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]

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 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

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 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)

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.