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Fitting Statistical Models to Data with Python

Statistics with 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.

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Rating 4.2 based on 27 ratings
Length 5 weeks
Effort 4 weeks; 4-6 hours/week
Starts Dec 14 (13 weeks ago)
Cost $79
From University of Michigan via Coursera
Instructors Brenda Gunderson, Brady T. West, Kerby Shedden
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Math And Logic Probability And Statistics

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What people are saying

data science

I must say that this is a must take course for ones who are aspiring a career in Data Science.

sir & brendra ma'am

Brady Sir & Brendra Ma'am are simply phenomenal, the way they explain the concepts are incredible.

mix between basic subjects

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.

my incoming business education

I have learned basic knowledge to succeed my incoming business education.

small poorly explained notebook

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.

starting my machine learning

I enrolled in this specialisation before starting my Machine Learning so that I have all the necessary fundamentals of Statistics.

evaluations focused on database

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 Thank you for creating this course.

had never given much

I had never given much thought to multilevel models and their implications (for example how clustering or the interviewer effected the results).

little into bayesian statistics.note

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.

again michigan online

Thank you again Michigan Online for your great courses!

keeps me interested

The professor has a good speaking and teaching style which keeps me interested.

10-12 min talk

It feels like Brady is reading off the slides and squeezing in a lot of information in a 10-12 min talk.

Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Statistical Technician $39k

Assistant Statistical Research Specialist $60k

Statistical Support Specialist $63k

Statistical Programmer Trainee $63k

Senior Statistical Data Analyst (part time) $69k

Assistant Supervisor Statistical Programmer Analyst $71k

Statistical Programmer/ Clinical SAS Programmer Contractor $82k

Statistical Accounting Analyst $89k

Staff Statistical Analyst $97k

Quality Engineer - Statistical Evaluation and Validation Manager $97k

Senior Statistical/Clinical Programmer $98k

Associate Principal Statistical Programmer $161k

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Rating 4.2 based on 27 ratings
Length 5 weeks
Effort 4 weeks; 4-6 hours/week
Starts Dec 14 (13 weeks ago)
Cost $79
From University of Michigan via Coursera
Instructors Brenda Gunderson, Brady T. West, Kerby Shedden
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Math And Logic Probability And Statistics

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