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Fitting Statistical Models to Data with Python

Fitting Statistical Models to Data with Python

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8.7/10 (Our Score)
Product is rated as #138 in category Python

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. The mission of the University of Michigan is to serve the …

Instructor Details

Brenda Gunderson received her PhD in Statistics from The University of Michigan in 1989. She has stayed on at UM and is a Senior Lecturer. She coordinates and teaches the largest undergraduate statistics course, Statistics and Data Analysis, with approximately 1800 students each term. She is also an undergraduate advisor for students electing to major or minor in Statistics. Her research focuses on Statistical Education, in particular using technology to enhance teaching and learning. Brenda received the UM Teaching Innovation Prize for her work on Infusing Technology for Guided Continuous Learning in a Large Gateway Course. She is co-investigator for a UM grant called: Enhancing Undergraduate Education through the Deployment of Quality Learning Objects. Her work on this grant led to receiving the Innovative Use of MERLOT Award (2009) and a Sloan-C Effective Practice Award (2012). She is also part of an NSF project to expand the UM E2Coach system (Expert Electronic Coaching) to students in introductory statistics courses computer tailored communication technology allows us to provide individualized coaching and advice to students using their individual background, goals, and current standing in the course.

Specification: Fitting Statistical Models to Data with Python

Duration 7 hours
Year 2019
Level Intermediate
Certificate Yes
Quizzes Yes

28 reviews for Fitting Statistical Models to Data with Python

4.3 out of 5
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  1. Avatar

    Kristoffer H

    If you don’t already understand the topic don’t bother with this course, the lectures are 95% hand waving and showing formulas they don’t explain how to make sense of and then the quizzes are answering questions on what they didn’t bother to explain.

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  2. Avatar

    Yaron K

    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.

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  3. Avatar

    Harish S

    Content of course was good. Some issue with quiz.

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  4. Avatar

    David Z

    Great lecture content, poor quiz design. Hard to apply any of the concepts that you learn.

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  5. Avatar

    Tobias R

    The content itself is great but some notebooks were a bit unready. Otherwise great course!

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  6. Avatar

    Alvaro F

    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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  7. Avatar

    Varga I K

    Great review of machine learning used in statistics finished up with some overview on bayesian math. Enjoyed very much and learnt even more.

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  8. Avatar

    nipunjeet s g

    Very informative and the example applications are extremely detailed

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  9. Avatar

    Aayush G

    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.

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  10. Avatar

    EDILSON S S O J

    Spectacular Course!

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  11. Avatar

    JIANG X

    Really thorough and in–depth material about statistical models with python.

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  12. Avatar

    Jafed E

    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

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  13. Avatar

    HUNG H L

    Thank you for creating this course. I have learned basic knowledge to succeed my incoming business education. I have a bachelor degree of laws and am transferring to a master of management. I used this course to learn the prior knowledge that I need about statistics. I finished this specialization and feel more confident about the numerical analysis. Thank you again Michigan Online for your great courses!

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  14. Avatar

    Joffre L V

    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

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  15. Avatar

    Jose H C

    It was good – Thanks.!

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  16. Avatar

    Vinicius G d O

    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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  17. Avatar

    Ersyida K

    please better explanation of python videos

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  18. Avatar

    Appi

    Very good instructors and very good workload!

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  19. Avatar

    ILYA N

    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.

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  20. Avatar

    Nadine A

    Challenging but excellent course, especially how content was organized and examples used to explain concepts

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  21. Avatar

    Mike W

    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.

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  22. Avatar

    Kevin K

    Good Intro course

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  23. Avatar

    Bharti S

    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.

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  24. Avatar

    Michael L

    I was looking for an application course that would help with using Python with real world data. This was a theory course that added a small poorly explained notebook and a very brief lecture which didn’t explain the code very well. If you’re looking for a statistics theory course this might be fore you. If you’re looking for how to use Python in the real world, I might look at other courses first.

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  25. Avatar

    Nicholas D

    Excellent course, really enjoyed the section on Bayesian statistics.

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  26. Avatar

    Flo

    The most impressive part is Week 2 Linear and Logistic Regression model fitting, Professor Brenda is Brilliant! She has the magic to explain complicated and abstract concept into a very easily understandable ones. Thanks her a lot! Also I was impressive on Week 4 Bayesian approaches courses. Thanks Mark Kurzeja. I think He is a very qualified teacher and prepare for this course content very careful and take it seriously. He also gives a very clear mind to understand those abstract statistic concept! Overall, the series of Statistic with Python are impressive! You can really learn something useful and the course design is scientific. All teachers in all courses are very good!

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  27. Avatar

    Gopichand M

    Excellent!

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  28. Avatar

    Sumit M

    Very Very Good For learning Statistics

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