Data Science and Visualization
Undergraduate Certificate
Quick Facts
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- Payscale, 2024
Expand your statistical and programming knowledge and familiarize yourself with tools necessary to succeed in today’s advanced data-rich landscape. The Data Science and Visualization Certificate will provide you with confidence and training in data collection, exploration, manipulation and storage, analysis, and presentation. You’ll gain hands-on experience working with real-world data sets drawn from science, social media and business.
The Data Science and Visualization Certificate is distinct in its accessibility serving a diverse student population. Technically-minded students will be trained in the nuances associated with successfully developing and communicating data methods and results for non-experts and the general public. Less technically-minded students will learn the basic skills necessary for gathering insights from data.
No more than six units may be shared with a degree requirement (major, minor, General Education) or second certificate. Information Science and eSociety students looking to develop their data science and technical skills may wish to use this course in conjunction with their degree by double-using ESOC 214 and ESOC 302.
*Residents of some U.S. Territories may not be eligible. Please see our Eligibility & State Authorization page for more information.
Students must complete four required courses in order to earn the certificate. This includes a choice between either ESOC 214 or ISTA 116, followed by ISTA 320, ISTA, and one additional elective.
Examine information management in the context of massive sets of data, gain proficiency with a variety of data analysis tools, and varied data platforms. Build skills and learn concepts related to data mining and statistical analysis. Particular attention will be given to toolkits embedded in R and other platforms.
Understand uncertainty and variation in modern data: data summarization and description, rules of counting and basic probability, data visualization, graphical data summaries, working with large data sets, prediction of stochastic outputs from quantitative inputs. Operations with statistical computer packages such as R.
Learn the fundamentals of data exploration data via visualizations, how to manipulate and reshape data to make it suitable for visualization, and how to prepare everything from simple single-variable visualizations to large multi-tiered and interactive visualizations. Visualization theory will be presented alongside the technical aspect of the course to develop a holistic understanding of the topic.
This course introduces students to the theory and practice of data mining for knowledge discovery. This includes methods developed in the fields of statistics, large-scale data analytics, machine learning, and artificial intelligence for automatic or semi-automatic analysis of large quantities of data to extract previously unknown and interesting patterns. Topics include understanding varieties of data, classification, association rule analysis, cluster analysis, and anomaly detection.
