Data analysis skills are widely sought by employers, both nationally and internationally. This specialization is ideal for anyone interested in data analysis for improving quality and processes in business and industry. The skills taught in this specialization have been used extensively to improve business performance, quality, and reliability.

By completing this specialization, you will improve your ability to analyze data and interpret results as well as gain new skills, such as using RStudio and RMarkdown. Whether you are looking for a job in data analytics, operations, or just want to be able to do more with data, this specialization is a great way to get started in the field.

Learners are encouraged to complete this specialization in the order the courses are presented.

This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://hua.dididi.sbs/degrees/master-of-science-data-science-boulder.

应用的学习项目

Learners develop an understanding of how to manage, describe, and analyze continuous and discrete data using examples from business and industry. They explore how to assess processes for sources of variation through time as well as determine process capability with respect to customer requirements. Learners gain familiarity with the analysis procedures to assess measurement systems for continuous and discrete data in order to make decisions regarding the capability and acceptability of the measurement system. Assignments require learners to perform analyses for various data types and scenarios, interpret results, and make appropriate decisions.