Fitting Statistical Models to Data with Python

This specialization is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will learn where data come from, what types of data can be collected, study data design, data management, and how to effectively carry out data exploration and visualization. They will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures. Finally, they will learn the importance of and be able to connect research

( 38 Reviews )

Created by: Brenda Gunderson

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Overall Score : 86 / 100

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Course Description

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.

Time: 14h

Instructor Details

Brenda Gunderson

4.3 Rating
38 Reviews

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.

Reviews

4.3

38 total reviews

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By Tobias R on 10-Mar-19

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

By Aayush G on 29-May-19

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 on 27-Jan-19

Content of course was good. Some issue with quiz.

By Alvaro F on 12-Mar-19

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 Varga I K on 14-Apr-19

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 on 25-May-19

Very informative and the example applications are extremely detailed

By EDILSON S S O J on 18-Jun-19

Spectacular Course!

By JIANG X on 30-Jun-19

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

By Jafed E on 6-Jul-19

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 HUNG H L on 1-Aug-19

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 Vincius G d O on 18-Sep-19

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 Jose H C on 2-Sep-19

It was good - Thanks.!

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