Statistical Inference for Estimation in Data Science

Statistical inference, sampling distributions, and confidence intervals are all covered in this course. Students will learn how to define and construct good estimators, as well as the method of moments estimation, maximum likelihood estimation, and confidence interval construction methods that can be applied to a variety of situations.

This course is part of CU Boulder's Master of Science in Data Science (MS-DS) program, which is available on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from the Applied Mathematics, Computer Science, Information Science, and other departments at CU Boulder. 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, as it is based on performance rather than application.

This course offers:

• Flexible deadlines: Reset deadlines based on your availability.
• Shareable certificate: Get a Certificate when you complete
• 100% online
• Course 2 of 3 in the Data Science Foundations Specialization: Statistical Inference
• Intermediate level: Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R.
• Approx. 26 hours to complete
• Subtitles: English

Course ratings: 4.3/5

Enroll here: https://www.coursera.org/learn/statistical-inference-for-estimation-in-data-science