
Overview
Data analysis skills are widely sought by employers, both nationally and internationally. This Data Science Methods for Quality Improvement course offered by Coursera in partnership with University of Colorado Boulder 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.
Applied Learning Project
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.
What You Will Learn:
- Manage, describe, and analyze data using applied statistics
- Analyze measurement systems to ensure their stability and capability
- Apply continuous and/or discrete data methods for process analysis, improvement, and ongoing management in a business or workplace
Skills You Will Gain:
- Data Science
- Data Analysis
- Process Engineering
- Rstudio
- Quality Improvement
Get more details
Visit programme websiteProgramme Structure
Courses include:
- Managing, Describing, and Analyzing Data
- Stability and Capability in Quality Improvement
- Measurement Systems Analysis
Check out the full curriculum
Visit programme websiteKey information
Duration
- Part-time
- 1 months
- Flexible
Start dates & application deadlines
Language
Delivered
Disciplines
Data Science & Big Data Operations and Quality Management View 578 other Short Courses in Data Science & Big Data in United StatesExplore more key information
Visit programme websiteAcademic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
Intermediate level
- Recommended experience: Familiarity with RStudio and applied statistics is recommended.
Make sure you meet all requirements
Visit programme websiteTuition Fee
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International
FreeTuition FeeBased on the tuition of 0 USD for the full programme during 1 months. -
National
FreeTuition FeeBased on the tuition of 0 USD for the full programme during 1 months.
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Funding
Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project.