Data Science and Visualization Certificate - Elective

WFSC 223: Dealing With Data in the Wild

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.

BIOS/EPID 450: Health Data Acquisition and Assessment

BIOS 376: Introduction to Biostatistics

ISTA 322: Data Engineering

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.

ISTA 457: Neural Networks

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.

ISTA 355: Introduction to Natural Language Processing

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 sou

ISTA 331: Principles and Practice of Data Science

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 an

ISTA 421: Introduction to Machine Learning

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.

Subscribe to RSS - Data Science and Visualization Certificate - Elective