Certificate in Analytics

Intelligent Automation is changing the nature of work. Technology is enabling workforce automation and employee augmentation, aiming to automate repetitive tasks and reduce the number of lower value processes being performed manually. The goal for these technologies is to empower companies and their employees to invest their time in higher-yield, higher complexity tasks that can result in greater value for business, their customers and clients, and improve profits and efficiencies. Introduction to Intelligent Automation will explore the emergence and adoption of intelligent automation throughout various sectors. Fundamental concepts, vocabulary, automation tools and frameworks needed to identify, evaluate and measure opportunities will be introduced. Students will build simple automations using an automation tool.

This course will cover the basics of how to write computer programs in Python. Topics will include data types, loops, strings, lists, methods, graphics and GUI interfaces. Time permitting, we will discuss tools for data analysis. In class instruction will focus on case studies tackling business applications.

Industry's appetite for intelligent automation solutions grows as stakeholders recognize the power and value to these impressive tools. However, solution identification and deployment is not a simple matter. The appropriate analysis must be performed to identify the opportunity, document the process and data environment, craft the business solution and build a plan that minimizes risks. Historically, 50% of technology projects fail because this critical step is rushed, skipped or done badly. Students will learn the fundamentals of business analysis in the course from an industry best practice framework. By the end of class, students will have used key aspects of the framework and completed foundational business analysis templates.

In this course students will build upon their knowledge of automation development and business analysis. Students will learn more advanced automation skills, including effective development practices, preparing them to identify and build automations without instruction. How these automations are built matters, though, as building bunches of bad bots creates more problems for an organization than the automations may have solved. The concepts of Center of Excellence and governance practices will be introduced so students learn how business analysis and development practices contribute (positively or otherwise) to the overall transformation initiative of an organization.

Analytics is the process of getting value out of data. It explains how seemingly mundane data points, when combined, can provide insights into habits, processes, and patterns. We live in a world overflowing with data, and only recently has technology reached the point where anyone can conduct this type of analysis without the aid of supercomputers or consulting firms. This course covers "D2D", or Data to Decisions. Specifically, this means we will learn what to collect, how to collect it, and how to transform it into actionable information. It is also focused on solving real business problems, which is the most practical application for Nichols business students.

This hands-on course covers the art and science of data visualization. It explores various visualization techniques and the way that shape, size, color, orientation, and motion influence the way information is comprehended. The class will use world-class visualization platforms such as Tableau and PowerBI. Students will understand the difference between exploratory versus explanatory visualization as well as gain an appreciation for the appropriate use of visualization techniques. We'll discuss how visualizations can be manipulated to mislead or misinform others and discuss the ethics around data visualization. Students will work through a series of interactive dashboards culminating with the final project in which the student will create a story with their data.

The primary goal of this class is to learn principles and practices of database management and database design. We will discuss the database relational database design, normalization, SQL queries, reports and other interfaces to database data, and documentation. We will also treat ethical and privacy issues associated with database systems. In-class instruction and exercises will focus on the fundamentals for creating sophisticated, interactive, and secure database applications.

This course provides an introduction to regression including: simple linear regression, multiple regression, model building, and variable screening methods.

This course will introduce modern techniques of computational statistics for practical analysis of data utilizing the R programming language. Data analysis and interpretation will be emphasized, rather than statistical theory. Real world data sets will be used to illustrate statistical principles.