Вернуться к Fitting Statistical Models to Data with Python

4.4
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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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## 26–50 из 115 отзывов о курсе Fitting Statistical Models to Data with Python

автор: Walt T S L

20 нояб. 2020 г.

Great statistical lessons, I did not realize there were more regression-type models besides Ordinary Least Squares, which expanded my learning horizon, and of course, applied using Python Jupyter Notebooks. Python Code was comprehensive and enabled easy following. It was immensely helpful as I did not know how to even begin constructing a linear model study, using independent or dependent data.

автор: ellie c

15 авг. 2020 г.

The most difficult course in this specification! The most important takeaway point of this course is to understand why we choose any model to fit our data, and how to interpret the model. Don't jump into complex math calculation, we got python to do that for us! Dr Brady did a very good job conveying those ideas to us.

p.s the forum has great discussion posts, make sure to use the forum.

автор: William S

5 окт. 2021 г.

I have learnt to applying coding in statistical analysis. I really enjoyed the Week 4 Bayesian Statistics because the use of coding has added new favor to this topic. It makes the study a real science but not something set in the stone (textbook).

автор: ARVIND K S

7 апр. 2020 г.

A great course on how to fit models to data. Very rich on theoretical concepts and equally great on the practical aspects of using python to fine-tune your model, viewing the same each time as you modify data. Very fine course indeed

автор: Bharti S

18 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.

автор: Alvaro F

12 мар. 2019 г.

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.

автор: Kylie A

12 июля 2021 г.

Just like the other courses in the specialization, very well thought out and planned! Up to date, great professors . . . couldn't ask for more!

автор: Varga I K

14 апр. 2019 г.

Great review of machine learning used in statistics finished up with some overview on bayesian math.

Enjoyed very much and learnt even more.

автор: Camila B V

28 июня 2021 г.

Excellent, the explanations were perfect and its theorical focus was the thing why I loved this course.

автор: Kumar R

12 янв. 2021 г.

These whole three certifications lays the foundation for learning Machine Learning a more in-depth way.

автор: Xinyuan G

15 июня 2020 г.

The specialization covers important practical topics. I am glad to have the opportunity to explore it.

автор: Alexander B

28 мая 2020 г.

Overall really great coure that covers a lot of material in a concise way.

автор: Tarit G

4 июля 2020 г.

Excellent course! Thanks to the instructors and the team made this MOOC.

автор: RODRIGO E P M

23 авг. 2020 г.

An excellent introductory course to the world of statistical modeling.

автор: Nicky D

22 янв. 2020 г.

Excellent course, really enjoyed the section on Bayesian statistics.

автор: nipunjeet s g

25 мая 2019 г.

Very informative and the example

applications are extremely detailed

автор: Prabakaran C

17 мар. 2020 г.

Have given me CLearcut idea about Mixed-effects and Marginal Models

автор: Erhan K

17 янв. 2022 г.

E​specially the part on Bayesian Statistics are very informative.

автор: Hrishi P

11 июня 2020 г.

Great practical applications of statistics with Python!

автор: DIBYA P S

21 июня 2020 г.

good conceptual development , helped lot in learning

автор: Harish S

27 янв. 2019 г.

Content of course was good. Some issue with quiz.

автор: Appi

23 сент. 2019 г.

Very good instructors and very good workload!

автор: Debabrata A K S

19 февр. 2020 г.

Very nice course. Well explained kudos.

автор: Sumit M

30 мар. 2020 г.

Very Very Good For learning Statistics

автор: JamieLiu

8 сент. 2021 г.

Great course ,I learned a lot from it