DEVELOP APPLICABLE SKILLS, NOT JUST THEORY.

Foundations of Data Management course provides students exposure to fundamental data management skills used in modern information systems that support various operational and functional areas within a business organization. Topics covered in the course have an emphasis on how data is fundamentally identified, organized, described and managed as the most valued asset within an organization. Course emphasis is also on applied learning of concepts and skills for relational data modeling and querying. This course will help prospective Data Science and Analytic business professionals to develop and apply data management skills that will be essential to the success in subsequent coursework.

This course provides students with a foundation in basic statistical analysis, focusing primarily on descriptive univariate statistics. Topics addressed in this course include variables and their properties, measurement scales, descriptive analyses of continuous and categorical variables, Central Limit Theorem, univariate and bivariate estimation, and hypothesis testing logic and procedures. Students will be exposed to hands-on computational examples using R and SPSS as they learn how to apply the various statistical concepts covered in this course to real-life business situations.

This course introduces students to the fundamentals of exploratory data analyses, broadly defined here as review of the available/focal data, and extraction of descriptive characteristics with the goal of generating valid and reliable insights. The course covers the basic data due diligence and curation considerations, key data preparatory steps, and offers an overview of a general descriptive data analytical framework. Analytic approach-wise, the course addresses analyst-led exploration utilizing classical statistical techniques, as well as automated data mining applications, while addressing topics of statistical inference, statistical significance, and outcome validity and reliability. Lastly, the course combines conceptual overview of the focal concepts and statistical reasoning, while also providing hands-on introduction to the practical side of data exploration. The general instructional approach used in this course is one that casts exploratory data analyses in the context of the data –> information –> knowledge continuum that underpins extraction of decision-guiding insights out of the available data as a way of answering business questions.

This course will offer Business Analytics students with a broad base introduction to multivariate statistical methods, with particular emphasis on explanatory and predictive techniques. The commonly used dependence and interdependence techniques are discussed, including linear and logistic regression, decision trees and select machine learning approaches. The course is focused particularly on topics relating to technique selection, result interpretation and translation of analytic outcome into decision-guiding insights.

As data analytic technologies became more advanced, it became progressively easier and easier to execute sophisticated analyses; also, as the volume of the available data exploded, more and more analyses began making use of very large sample sizes. Those trends have a direct impact on the validity and reliability of outcomes of statistical analyses, the investigation of which is the focus of this course. More specifically, in this course the students will take a closer look at topics such as the impact of sample size on statistical significance, and the relationship between practical materiality of findings and the more abstractly framed statistical significance.

This course focuses on the effective communication of data analysis and its insights and implications. Students will learn the principles and techniques for information visualization and representation as well as verbal and written communication. Students will develop proficiency in several of the latest tools for visualization. Students will use real-world business scenarios to gain experience designing and building data visualization and communication.  Best practices will be highlighted and students will receive tailored individual coaching and feedback sessions to accelerate skill improvement.

This course introduces students to the current and emerging topics and considerations addressing the issue of data governance, usage and security; furthermore, this course also investigates the ethical and legal right and responsibilities of data analysts, and delves into questions that emerge from developing, storing, analyzing and using data.  Issues including intellectual property, data ownership, storage security and safeguards as well as the human impact of using data are investigated; case studies and scenario explorations in a range of industries from consumer products and retail to government and social justice are used to illustrate and apply the concepts discussed in class, with the goal of preparing students to manage these often grey issues during their career.

Digital marketing including social media and web-based advertising have fundamentally transformed how goods and services are marketed and sold today. The strategic aspects, performance metrics and business value of social media and digital marketing analytics will be explored. Each student will develop a capstone project that will include collecting, analyzing and developing insights from social media and/or digital marketing data in an industry and business of interest to them. Faculty will work to pair students with partner companies to supply data and business challenges to use as the foundation of the capstone project. Students will be required to deliver their analysis and communicate their results to their partner company sponsor as well as their faculty and peers.