This course will introduce students to this rapidly growing field and equip them with some of its basic principles and tools as well as its general mindset. Students will learn the concepts, techniques and tools they need to deal with various facets of data science practice, including data collection and integration, exploratory data analysis, predictive modeling, descriptive modeling, data product creation, evaluation and effective communication. A laboratory component will be included.

Prerequisite: CSCI-208 or CSCI-218

Terms Typically Offered: On Demand.

Recommended CSCI-340. Data Mining studies algorithms and computational paradigms that allow computers to find patterns and regularities in databases, perform prediction and forecasting, and generally improve their performance through interaction with data. It is currently regarded as the key element of a more general process called Knowledge Discovery that deals with extracting useful knowledge from raw data. The knowledge discovery process includes data selection, cleaning, coding, using different statistical and machine learning techniques, and visualization of the generated structures. The course will cover all these issues and will illustrate the whole process by examples. Special emphasis will be given to the Machine Learning methods as they provide the real knowledge discovery tools. Important related technologies such as data warehousing and online analytical processing (OLAP), will be also discussed. The students will use recent Data Mining software.

Terms Typically Offered: Spring, even years.

Visualization is increasingly important in this era where the use of data is growing in many different fields. Data visualization techniques allow people to use their perception to better understand this data. The goal of this course is to introduce students to data visualization including both the principles and techniques. Students will learn the value of visualization, specific techniques in information visualiziation and scientific visualization, and understand how best to leverage visualization methods.

Prerequisite: DSCI-350

Terms Typically Offered: Fall, odd years.

Introduces advanced statistical concepts and analytical methods for the experimental needs and data encountered in computer, data and physical sciences. Experimental design/conduct, quantitative analysis of data, and statistical inferences and interpretations are studied for scientific hypothesis testing, as well as clinical trials. Explores methodological approaches to bioassay development/testing and provides a foundation for critically evaluating information to support research findings, product claims, and technology opportunities. Students apply statistical analysis software and write algorithms in programming languages commonly used in technology and professional science industries (ie. Python). Topics include statistical tools such as Bayesian statistics, Markov processes, and information theoretic indices.