Overview

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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 course is basically an introduction to statistics in R. The course covers many different topics in the span of 4 weeks from basic probability and distributions to T tests, p values and statistical power. The lectures take the form of slideshows with a lot of dense mathematical notation, small text and mediocre voiceovers. The course tries to cover too much ground too fast and the material isn't presented in a way that is easy to understand or engaging. I don’t think the lecturer’s face was shown once in the entire course. That’s not to say there isn't good information in the lecture slides, but the presentation and execution are poor.

by
Brandt
completed this course, spending 2 hours a week on it and found the course difficulty to be very easy.

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. The course covers probability, variance, distributions (normal, binomial, poisson), hypothesis testing...

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. The course covers probability, variance, distributions (normal, binomial, poisson), hypothesis testing and p-values, power, multiple comparisons, and finally resampling. Overall this is a rather poor introduction to statistical methods, and the only really relevant hypothesis test covered is the simple t-test.

This is the first course taught by Brian Caffo, who is more mathematically-inclined, and he doesn't do a particularly good job of explaining the material in an intuitive way. There are a few good portions of the course, though, and I though the explanation of statistical power using the manipulate package in R was particularly good, and quite a bit better than the coverage I've received in face-to-face university courses I've taken. Otherwise, though, this course in no way will prepare students to actually conduct most common statistical tests, and it doesn't cover non-parametric statistics in any depth whatsoever. I have heard good things about the former Duke statistics course on Coursera, and that course has just (at the time of this writing) been released as a new specialization (Statistics with R), so that might be a better choice for learners looking for a better coverage of statistics using R packages.

Overall, three stars. There are a few gems hidden among the rest of the course content, but overall the course is not particularly good for learning statistical techniques, and it is unlikely that you'll come away from this with any real understanding of how to apply statistical hypothesis testing unless you have pre-existing experience in this area.

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. There are much better courses that cover this topic - Duke as mentioned above is great. Also, the month I took the JHU course there was zero participation from the staff and TAs, in spite of the fact that several of us reached out to Coursera and the Staff help never arrived.

Not recommended - take Duke University's course instead. Confusing lectures and poor development of homework material. Peer grading was poor due to lack of clarity in the grading rubric. Appeared to be no interaction by instructor or feedback (vice Duke course in which the instructor was highly engaged). Don't waste your time or money.

by
Barbara
completed this course, spending 8 hours a week on it and found the course difficulty to be hard.

This course and professor get a bad rap in my opinion. The topic can be difficult if you don't have any prior experience with statistics. I believe the professor tries very hard to improve the course over time because one of the earlier complaints was that the videos were just slides with a voiceover. That's not true any more.

Students are given many study aids such as homework, swirl exercises (r language) and examples that, if a student makes sure to go through ALL the course materials and reading, will pretty much give you the answers to the project.

This was an outstanding course that does squeeze a lot of stats into a short time frame. It's rather hard but so are a lot of worthwhile things, right?

First of all the course is not easy, especially for a person who have little or no experience in statistics and math-like me-. Anyone starting this course should be aware of this. Some concepts really needs extra time to study after lessons, probably it would be better to start from a really basic book like "statistics for dummies". That was what i did actually. I needed to study for a couple of months to have a good understand of this lesson. But at the end i started to get familiar with statistical language and satisfied with the course. There are many real life examples, swirl classes and books provided. Ofcourse it could have been better with a better and detailed design of content but yet i am satisfied with it, and i can recommend it to anyone who wants to deal with statistics.

I was enrolled in the data science specialization with John Hopkins University in Coursera, and this was the 6th class in the program, out of 10.
This class is the one that made me drop out of the program entirely.
I was able to follow easily the Data Scientist's toolbox, R programming, Getting and...

I was enrolled in the data science specialization with John Hopkins University in Coursera, and this was the 6th class in the program, out of 10.

This class is the one that made me drop out of the program entirely.

I was able to follow easily the Data Scientist's toolbox, R programming, Getting and Cleaning Data, Exploratory Analysis and Reproducible Research, as the classes were laid out in a good manner, and the assignments and projects reflected what was being taught.

Mind you, I have a programming background already but didn't know R prior to this program.

Then come statistical inference, and this is where hell starts. The subject itself is very interesting, but there's absolutely NO way you can go through the class without looking at other course material elsewhere. The teacher (a new teacher starting this class) just shoots up complex mathematical formulas without really explaining where they come from, or what/how to memorize it, then moves on to more complex ones without giving you a breath or more thorough explanation. I don't know how many times I went on Khan Academy to fully understand everything.

R had very little in the class, as it was more of a mathematical course. But then the project comes and boom, time for you to apply all those confusing probability concepts with R, which you've never had the chance to do before until now. (that's when I dropped the class, at week 4)

I've read others'review and couldn't agree more, and people saying that the Regression Models class is as bad, well no thank you. I'll be taking the Duke's program from now on. However, I really liked the first 5 classes of the JH data science specialization.

To the difference of many I found this course very interesting, difficult for sure and true the lecturer could be fast. You need to spend time with the slides, but if you want to grab inference this is the course. Keep in mind it is a bit as when you are at uni, one hour lesson then 4 hours work.

by
Brett
completed this course, spending 8 hours a week on it and found the course difficulty to be hard.

The course covers quite a lot of material, very quickly. Unfortunately, the material, while nominally for beginners, requires a decently strong statistics background. 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. I found it most frustrating that when I was struggling to grasp a concept, the instructor would say something like, "It is obvious that..." or "everybody knows..."

There is a lot of material, and if you have time to go and learn everything that is covered you should be good with this, but don't expect to learn the subject just by watching the lectures and doing the swirl exercises.

by
Daniel
completed this course, spending 6 hours a week on it and found the course difficulty to be hard.

Too much content for a few weeks, if you don´t have a clue about statistics, It will be hard.

Also, the professor isn´t bad, the guy really knows a lot, but the teaching method is not awesome as in the other courses. Maybe a little more didactic (Specially because of the math), would be helpful, more examples and more weeks to cover the whole content.

by
Jason
completed this course, spending 3 hours a week on it and found the course difficulty to be medium.

The material in the class is solid, but is poorly described. These are the foundations of statistical analysis, and unfortunately there's a lot of statistics jargon that students aren't going to be familiar with in here.