What Are Python Libraries for Data Science?
April 10, 2024
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Instructors: Brian Caffo, PhD
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Understand the process of drawing conclusions about populations or scientific truths from data
Describe variability, distributions, limits, and confidence intervals
Use p-values, confidence intervals, and permutation tests
Make informed data analysis decisions
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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. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.
This week, we'll focus on the fundamentals including probability, random variables, expectations and more.
10 videos11 readings1 assignment5 programming assignments
We're going to tackle variability, distributions, limits, and confidence intervals.
10 videos4 readings1 assignment3 programming assignments
We will be taking a look at intervals, testing, and pvalues in this lesson.
11 videos5 readings1 assignment3 programming assignments
We will begin looking into power, bootstrapping, and permutation tests.
9 videos4 readings1 assignment3 programming assignments1 peer review
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
Instructor ratings
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
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Reviewed on Feb 22, 2016
This was probably the most difficult and challenging course . Had to pull out my old stats books to remember most of it. Using R to do what we used to do with TI-83's was great!
Reviewed on Jul 8, 2021
Statistics was not, to put it mildly, my favorite subject in college. This class, however, managed to actually get me involved in the subject as it is tought with applicability in mind. Thank you.
Reviewed on May 22, 2017
Excellent course. After completion, I really feel like I have a great grasp of basic inferential statistics and this course introduced ideas that I had not even considered before.
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