Data Science and Visualization Certificate

This course will explore broad research paradigms and theoretical approaches that inform contemporary social research, varying study designs, as well as the systematic methods utilized in differing types of data analyses. Though this course will introduce research processes across the academic spectrum, quantitative analysis of both small and large data sets will be emphasized. Therefore, students will learn about basic statistical analyses and will be introduced to the emerging worlds of data science and social media analytics. Students will also consider related topics such as data visualization or research presentations.

This course will be inviting for a wide variety of students from across disciplines, and they will learn how to use industry standard tools and practices to make large data sets usable for scientists and other decision makers. From data collection and preparation, to the creation of big data stores, databases, or systems to make data flow, this course will focus on the practical work needed to prepare big data for analyses across contexts. Students will be introduced to a variety of technical tools for data management, storage, use, and manipulation.

This course surveys the techniques central to the modern practice of extracting useful patterns and models from large bodies of data and the theory behind these techniques.  Students will learn the purpose, power, and limitations of models, with concrete examples from business and science.  Course subject matter may include classification and regression, supervised segmentation and decision trees, similarity/distance metrics and recommender systems, clustering and nearest neighbors, support vector machines, understanding and avoiding overfitting, natural language processing and sentiment analysis, machine learning, neural networks, and AI, and logistic regression.

Natural language processing (NLP) is the study of how we can teach computers to use language by extracting knowledge from text, and then use that knowledge in some meaningful way.  In this introductory course, we will examine the fundamental components on which natural language processing systems are built, including frequency distributions, part of speech tagging, syntactic parsing, semantics and analyzing meaning, search, introductory information and relation extraction, and structured knowledge resources.  We will also examine pragmatic concerns in processing raw text from real-world sources.

Machine learning describes algorithms which can modify their internal parameters (i.e., "learn") to recognize patterns and make decisions based on examples or through interaction with the environment.  This course will introduce the fundamentals of machine learning, will describe how to implement several practical methods for pattern recognition, feature selection, clustering, and decision making for reward maximization, and will provide a foundation for the development of new machine learning algorithms.

Students will learn from experts from projects that have developed widely adopted foundational Cyberinfrastrcutrue resources, followed by hands-on laboratory exercises focused around those resources. Students will use these resources and gain practical experience from laboratory exercises for a final project using a data set and meeting requirements provided by domain scientists. Students will be provided access to computer resources at: UA campus clusters, iPlant Collaborative and at NSF XSEDE. Students will also learn to write a proposal for obtaining future allocation to large scale national resources through XSEDE.

Neural networks are a branch of machine learning that combines a large number of simple computational units to allow computers to learn from and generalize over complex patterns in data. Students in this course will learn how to train and optimize feed forward, convolutional, and recurrent neural networks for tasks such as text classification, image recognition, and game playing.

Do you want to live permanently on Antarctica? Now is your chance, apply for Mission Antarctica! The ice is melting, the penguins are marching; it seems like a perfect time to settle, but many challenges await. Data can help you live and thrive in this changing environment and not be eaten by a leopard seal. However, most of us do not know how to organize, analyze, and translate real-life data into decisions. In this class, we undergo a series of scenarios to teach you how to use data to design and evaluate if we are making a difference in our new society. These scenarios include case studies related to disease, food security, conservation, sustainability, and nutrition. Through a combination of lectures, hands-on problem solving, and collaboration, this course teaches introductory data literacy skills such as data management, analytics, and visualization useful for decision making and your careers. No programming experience is required and students are encouraged to have in class laptops for in-class activities and assignments. All readings and supplemental material are open source, or free to students. Most importantly, no penguins will be harmed in this adventure, we promise.