Вернуться к Fitting Statistical Models to Data with Python

4.4
звезд
Оценки: 607
Рецензии: 112

О курсе

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....

Лучшие рецензии

BS

17 янв. 2020 г.

I am very thankful to you sir.. i have learned so much great things through this course.

this course is very helpful for my career. i would like to learn more courses from you. thank you so much.

VO

17 сент. 2019 г.

Good course, but the last of three was the most difficult one. I hope that it were a good introduction to the fascinating world of statistics and data science

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76–100 из 115 отзывов о курсе Fitting Statistical Models to Data with Python

автор: Fernando S

21 окт. 2020 г.

Overall, the course was a great refresher of statistical theory and application with some great Python exercises. However, some of the Python coding instruction itself could have been more detailed.

автор: sutan a m

16 июня 2020 г.

A great introduction to regression and bayesian analysis in python. I get that the content is hard, but they sum it all well. I would recommend for those who have prior knowledge of statistics.

автор: YAĞMUR U T

22 сент. 2020 г.

The code examples may be more precise with detailed comments. Some codes are not understood, in other words codes can be refactored in a way that can be more suitable for reproducible studies.

автор: Joffre L V

13 авг. 2019 г.

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

автор: JITHIN P J

24 мая 2020 г.

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.

автор: Joe K

11 июня 2020 г.

Good course giving a fair view on fitting statistical models. Could do to elaborate on some of the theoretical models using more illustrations for more understanding.

автор: Tushar W

5 сент. 2020 г.

Good for advance topics like Marginal and Multilevel modelling. The Bayesian model could be explained in a detailed manner by providing more python assignments.

автор: Nicoli M U

4 июня 2020 г.

The course is great, the only improvement I would make is to be a little more didactic in the last two units because it is a more complicated subject.

20 июня 2020 г.

The course was wonderful however, sometimes I felt that a little bit more details could be provided when python code was being explained for week 2.

автор: Samson

16 июня 2021 г.

It was very technical and a lot of the mathematics behind the models were not explained properly. The codes were also not explained properly

автор: JIONG L

15 окт. 2020 г.

Overall it's very good for someone who has a fair background in statistics, except for some small mistakes in slides and notebooks.

автор: Luis D R T

7 мая 2020 г.

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

автор: Sheng-Ta T

24 янв. 2021 г.

Week 3 starts to get unreasonably difficult and hard to understand. Apart from that, the course is still worthwhile to take.

автор: Ezequiel P

11 окт. 2020 г.

Great course. In my view, the lectures were too long and the assignments a bit easy. But, overall, great course.

автор: Antonio P

7 сент. 2020 г.

I think the notebook walkthroughs, while useful, could use some extra reinforcement in the statistical concepts

автор: Iderval d S J S

30 нояб. 2020 г.

The course is great, but I would suggest that the subject of week 3 be divided into two weeks.

автор: Sunit K

27 мая 2020 г.

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

автор: Santanu G

22 июля 2021 г.

Starting from basics of Statistical model to the depth its fine course.

автор: G.akhil

6 мар. 2020 г.

team work

автор: sahil f

17 сент. 2020 г.

None

автор: Sebastien d L

1 июня 2020 г.

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).

автор: Fabian d A G

20 сент. 2021 г.

The final course was definitely a step up in terms of difficulty from the previous two courses. The assignments aren't that hard, but lot of the material are discussed without getting into depth, which makes it difficult to really get a good idea about the inner workings of the statisticsa methods used. I wish the course developers planned the specialization to be a 05 or 06 course specialization, so that the materials covered will be well spread and learners will be eased into the new concepts. Giving a low rating owing to the structure of the course.

автор: Anastasios B

12 дек. 2021 г.

While the topics are interesting, like other courses in this specialization, this one does not really teach Python. Rather, it uses it as a tool in prepared notebooks that you can follow along with, but largely need to do your own research to understand the various syntax and variables used. This really is more of a Stats course, where the Python element doesn't add much other than some visuals of how to read results or view charts/plots using Python. It really isn't an integral part of the course material.

автор: Carlos M V R

13 сент. 2020 г.

I do not feel like this course had given me great knowledge, there is a lot of theory and almost none practice of python, specially in the last two weeks. Topics are interesting and they are good as an opener to learn statistics but there is not enough python about them. I am disappointed on this specialization (specially on this course), I only finished the course because it was the one left to complete the specialization.

автор: Mike W

21 дек. 2019 г.

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.