Chevron Left
Volver a Fitting Statistical Models to Data with Python

Opiniones y comentarios de aprendices correspondientes a Fitting Statistical Models to Data with Python por parte de Universidad de Míchigan

4.4
estrellas
608 calificaciones

Acerca del Curso

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....

Principales reseñas

BS

17 de ene. de 2020

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 de sep. de 2019

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

Filtrar por:

51 - 75 de 115 revisiones para Fitting Statistical Models to Data with Python

por SHIVAM A

2 de jun. de 2021

awesome course teaching and materials

por Emory F

13 de abr. de 2020

The classes and mentors are amazing.

por Wissam Z

3 de ene. de 2022

very advance and helpful course

por Álvaro M R

22 de mar. de 2021

Amazing course! Really good.

por Yiyi Z

17 de ago. de 2021

This course is very good

por Jose H C

2 de sep. de 2019

It was good - Thanks.!

por João G T B

23 de sep. de 2020

Very good statistics!

por Aniket S

18 de abr. de 2020

Detailed and Precise.

por Enrique A M

23 de nov. de 2020

Thanks U. Michigan..

por Edilson S

17 de jun. de 2019

Spectacular Course!

por Kevin K

2 de ene. de 2020

Good Intro course

por Rabia G

20 de jun. de 2022

very informative

por Sebastian R R

22 de sep. de 2020

Excelente curso.

por Mogaparthi G

24 de mar. de 2020

Excellent!

por Dr G S

12 de mar. de 2022

very good

por A.Srinivasa R

6 de jun. de 2020

excellent

por Lou B V

17 de sep. de 2020

Great!

por Dr. S R

18 de ago. de 2020

nice

por Edward J

12 de ene. de 2021

Another interesting course - the final one in this specialisation - but the difficulty really ramped up in Week 3 after the final peer marked assignment. I had been so impressed with the clear explanations, revision and review, and the opportunities to apply new knowledge. However, it all became very abstract - I thought Mark did a good job but perhaps Bayesian is a whole different specialisation. Overall, I really enjoyed the specialisation and I am pleased to have received a good grounding in statistics ahead of my Data Science diploma. Thank you to Brenda and Brady especially but everyone was very strong and the future is bright with some enthusiastic young talent coming through at Michigan. Edward

por Yasin A

16 de abr. de 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.

por Fanchen H

4 de abr. de 2021

Overall, this course clearly conveys the general ideas about model fitting. The python labs of week 2 and 3 are helpful. However, the materials for week 3 and week 4 lectures are not as good as others in this series. I understand that the author tend to avoid confusing learners with complicated math. Unfortunately, jumping to piles of conclusions without any necessary justifications leaves learners lost.

por NIWANSHU M

15 de jun. de 2020

The videos were really lengthy, above 15 minutes videos are hard to understand for me. Although the overall specialization is really good and gives me very confidence. I would recommend everyone who wants to be a data scientist in future.Thanks Brenda and Brady T West and of course Julie Deeke and other students.

por ILYA N

5 de oct. de 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.

por DHRUV D

10 de sep. de 2020

python codes were pretty tough to undertsand in the end but the concepts though difficult to understand the faculty did there best possible to make it understand. Python codes should have got little bit more time to be explained

por mohamad z

28 de sep. de 2021

t​his course consist of very important topics , they give you an overview of these topics and you have to dive in .

some information hard to understand and other easy .

i​ enjoyed learning this course