Degrees and Certificates
Data Analytics,Associate in Science
DATA101N: Introduction to Data Analytics
In this course students receive an introduction to the tools and processes used by data analysts. This course gives an overview of the data life cycle including collecting, storing, formatting and preparing data. This course also provides an introduction to the ideas behind analyzing data in order to make data informed decisions. Additionally, this course will introduce how to communicate results through visualizations. A basic understanding of spreadsheets is recommended.
DATA105N: Data Mining
Students will learn how to consolidate data from multiple sources, mine relevant information from source data, and display and summarize information effectively. An important component of this course is the completion of an applied project utilizing a current business intelligent tool software such as Microsoft PowerBI.
DATA120N: Applied Data Analysis
In this course students will apply basic statistical and data mining techniques to work with large scale datasets. Examples of large scale data analysis in practice will be reviewed. Students learn to use statistical programming tools such as RStudio and SAS. Topics covered include descriptive statistics, statistical modelling, statistical inference and dimensionality reduction techniques on multivariate datasets. Students will complete and present an applied analytical project using statistical programming tools.
DATA205N: Data Visualization
In this course students will learn to apply design principles and techniques of effectively visualizing data. Students will develop an understanding of how visual representations are used in the analysis of complex real world data. Class projects will require students to practice creating and presenting interactive visualizations. A current data visualization tool such as Tableau will be utilized.
DATA220N: Introduction to Machine Learning
This course will provide a basic summary of skills that include: Machine learning concepts, techniques and procedures. Both supervised and unsupervised machine learning will be discussed. Students will develop an understanding of how machine learning is an integral part of data analytics. They will explore how machine learning helps to develop data-driven decisions and gives computers the ability to learn without being explicitly programmed. Previous programming experience is highly recommended.