Do you do usability tests of your courses? Like you can test a landing page - you pick a random person to perform a certain action on your landing page, and see where they struggle or what is unclear? If you did this with this course before going live, it would benefit everyone. Right now the quality of this course is too low, concepts are not explained enough, and the assignments (especially week 3) contain wrong instructions and errors.

If you want to learn basic and inferential statistics - I would advise checking out the courses with these name from by University of Amsterdam(you can take them without taking the specialization). they are much clearer. And then if you want examples of Python code - take this course. Just check out the forums first. As of jan2019 the Python Notebook used for the week3 assessment had various problems.

This course attempts to cover very useful topics but falls short on several areas. 1. Multiple errors in the assignments. Practice exercises don't have any answers for students to check. 2. Course slides are not provided. 3. Lack of support to questions asked in forum. I learned a lot from the course but a significant amount of time could have been saved if the issues I mentioned were addressed.

In this course, they cover making confidence intervals and calculating p-values given a specific test scenario (compare sample proportion to population proportion, sample mean to population mean, two sample means to each other, etc). While they go though each statistical procedure clearly, I feel like a lot of underlying context is missing. What is the different between a z- and t-distribution? Why do we use those distributions? How do the different tests relate to each other? Etc. It feels like this course needed an extra 50-60 minutes of lecture time to tie all these concepts together. A textbook to follow along would have been great too.

Dissapointing. Full of errors during all the course. I came from the first course and I found the second one dissapointing. Some issues I found I thonk they should solve:

*The assessments have no sense. Which is the sense of asking if 0 or 1 corresponds to napping or not napping from a datasheet? In general I also found myself wasting a lot of time trying to understand the definition of columns of NHANES or other datasheets which I think is useless. In overall I found that the assessments do not evaluate the progress of the student.

*The assessments sometimes are not related to the concepts explained during the week (Name that scenario) or in the case of Pythos quiz ask for unexplained functions (week 3).

*Some assignements, like the Chocoloate assignement, are about previously not explained concepts (cross-over test desing).

*The Jupyter Notebook have several errors, reported in the discussion forums but not solved. Moreover, some parts of the code shown are not explained. And finally, the way to solve a problem is different in the explanation video and notebook; this is very confusing for the student.

*Week 3 explains several times the same concept (CI and p_value) but them skip to explain the Power or SampleSize in detail, which are just mentioned in a Jupyter Python notebook. Moreover, I found missing a detailed explained on when to use z_test or t_test; I would had dedicate some time to this explanations, and less to repeat the same CI and p_value examples over and over.

*Week 4, is just a review of week 3. I like the way Brady T. West explains the concepts, but once agains I found extremely repetitive showing so many examples of the CI and p_value.

Alltogether the course was great. I learned so much and understood some principles I did not understand when having read of them before.

However in some notebooks, calculations were wrong or notbooks were missing alltogether (week 4, last jupyter notebook). Furthermore it can be annoying if you cannot trust a result of a statistical analysis in a notebook because there were other mistakes before. That's why I give you "only" 4/5 stars.

Very clear and interesting lectures, but quizzes and Jupyter notebooks could benefit from some additional proofreading and pre-release testing. Material in last week is out of order. Spent a few hours some week just figuring out the mistakes with the help of the course forum.

Also, I would have liked to have a bit more background and explanation, e.g. information on why we using a particular distribution or a particular test, not just how. While a complete derivation of all the material would clearly be out of scope, other courses did a better job of introducing the theory behind their methods.

The course contents are good to an introduction or refreshing in statistics but the assigments are not really well prepared, and contains many unrepaired errors. This drops down the level an educational potential of this course (and the entire specialization) and converts it in a poor educational resource and a waste of time, in my opinion

Great lecture content. Poor quiz design.

Good lectures but too little practice and quizzes that don't cover all the material. Very little Python.

No lecture slides or "handouts" to summarize procedures or formulae that tend to jumble together for the various scenarios you learn. Some of the lectures told us to find tables needed to do the quizzes online, no more specifications. That was very disappointing.

This course is a good statistics course, but a poor Python course. Python is practically an after thought in each week's lesson as the focus in the lecturing learning methods is entirely verbal rather than supported by in lecture use of Python. The Python review at the end of each week before the assessment is not connected enough with the lecture materials and makes for a very disjointed week of learning.

I found it a lot more interesting than the first one. Better examples, better structure.

My only complain: I do realize that week 4 was meant to be as an overview of the course, but... the structure of week 4 is much much more clear than for the 1-3 week. I for one, wish i watched week 4 first.

Why i say that ... because the examples were all over the place... at some point i wasn't sure if i was supposed to use a t-test or a z-test. Some examples were repeating themselves throughout the course. Sometimes the code presented in the video was not the same as the one present in the lab files.

I can say that at the end of first 3 weeks...i understood almost everything there is about the inferential aspect of statistics...but... the python part was a complete mess. After week 4 & this: Statistics 350 – Formula Sheet (umich.edu) ... everything became clear. The link should be provided in the course... I stumbled on it by accident in the forum section.

I must say that this is a must take course for ones who are aspiring a career in Data Science. All the concepts were laid out so beautifully and it was explained very clearly with visualisations of each real-life-examples. I enrolled in this specialisation before starting my Machine Learning so that I have all the necessary fundamentals of Statistics. Brady Sir & Brendra Ma'am are simply phenomenal, the way they explain the concepts are incredible. The concepts gets etched in one's memory.

From the introduction, the course is supposed to build the knowledge ground up for a beginner in Statistics. However, it falls short in clearing many concepts and the principals end up being vague in a lot of sense, hence, there is a lack of cohesiveness in the concepts spread across weeks. Fortunately, I took an open course offered by Stanford University of the Inference Concepts explained in this course; before taking up this course.

Overall, this is a good course for someone who is familiar with the Inference concepts. For a beginner, a significant amount effort would be required to catch up to these concepts.

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand

This is a very great course. Statistics by itself is a very powerful tool for solving real world problems. Combine it with the knowledge of Python, there no limit to what you can achieve.

Great Course. There are so many example to understand the topic. I really enjoyed every lesson of this specialization. I am going forward for the next one.

It is absolutely great. Instructors are veeeery pasionated with what they do, and the course material is very good.

I really like this course.

El curso es bastante robusto en terminos teoricos y si se sigue en linea la especialización se aprende bastante. En este curso numero 2 me falto que hubieramos podido tocar el tema de regresión lineal. Ahora, los cuadernos de práctica frente a algunos ejemplos de los videos no concuerdan, o se simplifican a tal manera que en muchas ocasiones no hay coherencia frente al código que plantean y frente a la misma explicación. Frente a eso lo más importante es que podamos realizar los ejercicios y el procedimiento completo utilizando python, y luego facilitar el trabajo mediante los modulos. En general si es un curso que recomendaría, me parece que fue bastante robusto y hubo bastante repaso de los temas de intervalos de confianza, estadística t y z y pruebas de hipotesis.

Useful course to learn basic concepts of inferential statistical analysis. However, I would expect more Python exercises/assignments than the essay-type writing assignment.