Back to Fitting Statistical Models to Data with Python

4.4

107 ratings

•

19 reviews

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....

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.

Jun 30, 2019

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

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By Tobias R

•Mar 10, 2019

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

By Aayush G

•May 29, 2019

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.

By Harish S

•Jan 27, 2019

Content of course was good. Some issue with quiz.

By Varga I K

•Apr 14, 2019

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

Enjoyed very much and learnt even more.

By nipunjeet s g

•May 25, 2019

Very informative and the example

applications are extremely detailed

By EDILSON S S O J

•Jun 18, 2019

Spectacular Course!

By HUNG H L

•Aug 01, 2019

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!

By Jafed E

•Jul 06, 2019

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

By Jose H C

•Sep 02, 2019

It was good - Thanks.!

By Vinícius G d O

•Sep 18, 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

By Appi

•Sep 24, 2019

Very good instructors and very good workload!

By JIANG X

•Jun 30, 2019

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

By Alvaro F

•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.

By 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

By 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.

By 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.

By David Z

•Feb 10, 2019

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

By Ersyida K

•Sep 18, 2019

please better explanation of python videos

By Kristoffer H

•Jan 13, 2019

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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