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Blended Language Learning: Design and Practice for Teachers
by Edwige SimonTop Instructor- 4.8
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Approx. 21 hours to complete
This course is for language educators who wish to learn how to build and teach a blended language course. If you complete all the assignments, at the end of this course you will have fairly fleshed out a blended unit and you will be well on your way to building your own blended language course....
Predictive Analytics and Data Mining
by Sridhar Seshadri- 4.4
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Approx. 24 hours to complete
This course introduces students to the science of business analytics while casting a keen eye toward the artful use of numbers found in the digital space. Lecture 1-2: Applications of Clustering Lecture 1-7: Real World Clustering Example Syllabus Lecture 2-5: Classification Tree Example Lecture 2-6: Regression Tree Example Lecture 3-7: Employee Attrition Example...
Applying Data Analytics in Marketing
by Joseph T. Yun- 4.4
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Approx. 17 hours to complete
This course introduces students to the science of business analytics while casting a keen eye toward the artful use of numbers found in the digital space. Course Introduction Coursera Course Introduction *** 2 Measurements and Scaling Techniques – Primary Scales of Measurement Syllabus 2 A/B Testing: Types of Tests 3 A/B Testing: R Example...
Bayesian Methods for Machine Learning
by Daniil Polykovskiy , Alexander Novikov- 4.5
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Approx. 33 hours to complete
In six weeks we will discuss the basics of Bayesian methods: from how to define a probabilistic model to how to make predictions from it. Rules on the academic integrity in the course MLE estimation of Gaussian mean Example of GMM training Summary of Expectation Maximization Example of Gibbs sampling Example of Metropolis-Hastings...
Practical Machine Learning
by Jeff Leek, PhD , Roger D. Peng, PhD , Brian Caffo, PhD- 4.5
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Approx. 9 hours to complete
This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. Types of errors A Note of Explanation Syllabus...
Epidemiology: The Basic Science of Public Health
by Dr. Karin Yeatts , Dr. Lorraine Alexander- 4.6
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Approx. 7 hours to complete
By applying the concepts learned in this course to current public health problems and issues, students will understand the practice of epidemiology as it relates to real life and makes for a better appreciation of public health programs and policies. Course Introduction, History of Epidemiology and Epidemiology Research Understanding Measures of Disease Frequency...
Nursing Informatics Training and Education
by Karen Monsen, Ph.D., RN, FAAN- 4.8
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Approx. 21 hours to complete
I will also guide you through the process of preparing a course document or syllabus for the nursing informatics specialty both in academic settings and in practice or industry. Develop a prototype course syllabus and introductory recorded message to apply learning in a simulated setting. The Who What When Were and Why of Nursing Informatics Training and Education...
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Auditing II: The Practice of Auditing
by Mark E. Peecher, PhD, CPA- 4.8
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Approx. 24 hours to complete
This course provides a continuation of the intensive conceptual and applied introduction to auditing in society begun in Auditing I: Conceptual Foundations of Auditing. About the Course Course Overview Module 1: Strategic Analysis of a Client and Its Business Processes WorldCom Example Part 1 Unit Sampling Example 2 Get Your Course Certificate...
ISTQB Foundation level (CTFL) Training 2021 exam + 1500quiz
by ISTQB Foundation level- 4.5
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16 hours on-demand video
--> TOPIC WISE EXPLANATION : Each and every topic of ISTQB 2018 syllabus is covered in the same order as they are there in the syllabus --> 10-Mock Tests Added at the end of the course for practice Why is the course length of more than 10 hr?...
$12.99
Statistical Inference
by Brian Caffo, PhD , Roger D. Peng, PhD , Jeff Leek, PhD- 4.2
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Approx. 54 hours to complete
Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. This course presents the fundamentals of inference in a practical approach for getting things done. 09 02 Example of choosing a rejection region...
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