Business Analytics Certificates

Data analysis is rapidly becoming a required skill set for today’s managers in competitive environments. As a decision-maker in business, you are likely to be asked to conduct your own analysis of data or to interpret a report that has been derived by others. In this course, you will have the opportunity to learn how to summarize, visualize, and manage data within software environments that are commonly used in business today. Additionally, techniques that help decision-makers reduce risk and identify opportunities that would provide an individual or a company with a competitive advantage will be reviewed.

Predictive analytics encompasses a variety of statistical and machine learning techniques and applications within a business environment. The primary goal of predictive analytics is to discover and apply relationships found within historic datasets to make predictions about the future or otherwise unknown events. In this hands-on course, students will be introduced to concepts related to constructing, testing, and applying quantitative models in various business settings. From this perspective, students will utilize major software tools to conduct an analysis of continuous, classification, and clustering models. Upon completion of this course, students will gain insight into how models are constructed and how predictive models can improve business.

Some refer to analytics as the new science of winning. It refers to a commitment by top management to the extensive use of data, statistical and quantitative analysis, explanatory and predictive models and fact-based management to drive decisions and actions. This course provides an introduction to analytics and covers spreadsheet modeling for decision-making. It employs techniques from the classical disciplines of statistics and operations research, as well as more recently developed methodologies: data mining, executive information systems, digital dashboards, and online analytical processing. You will be expected to master, at an introductory level, techniques that are at the heart of the competitive posture of many successful organizations.

This course provides a broad overview of business intelligence and data management including database fundamentals, business intelligence approaches, data management/data governance strategies, data mining, and other business/data analytics techniques. Our primary emphasis will be on the managerial perspective, focusing on how you can design, implement, and leverage business intelligence systems and strategies in a management role.

In this course, students will be introduced to programming concepts that are used in business analytics. Understanding the fundamentals of programming is an essential skill set to implement solutions that are commonly needed in business practices. Common scenarios include the automation of processing data sets, applying methods, and combining techniques for creating solutions to solve business problems.

This course requires students to apply advanced software tools that are commonly used in businesses for advanced modeling practices and visualization techniques. In particular, students will explore advanced techniques for pre- and post-processing datasets. Students will apply advanced visualization techniques to understand the data that they are investigating and to enhance their ability to communicate the results and implications of predictive models. Students are expected to demonstrate the skills learned in this class with a course project. The course project not only allows a student to improve problem-solving skills, but it can also enhance the students’ ability to communicate their findings and recommendations in a business context.

Data is often either structured or unstructured. Unstructured data presents a challenge because standard techniques commonly used in business analytics cannot be used to discover meaningful patterns, relationships, and other insights that could be useful when solving business problems. In this course, students will learn advanced techniques while using software that is commonly used in practice to overcome the problems associated with unstructured data.