications and their social impact, cloud computing, data analytics, IT privacy and security, artificial intelligence and its social impact.

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]

Standard statistical reasoning. Simple statistical methods. Social/physical sciences. Mathematical reasoning behind facts in daily news. Basic computing environment.

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

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

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

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: Undergraduates with strong math background are encouraged to register for 5811 in lieu of 3811 (Soc 5811 offered Fall terms only). Soc Majors/Minors must register A-F.

Random variables/probability distributions, statistical sampling/measurement, statistical inference, confidence intervals, hypothesis testing, single/multivariate regression, design of experiments. Applications to statistical quality control and reliability. prereq: MATH 1372 or equiv

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

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

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.

Random variables/distributions. Generating functions. Standard distribution families. Data summaries. Sampling distributions. Likelihood/sufficiency. prereq: Math 1272 or Math 1372 or Math 1572H

Estimation. Significance tests. Distribution free methods. Power. Application to regression and to analysis of variance/count data. prereq: STAT 4101

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), (MATH 2142 or CSCI 2033 or MATH 2373 or MATH 2243)

Sampling, sufficiency, estimation, test of hypotheses, size/power. Categorical data. Contingency tables. Linear models. Decision theory. prereq: [5101 or Math 5651 or instr consent]

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.

Random walks, Markov chains, branching processes, martingales, queuing theory, Brownian motion. prereq: 5651 or Stat 5101

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

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.

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

This is a 2-credit introductory course on financial reporting data analytics 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

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.

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.

This course introduces students to fundamentals of machine learning with a strong focus on communicating insights from data analysis and analytics. It is designed to provide students with opportunities to develop data processing, analysis, and visualization skills by taking a data-driven approach to HR?s impact on the business, with a topical focus on diversity, equity and inclusion. Students will learn how to effectively communicate insights from data analysis and analytics through streamlined storytelling presentations aimed to provide compelling recommendations to decision makers. Students will be given the opportunity to use Excel and/or Tableau, and will also be introduced to predictive analytics software. Prerequisites: HRIR3021 or HRIR3021H or IBUS 3021 and SCO 2550 or BA 2551 or equivalent statistics course

Concepts for designing, using, and implementing database systems. Normalization techniques. Structured Query Language (SQL). Analyzing a business situation. Building a database application.

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.

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.

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 BA 2551 or equivalent course. We also recommend a previously taken class with Excel, R, SAS, SQL, or Access.

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

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)

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)