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Data Analytics Offerings

DATA 101 Introduction to Data Analytics (2 credits)
Students will develop their literacy with various types of data, while being introduced to the ethics of data analysis, data visualization, design, appropriate data visualization selection and gain cursory experience with the outputs of some data processes. No prerequisites.

DATA 201 Data Analytics I (2 credits and a lab)
Students will be introduced to programming in R & Python, engage in exploratory data analysis techniques, data visualization and the basics of data wrangling (data cleaning). Prerequisites: DATA 101, CSCI 110, and MATH 221 or BUAD 228.

DATA 202 Data Analytics II (2 credits)
Students will explore standard data modeling techniques including regression/curve fitting with python & R, further data visualization and engage in an analytics project using clean data. To be taken directly following DATA 201. Prerequisites: DATA 201.

DATA 256 People Analytics
This course is designed to introduce students to the fundamental methods necessary to conducting people analytics. Organizations are increasingly relying on people analytics to improve decision-making in human resources, and ultimately contribute more effectively to organizational effectiveness. HR practitioners need to be skilled in understanding (1) the types of problems that can be addressed using HR analytics, (2) how to analyze and interpret human resource data, (3) how to evaluate the validity of those analyses, and (4) how to communicate analytical and statistical results in a way to influence decisions. The course is designed to teach basic people analytics skills and critical thinking skills with respect to HR decision-making. The course will involve data analysis and statistics, but its emphasis is on application and real-world problem solving. Prerequisite: BUAD 232 or DATA 201.

DATA 301 Adv Techniques in Data Analysis
Students will learn advanced data wrangling techniques to acquire, clean and explore data using programming techniques along with other advanced analytics methodologies. Topics may include but are not limited to data mining, cleaning, validation, transformation as well as machine learning, cluster analysis, principal component analysis, gradient analysis and singular value decomposition. Prerequisites: DATA 202, CSCI 205 and MATH 203.

DATA 400 Data Practicum Capstone
Students will gain exposure to real world data analytics through the successful application of their theoretical and practical skills to solving problems in science and industry in this capstone course.This course focuses on the application of content learned throughout the major to a large-scale data project with an additional emphasis on ethics, social responsibility, and the communication of the results. Prerequisites: DATA 301 and CSCI 330.

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