Minor in Data Analytics
Data Analytics is a dynamic field in which new approaches, content, and techniques for manipulating and analyzing data are constantly being introduced to answer questions such as “what does social media tell us about mental health?” and “how much will the global temperature increase in ten years?”. Data Analytics uses tools from Computer Science and Statistics to analyze large amounts of raw data to discover valuable relationships and inform decisions. Data Analytics is an essential tool for any field that generates and consumes large amounts of data.
Arcadia University offers an interdisciplinary minor in Data Analytics open to students from all majors, although it is expected that most students will draw from Biology, Business Administration, Computer Science, Mathematics, Psychology, Physical Therapy, and Public Health. The Data Analytics minor will consist of four core Computer Science and Statistics courses as well as two electives.
Introduction to Data Mining
Focus on learning to manipulate and extract new information from large amounts of data. Topics will include data preprocessing and feature selection, decision trees, cluster analysis, classification, machine learning, evaluation and validation, as well as scalability. The course will illustrate these issues and techniques through the use of practical applications and examples taken from various domains, including biology, computer science, sociology, and economics.
Introduction to Data Science with Python
This course provides an introduction of data science and analytics techniques using Python. Students will learn the essential concepts of python programming. They will use python tools to perform data exploration, cleaning, manipulation and visualization. They will also learn basic statistics and machine learning techniques to conduct data analysis. Students from non-computer Science majors are welcome.
Gain knowledge in the utilization of genome databases/browsers and bioinformatic tools employed for gene model prediction (annotation), and use those tools to annotate sequences from various eukaryotic genomes.