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4.2

45 Bewertungen

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12 Bewertungen

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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von Tobias R

•Mar 10, 2019

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

von 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

von JIANG X

•Jun 30, 2019

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

von EDILSON S S O J

•Jun 18, 2019

Spectacular Course!

von 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.

von nipunjeet s g

•May 25, 2019

Very informative and the example

applications are extremely detailed

von 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.

von 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.

von David Z

•Feb 10, 2019

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

von Harish S

•Jan 27, 2019

Content of course was good. Some issue with quiz.

von 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.

von 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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