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Statistical Inference

Data Science,

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.
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Rating 3.6 based on 643 ratings
Length 5 weeks
Starts Oct 5 (3 weeks ago)
Cost $49
From Johns Hopkins University via Coursera
Instructors Roger D. Peng, PhD, Brian Caffo, PhD, Jeff Leek, PhD
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Data Analysis Probability And Statistics

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What people are saying

statistical inference

I believe the course could be improved by taking that approach in the other weeks Very good introduction course to statistical inference.

Course needs more hands on example on all statistical inference tools - trying to disconnect from the daily routine and dive right into stats was difficult.

Truly recomend This course was very helpful to remember so concepts of statistical inference.

excellent executive introduction A topic such as statistical inference is not complicated, and could be taught in a much more straight forward and comprehendible fashion.

A lot of time is needed to sort out the documentation between R-files, the book (Statistical Inference for Data Science) and the slides.

Great course that covers the basics of statistical inference.

nice provides a formal explanation of statistical inference and its practical application.

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data science specialization

Statistical Inference is the sixth course in the Data Science specialization, and the first course in the analytical portion of the course (followed by Regression Models and Practical Machine Learning.

You'll need to complete this course for the JHU Data Science specialization but you will likely struggle if you don't already have a strong background in statistical inference.

Forced myself through this course :( The best course I've taken in Data Science Specialization.Thanks Professor Caffo!

This is one of the most important course in data science specialization series, everyone should take this course very attentive way, because it give very deep insight about the role of statistics in data science.

I'm in the data science specialization.

In my opinion, the optimal solution for the course would be to create a separate, longer course in PC and stats and require knowledge of the two for taking Data Science Specialization.

The only reason for enrolling is to complete the data science specialization, though it may make you reconsider continuing with it.

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khan academy

My advice: watch others videos (from Khan Academy for instance) in order to understand the basics concepts and then, come back to this course.

Had to go to other MOOC (specifically Khan Academy) to obtain proper understanding of the topic.

Some of the lessons on khan academy are much easier to follow.

I had to go re-watch most of the lessons on khan academy to understand the principles.

It took me three months to go through the basics in Khan Academy before attempting it - and after that it was straight forward.

Khan Academy is a great place to start, and Udacity has a great class that gives a good intuitive understanding.

Khan Academy, DataCamp, Udacity, Duke (Coursera), and Columbia (edX) all have great courses.

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hypothesis testing

The course covers probability, variance, distributions (normal, binomial, poisson), hypothesis testing and p-values, power, multiple comparisons, and finally resampling.

I'd had statistics back at the university but I never understood the underlying principle of hypothesis testing.

focussing mostly on hypothesis testing and p-values.

One gets exposure to topics in intro and intermediate statistic and starts to grasp how intricate the web of statistics it all the while the focus is on Hypothesis testing which is one cornerstones of statistics.

Important for those who are either going to take the Regression analysis or those who are working with data and want to do same basic hypothesis testing.

One gets to learn how to use R to perform the hypothesis testing.

It'd be a little steep of a learning curve for someone new to hypothesis testing/confidence intervals in four weeks.

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difficult to follow

Even with a good foundation of statistics it was difficult to follow when examples are presented quickly and referenced back to material covered in prior lectures or even weeks as if they ought to be totally fresh in the student's mind.

The lectures were almost completely useless - I had to look up videos from youtube and other sources for every single lecture to learn the concept, and then rewatch the lecture - even then the instructor was difficult to follow.

I had some difficult to follow the lessons, because the professor is kind of reading the material and not building the concepts during class time.

If I wasn't already familiar with statistics, I would find the lectures and course book difficult to follow.

The slides are very difficult to follow.

This course is very difficult to follow, not because the topics are hard or too technical but mostly due to lecturer's poor job in explaining and creating a narrative.

Perhaps I'm holding the video lectures to too high of a standard set by Roger Peng in previous courses, but the videos are difficult to follow.

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data analysis

Presentation could be more engaging Statistical inference is one of the most useful things in data analysis.

I thought the course material was great but I think the grading criteria for the assignment should be more rigorous and check for proper methodology/application of techniques, just because I'd like to know that my approach to a given data analysis is sound and that the conclusions I draw from running tests, p-value adjustment, calculating power, etc.

I would recommend this course to everyone who wants to know about data analysis using R language in particular.

If you’re looking for a good introduction to statistics that uses R, try Duke’s Data Analysis and Statistical Inference.

I can spend my time on Duke's Data Analysis and Statistical Inference which was highly recommended on coursera forums.

Take the one from University of Texas on EDX called Foundations of Data Analysis.

I found the lectures provided by the course "Data Analysis & Statistical Inference" helpful in understanding this subject.

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An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Statistical Technician $39k

Assistant Statistical Research Specialist $60k

Statistical Support Specialist $63k

Statistical Programmer Trainee $63k

Senior Statistical Data Analyst (part time) $69k

Assistant Supervisor Statistical Programmer Analyst $71k

Statistical Programmer/ Clinical SAS Programmer Contractor $82k

Statistical Accounting Analyst $89k

Staff Statistical Analyst $97k

Quality Engineer - Statistical Evaluation and Validation Manager $97k

Senior Statistical/Clinical Programmer $98k

Associate Principal Statistical Programmer $161k

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Rating 3.6 based on 643 ratings
Length 5 weeks
Starts Oct 5 (3 weeks ago)
Cost $49
From Johns Hopkins University via Coursera
Instructors Roger D. Peng, PhD, Brian Caffo, PhD, Jeff Leek, PhD
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Data Analysis Probability And Statistics

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