Build Your Statistical Skills for Data Science. Master the Statistics Necessary for Data Science

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Anne Dougherty 另外 +1 位授課教師

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隸屬於 Master of Science in Data Science 學位

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Data Science Foundations: Statistical Inference 專項課程科罗拉多大学波德分校

您將學到的內容有

Explain why probability is important to statistics and data science.

See the relationship between conditional and independent events in a statistical experiment.

Calculate the expectation and variance of several random variables and develop some intuition.

Identify characteristics of “good” estimators and be able to compare competing estimators.

您將獲得的技能

Inference
Statistics
Data Science
Probability
central limit theorem
continuous random variables
Bayes' Theorem
discrete random variables

關於此專項課程

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This program is designed to provide the learner with a solid foundation in probability theory to prepare for the broader study of statistics. It will also introduce the learner to the fundamentals of statistics and statistical theory and will equip the learner with the skills required to perform fundamental statistical analysis of a data set in the R programming language. 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://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Christopher Burns on Unsplash.

應用的學習項目

Learners will practice new probability skills. including fundamental statistical analysis of data sets, by completing exercises in Jupyter Notebooks. In addition, learners will test their knowledge by completing benchmark quizzes throughout the courses.

可分享的證書

完成後獲得證書

100% 在線課程

立即開始，按照自己的計劃學習。

靈活的計劃

設置並保持靈活的截止日期。

中級

Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R.

完成課程大約需要 4 個月

建議進度：7 小時/週

英語（English）

字幕：英語（English）

可分享的證書

完成後獲得證書

100% 在線課程

立即開始，按照自己的計劃學習。

靈活的計劃

設置並保持靈活的截止日期。

中級

Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R.

完成課程大約需要 4 個月

建議進度：7 小時/週

英語（English）

字幕：英語（English）

專項課程的運作方式

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此專項課程包含 3 門課程

課程1

課程 1

Probability Theory: Foundation for Data Science

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Understand the foundations of probability and its relationship to statistics and data science. We’ll learn what it means to calculate a probability, independent and dependent outcomes, and conditional events. We’ll study discrete and continuous random variables and see how this fits with data collection. We’ll end the course with Gaussian (normal) random variables and the Central Limit Theorem and understand its fundamental importance for all of statistics and data science.

This course 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://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Christopher Burns on Unsplash.

課程2

課程 2

Statistical Inference for Estimation in Data Science

This course introduces statistical inference, sampling distributions, and confidence intervals. Students will learn how to define and construct good estimators, method of moments estimation, maximum likelihood estimation, and methods of constructing confidence intervals that will extend to more general settings.

課程3

課程 3

Statistical Inference and Hypothesis Testing in Data Science Applications

This course will focus on theory and implementation of hypothesis testing, especially as it relates to applications in data science. Students will learn to use hypothesis tests to make informed decisions from data. Special attention will be given to the general logic of hypothesis testing, error and error rates, power, simulation, and the correct computation and interpretation of p-values. Attention will also be given to the misuse of testing concepts, especially p-values, and the ethical implications of such misuse.

授課教師

Anne Dougherty

Senior Instructor and Teaching Professor

Applied Mathematics

5,241 個學生

1 門課程

Jem Corcoran

提供方

科罗拉多大学波德分校

CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.

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此專項課程隸屬於科罗拉多大学波德分校提供的 100% 在線 Master of Science in Data Science。如果您被錄取參加全部課程，您的課程將計入您的學位學習進程。

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完成专项课程需要多长时间？
Statistical Inference for Data Science Applications takes approximately 16 weeks of instruction.
What background knowledge is necessary?
Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R.
Do I need to take the courses in a specific order?
Courses do not have to be taken a specific order, though it's recommended that learners follow the sequence of courses if they have no previous experience with data structures or algorithm analysis and design.
完成专项课程后我会获得大学学分吗？
Statistical Inference for Data Science Applications is part of CU Boulder's Master of Science in Data Science (MS-DS) program. Learners enrolled in the degree program will earn three credits for successful completion of the specialization.
What will I be able to do upon completing the Specialization?
Learners will learn foundational skills such as calculating probabilities of events, discrete and continuous random variables, and using the Central Limit Theorem to estimate probabilities associated with sums of random variables.

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