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294 Bewertungen

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47 Bewertungen

In this course, we will expand our exploration of statistical inference techniques by focusing on the science and art of fitting statistical models to data. We will build on the concepts presented in the Statistical Inference course (Course 2) to emphasize the importance of connecting research questions to our data analysis methods. We will also focus on various modeling objectives, including making inference about relationships between variables and generating predictions for future observations.
This course will introduce and explore various statistical modeling techniques, including linear regression, logistic regression, generalized linear models, hierarchical and mixed effects (or multilevel) models, and Bayesian inference techniques. All techniques will be illustrated using a variety of real data sets, and the course will emphasize different modeling approaches for different types of data sets, depending on the study design underlying the data (referring back to Course 1, Understanding and Visualizing Data with Python).
During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week’s statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera....

Jan 18, 2020

I am very thankful to you sir.. i have learned so much great things through this course.\n\nthis course is very helpful for my career. i would like to learn more courses from you. thank you so much.

Mar 12, 2019

The course is actually pretty good, however the mix between basic subjects (like univariate linear regression) and relatively advanced topics (marginal models) may discourage some students.

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von Sumit M

•Mar 30, 2020

Very Very Good For learning Statistics

von Emory F

•Apr 14, 2020

The classes and mentors are amazing.

von Jose H C

•Sep 02, 2019

It was good - Thanks.!

von Aniket D S

•Apr 18, 2020

Detailed and Precise.

von EDILSON S S O J

•Jun 18, 2019

Spectacular Course!

von Kevin K

•Jan 02, 2020

Good Intro course

von Gopichand M

•Mar 24, 2020

Excellent!

von Minas-Marios V

•May 06, 2020

This course does a nice work introducing the concepts of model fitting, especially during the first two weeks where the emphasis is on multiple linear regression and logistic regression. Professor West does a great job focusing on the theory that one needs to know before applying any modeling, and there is quite a lot of Python material at the end that the learner will have to explore mostly on his own, since the corresponding videos are somewhat lacking in depth. Week 3, on the other hand, introduces some very interesting but advanced concepts that can be quite hard to grasp, especially for learners that haven't had much experience with classic statistical model fitting. Week 4 is mostly an introduction to Bayesian Models, but nothing deep.

Overall, I was a bit disappointed with how the course was structured, and the fast pacing after Week 2 might discourage learners. I would recommend the couse however to anyone wanting to really follow up on the material covered, especially from a Statistics perspective (not Data Science-wise).

von Yasin A

•Apr 17, 2020

It is a good introductory course for statistics. The programming assignments were not challenging enough to cement what you have learned. The concepts in week 3 and week 4 were challenging and their approach was not good. I feel like I wasted my time. The focus should have been on multilevel model fitting rather than covering bayesian statistics. Week 4 only added more confusion. However, as an introduction course, they did a good job of presenting the concepts in the prior courses of the specialization.

von ILYA N

•Oct 05, 2019

The course is alright. They give a high-level overview of linear and logistic regression, and dip a little into Bayesian statistics.

Note that they use the StatsModel package in their practice assignments. So I was a bit disappointed I didn't get to practice sklearn, which is about x10 as popular in the field.

von Joffre L V

•Aug 13, 2019

Very good course, I like many practices and evaluations focused on database of real cases, perhaps it would be advisable to reproduce results from the same sources .....

JL

von JITHIN P J

•May 24, 2020

Very informative. But had few confusions in the last course. Also the python code explanations were not good as the instructor was rushing through it without explaining.

von Luis D R T

•May 07, 2020

Me gusto sobre todo los modelos de nivel combinados con estadistica bayesiana ,eso fue lo mejor y de verdad invaluable del curso

von Sunit K

•May 28, 2020

Great course. It really improved my understanding of statistical modeling methodologies.

von G.akhil

•Mar 06, 2020

team work

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von Sebastien d L

•Jun 01, 2020

The content of this course is very thorough, but unfortunately it does not make very good use of the online asynchronous nature of a platform like Coursera. Most of the course consists of lengthy video-lectures paging through slides (and occasionally walking through notebooks). The hands-on parts seem like a second thought, and are mostly made of either reading long Jupyter notebooks, or running simple pre-coded ones to answer a short quizz. Statistical modeling is a topic that shoudl naturally lend itself really well to a "learn by doing" method, but unfortunately this course took the more traditional academic approach (nothing wrong with the later, it's just less engaging for me, especially when sitting in front of a computer).

von Mike W

•Dec 21, 2019

There is some good lecture content, but the assessments don't really give you a chance to "do stats" and demonstrate mastery of the material.

E.g., the week 3 Python assessment consists of just running Python code--you don't actually write any code--and answering the questions is as easy as, e.g., picking the parameter with the largest number.

von Xiaoping L

•Feb 06, 2020

It feels like Brady is reading off the slides and squeezing in a lot of information in a 10-12 min talk. I would prefer the course slows down and would introduce a case example before jumping into models full blown. The slides look wordy. Circling out the numbers when they are mentioned in the talk would help students focus as well.

von Yaron K

•Jan 26, 2019

I had never given much thought to multilevel models and their implications (for example how clustering or the interviewer effected the results). So the course was definitely interesting. However the Python notebooks that are part of the course don't give enough detail to be able to apply the theoretic material to other models.

von Aurelien L

•May 23, 2020

I was a bit disappointed by the notebooks of week3: missing some details and explanations for me.

von Ersyida K

•Sep 18, 2019

please better explanation of python videos

von Mikel A

•May 31, 2020

In my opinion, the course does not worth. I just complete it, as I came from the first two courses and I wanted to complete all the specialization (and I still had some days untill the deadline of the fee).

The first week is very basic. Week two, could be the most usefull if they had develope the maths behind fitting, not just a conceptual explanation. And finally weeks three and four, in my opinion, are out of the level of the course; I can't understand why to move to multivelel or Bayesian, if the basic fitting of Week2 has not been explained. In all the course, just concepts are explained, not the maths to understand in detail.

Moreover, I found too many extern lectures, apps or interviews that add little to the course.

The quiz, as in the previous course should be re-thought, I don't think are the best evaluation method. As for example, you can have wrong answer just not for running the code in Jupyter Notebook but in Spyder. Moreover, the quiz from weeks 2 and 3 about Python are ridiculous, you just have to run a code already written by the teaching stuff.

von Ron C

•May 01, 2020

Good job in covering the initial models, and then above average when going into the multi-level modeling, but pretty disappointed on the marginal and the bayesian. Bayesian videos started out well, but really felt superficial when it was all done. With all of the courses in this specialization, there is little to no actually learning of python, just some simple outputs -- really missed the mark in teaching us python to solve these problems.

von Klaas v S

•Apr 19, 2020

Messy, too many half-explained ideas

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