Probability, measurement, random variables, distributions, central tendency, variability, confidence intervals, measures of uncertainty, estimation, prediction, hypothesis testing and inference, replicability, power, effect size, t-tests, correlation, univariate ANOVA, and simple linear regression. Introduction to descriptive and inferential statistics using computational and resampling approaches from data science. Introduction to StatisticsĭS 2100 Statistics for Data Science. Time and memory complexity dynamic memory structures sorting and searching advanced programming and program-solving strategies efficient software library use. Data structures and their associated algorithms in application to computational problems in science and engineering. Prerequisite: CS 1101 (in Java) or 1104 (in Python).ĬS 2204 Program Design and Data Structures for Scientific Computing (in Python). The study of elementary data structures, their associated algorithms and their application in problems rigorous development of programming techniques and style design and implementation of programs with multiple modules, using good data structures and good programming style. Note: DS / CS 1100 will be taught for the first time in the Fall 2021 semester.ĬS 2201 Program Design and Data Structures (in C++). Intended for students other than computer science and computer engineering majors. Scalar, vector, and matrix computations for scientific computing and data science. Computer ProgrammingĭS 1100 / CS 1100 Applied Programming and Problem Solving with Python. Note: DS 1000 will be taught for the first time in the Fall 2021 semester students can substitute HOD 3200 Introduction to Data Science or PSCI 2300 Introduction to Data Science for Politics with permission from the Director of Undergraduate Data Science. Topics introduced with real-world datasets using a statistical programming language for hands on experience in data science. ![]() ![]() Data summary and data visualization causality and correlation sampling, resampling, and uncertainty prediction with linear regression classification, clustering, and machine learning ethics. Accessible, engaging, applied introduction to data science for students from all colleges and majors. Introduction to Data ScienceĭS 1000 Data Science: How Data Shape Our World. NOTE: Check YES for the most up-to-date course descriptions, prerequisites, exclusions, credit hours, and (for A&S) AXLE categories. On YES, to select all courses approved for credit in the Data Science minor, select the “Advanced” link next to the search box, select the “Class Attributes” drop-down box on the bottom right of the advanced search page, and then select “Eligible for Data Science” to find all courses. Course Descriptions Finding Eligible Data Science Courses
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |