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Brenda Gunderson,
Brady T. West,
and
Kerby Shedden
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/8fwgeo/fitting
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Syllabus
WEEK 1 - OVERVIEW & CONSIDERATIONS FOR STATISTICAL MODELING
We begin this third course of the Statistics with Python specialization with an overview of what is meant by “fitting statistical models to data.” In this first week, we will introduce key model fitting concepts, including the distinction between dependent and independent variables, how to account for study designs when fitting models, assessing the quality of model fit, exploring how different types of variables are handled in statistical modeling, and clearly defining the objectives of fitting models.
Read more
Syllabus
WEEK 1 - OVERVIEW & CONSIDERATIONS FOR STATISTICAL MODELING
We begin this third course of the Statistics with Python specialization with an overview of what is meant by “fitting statistical models to data.” In this first week, we will introduce key model fitting concepts, including the distinction between dependent and independent variables, how to account for study designs when fitting models, assessing the quality of model fit, exploring how different types of variables are handled in statistical modeling, and clearly defining the objectives of fitting models.
Read more
WEEK 2 - FITTING MODELS TO INDEPENDENT DATA
In this second week, we’ll introduce you to the basics of two types of regression: linear regression and logistic regression. You’ll get the chance to think about how to fit models, how to assess how well those models fit, and to consider how to interpret those models in the context of the data. You’ll also learn how to implement those models within Python.
WEEK 3 - FITTING MODELS TO DEPENDENT DATA
In the third week of this course, we will be building upon the modeling concepts discussed in Week 2. Multilevel and marginal models will be our main topic of discussion, as these models enable researchers to account for dependencies in variables of interest introduced by study designs. We’ll be covering why and when we fit these alternative models, likelihood ratio tests, as well as fixed effects and their interpretations.
WEEK 4: Special Topics
In this final week, we introduce special topics that extend the curriculum from previous weeks and courses further. We will cover a broad range of topics such as various types of dependent variables, exploring sampling methods and whether or not to use survey weights when fitting models, and in-depth case studies utilizing Bayesian techniques to derive insights from data. You’ll also have the opportunity to apply Bayesian techniques in Python.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores how to connect research questions to data analysis methods
Emphasizes different modeling approaches for different types of data
Introduces learners to Python libraries like Statsmodels, Pandas, and Seaborn
Taught by experienced instructors recognized for their work
Covers both theoretical concepts and practical applications
Suitable for learners with a foundational understanding of statistics
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Reviews summary
Statistical modeling with python
Learners say this course provides a comprehensive overview of statistical modeling techniques using Python, with a focus on regression models, multilevel models, and Bayesian statistics. Led by experienced instructors, the course is structured into four weeks, each covering a specific topic. Throughout the course, learners engage with engaging video lectures, hands-on Jupyter notebooks, and interactive quizzes. While the course is generally well-received, learners should note that the material can be challenging, especially in the later weeks. Overall, this course is a valuable resource for students and practitioners looking to enhance their skills in statistical modeling with Python.
Four-week structure covers regression models, multilevel models, and Bayesian statistics.
"In this "Fitting Statistical Models to Data with Python" course, you will learn about"
"The course covered lecture videos, well-prepared readings, Jupyter notebooks to introduce concepts as well as practice notebooks, lab walkthroughs and quizzes."
Jupyter notebooks for practice and reinforcement.
"The course covered lecture videos, well-prepared readings, Jupyter notebooks to introduce concepts as well as practice notebooks, lab walkthroughs and quizzes."
Led by experienced instructors.
"Brady may speak alittle too fast, especially when it comes to long sentences, so you may need to rewind certain segments of the videos numerous times to revisit some concepts as you reflect and learn."
Comprehensive overview of statistical modeling techniques.
"The course covered lecture videos, well-prepared readings, Jupyter notebooks to introduce concepts as well as practice notebooks, lab walkthroughs and quizzes."
Challenging material, especially in later weeks.
"The content of this course is very thorough, but unfortunately it does not make very good use of the online asynchronous nature of a platform like Coursera."
"The course made things even more complicated."
"It is a good introductory course for statistics."
"I found the course to be good."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Fitting Statistical Models to Data with Python with these
activities:
Complete tutorials on statistical software packages
Show steps
Familiarizing yourself with statistical software packages will allow you to efficiently apply the techniques covered in the course to real-world datasets.
Browse courses on
Python
Show steps
-
Select a software package that you want to learn.
-
Find tutorials that cover the basics of the software.
-
Complete the tutorials and practice using the software.
-
Apply the software to a small dataset to test your understanding.
Review Linear Models with R
Show steps
Reviewing Linear Models with R will help reinforce the course material because it covers a few regression models and how to apply them to real-world data and scenarios.
View
Linear Models with Python (Chapman & Hall/CRC...
on Amazon
Show steps
-
Read the table of contents to orient yourself with the book's structure.
-
Select a chapter to read that corresponds to a topic in the course.
-
Summarize key concepts in your own words.
-
Write down any questions you have.
-
Find solutions to your questions using online resources or by asking the instructor.
Form a study group with other students
Show steps
By participating in a study group, you will get the opportunity to interact with your peers and discuss different perspectives on the course material, which will enhance your understanding.
Show steps
-
Identify other students who are taking the course.
-
Schedule regular study sessions.
-
Prepare for the study sessions by completing the assigned readings and reviewing the notes.
-
Discuss the course material, share insights, and work together on problem-solving.
-
Provide feedback to each other on your understanding of the topics.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Organize and review course materials
Show steps
Keeping your course materials organized will not only save time, but also help you synthesize the information in a way that supports retention.
Show steps
-
Gather all of your course materials, including notes, assignments, and quizzes.
-
Set up a system for organizing the materials, such as using folders or a binder.
-
Review the materials on a regular basis to reinforce your understanding.
Solve practice problems on regression models
Show steps
Regression models are the centerpiece of this course. It's vital that you practice various problem types to improve your problem-solving skills and deepen your understanding.
Browse courses on
Regression
Show steps
-
Find a set of practice problems (online or in a textbook).
-
Select a few problems that cover different types of regression models.
-
Solve the problems using the techniques you learned in the course.
-
Check your answers and identify any areas where you need more practice.
-
Review the solutions to the problems and make sure you understand the concepts involved.
Attend workshops on statistical modeling
Show steps
Workshops are a great way to deepen your understanding of specific statistical modeling techniques and learn from experts in the field.
Browse courses on
Statistical Modeling
Show steps
-
Identify workshops that are relevant to your interests and the course topics.
-
Register for the workshops and attend them.
-
Take notes and ask questions during the workshops.
-
Apply the knowledge gained from the workshops to your course assignments and projects.
Create a poster presentation on a specific statistical modeling technique
Show steps
By creating a poster, you will be able to assess your understanding of the statistical techniques presented in the course and explain them to others.
Browse courses on
Regression
Show steps
-
Choose a statistical modeling technique that interests you.
-
Research the technique and gather information on its applications, advantages, and challenges.
-
Design a poster that visually explains the technique, including its mathematical formulation, assumptions, and interpretation.
-
Present your poster at a student conference or event.
Complete tutorials on statistical software packages
Show steps
Familiarizing yourself with statistical software packages will allow you to efficiently apply the techniques covered in the course to real-world datasets.
Browse courses on
Python
Show steps
Show steps
Show steps
- Select a software package that you want to learn.
- Find tutorials that cover the basics of the software.
- Complete the tutorials and practice using the software.
- Apply the software to a small dataset to test your understanding.
Review Linear Models with R
Show steps
Reviewing Linear Models with R will help reinforce the course material because it covers a few regression models and how to apply them to real-world data and scenarios.
View
Linear Models with Python (Chapman & Hall/CRC...
on Amazon
Show steps
Show steps
Show steps
- Read the table of contents to orient yourself with the book's structure.
- Select a chapter to read that corresponds to a topic in the course.
- Summarize key concepts in your own words.
- Write down any questions you have.
- Find solutions to your questions using online resources or by asking the instructor.
Form a study group with other students
Show steps
By participating in a study group, you will get the opportunity to interact with your peers and discuss different perspectives on the course material, which will enhance your understanding.
Show steps
Show steps
Show steps
- Identify other students who are taking the course.
- Schedule regular study sessions.
- Prepare for the study sessions by completing the assigned readings and reviewing the notes.
- Discuss the course material, share insights, and work together on problem-solving.
- Provide feedback to each other on your understanding of the topics.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Organize and review course materials
Show steps
Keeping your course materials organized will not only save time, but also help you synthesize the information in a way that supports retention.
Show steps
Show steps
Show steps
- Gather all of your course materials, including notes, assignments, and quizzes.
- Set up a system for organizing the materials, such as using folders or a binder.
- Review the materials on a regular basis to reinforce your understanding.
Solve practice problems on regression models
Show steps
Regression models are the centerpiece of this course. It's vital that you practice various problem types to improve your problem-solving skills and deepen your understanding.
Browse courses on
Regression
Show steps
Show steps
Show steps
- Find a set of practice problems (online or in a textbook).
- Select a few problems that cover different types of regression models.
- Solve the problems using the techniques you learned in the course.
- Check your answers and identify any areas where you need more practice.
- Review the solutions to the problems and make sure you understand the concepts involved.
Attend workshops on statistical modeling
Show steps
Workshops are a great way to deepen your understanding of specific statistical modeling techniques and learn from experts in the field.
Browse courses on
Statistical Modeling
Show steps
Show steps
Show steps
- Identify workshops that are relevant to your interests and the course topics.
- Register for the workshops and attend them.
- Take notes and ask questions during the workshops.
- Apply the knowledge gained from the workshops to your course assignments and projects.
Create a poster presentation on a specific statistical modeling technique
Show steps
By creating a poster, you will be able to assess your understanding of the statistical techniques presented in the course and explain them to others.
Browse courses on
Regression
Show steps
Show steps
Show steps
- Choose a statistical modeling technique that interests you.
- Research the technique and gather information on its applications, advantages, and challenges.
- Design a poster that visually explains the technique, including its mathematical formulation, assumptions, and interpretation.
- Present your poster at a student conference or event.
Career center
Learners who complete Fitting Statistical Models to Data with Python will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help organizations make informed decisions. This course will help you develop the skills you need to succeed in this role, including how to fit statistical models to data, assess model quality, and interpret results. You will also learn how to use Python libraries such as Statsmodels, Pandas, and Seaborn to implement statistical models. This course may be particularly useful for Data Analysts who want to specialize in predictive modeling or who work with complex datasets.
Statistician
Statistician
Statisticians use statistical methods to collect, analyze, interpret, and present data. This course will help you develop the skills you need to succeed in this role, including how to fit statistical models to data, assess model quality, and interpret results. You will also learn how to use Python libraries such as Statsmodels, Pandas, and Seaborn to implement statistical models. This course may be particularly useful for Statisticians who want to specialize in predictive modeling or who work with complex datasets.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical methods to analyze financial data and make investment decisions. This course will help you develop the skills you need to succeed in this role, including how to fit statistical models to data, assess model quality, and interpret results. You will also learn how to use Python libraries such as Statsmodels, Pandas, and Seaborn to implement statistical models. This course may be particularly useful for Quantitative Analysts who want to specialize in risk modeling or who work with high-frequency data.
Business Analyst
Business Analyst
Business Analysts use data to help businesses improve their performance. This course will help you develop the skills you need to succeed in this role, including how to fit statistical models to data, assess model quality, and interpret results. You will also learn how to use Python libraries such as Statsmodels, Pandas, and Seaborn to implement statistical models. This course may be particularly useful for Business Analysts who want to specialize in predictive modeling or who work with complex datasets.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. This course will help you develop the skills you need to succeed in this role, including how to fit statistical models to data, assess model quality, and interpret results. You will also learn how to use Python libraries such as Statsmodels, Pandas, and Seaborn to implement statistical models. This course may be particularly useful for Machine Learning Engineers who want to specialize in predictive modeling or who work with complex datasets.
Market Researcher
Market Researcher
Market Researchers collect, analyze, and interpret data to help businesses understand their customers and make informed decisions. This course will help you develop the skills you need to succeed in this role, including how to fit statistical models to data, assess model quality, and interpret results. You will also learn how to use Python libraries such as Statsmodels, Pandas, and Seaborn to implement statistical models. This course may be particularly useful for Market Researchers who want to specialize in predictive modeling or who work with customer segmentation.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. This course will help you develop the skills you need to succeed in this role, including how to fit statistical models to data, assess model quality, and interpret results. You will also learn how to use Python libraries such as Statsmodels, Pandas, and Seaborn to implement statistical models. This course may be particularly useful for Data Scientists who want to specialize in predictive modeling or who work with complex datasets.
Biostatistician
Biostatistician
Biostatisticians use statistical methods to analyze biological data. This course will help you develop the skills you need to succeed in this role, including how to fit statistical models to data, assess model quality, and interpret results. You will also learn how to use Python libraries such as Statsmodels, Pandas, and Seaborn to implement statistical models. This course may be particularly useful for Biostatisticians who want to specialize in clinical trials or who work with genetic data.
Health Economist
Health Economist
Health Economists use economic principles to analyze the healthcare system. This course will help you develop the skills you need to succeed in this role, including how to fit statistical models to data, assess model quality, and interpret results. You will also learn how to use Python libraries such as Statsmodels, Pandas, and Seaborn to implement statistical models. This course may be particularly useful for Health Economists who want to specialize in health policy or who work with healthcare data.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical methods to improve the efficiency of organizations. This course will help you develop the skills you need to succeed in this role, including how to fit statistical models to data, assess model quality, and interpret results. You will also learn how to use Python libraries such as Statsmodels, Pandas, and Seaborn to implement statistical models. This course may be particularly useful for Operations Research Analysts who want to specialize in predictive modeling or who work with complex datasets.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course will help you develop the skills you need to succeed in this role, including how to use Python libraries such as Statsmodels, Pandas, and Seaborn to implement statistical models. This course may be particularly useful for Software Engineers who want to specialize in data science or who work with data-intensive applications.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment decisions. This course will help you develop the skills you need to succeed in this role, including how to fit statistical models to data, assess model quality, and interpret results. You will also learn how to use Python libraries such as Statsmodels, Pandas, and Seaborn to implement statistical models. This course may be particularly useful for Financial Analysts who want to specialize in quantitative finance or who work with complex financial data.
Actuary
Actuary
Actuaries use mathematical and statistical methods to assess risk and uncertainty. This course will help you develop the skills you need to succeed in this role, including how to fit statistical models to data, assess model quality, and interpret results. You will also learn how to use Python libraries such as Statsmodels, Pandas, and Seaborn to implement statistical models. This course may be particularly useful for Actuaries who want to specialize in risk modeling or who work with complex data.
Epidemiologist
Epidemiologist
Epidemiologists study the causes and distribution of disease. This course will help you develop the skills you need to succeed in this role, including how to fit statistical models to data, assess model quality, and interpret results. You will also learn how to use Python libraries such as Statsmodels, Pandas, and Seaborn to implement statistical models. This course may be particularly useful for Epidemiologists who want to specialize in biostatistics or who work with public health data.
Economist
Economist
Economists study the production, distribution, and consumption of goods and services. This course will help you develop the skills you need to succeed in this role, including how to fit statistical models to data, assess model quality, and interpret results. You will also learn how to use Python libraries such as Statsmodels, Pandas, and Seaborn to implement statistical models. This course may be particularly useful for Economists who want to specialize in econometrics or who work with economic data.
For more career information including salaries, visit:
OpenCourser.com/course/8fwgeo/fitting
Reading list
We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Fitting Statistical Models to Data with Python.
A comprehensive textbook on Bayesian data analysis, introducing both theoretical concepts and practical applications for a wide range of statistical models.
Save
A comprehensive textbook on deep learning, covering both theoretical foundations and practical applications.
A seminal work on generalized linear models, providing a systematic framework for analyzing data from a wide range of distributions.
An in-depth guide on regression modeling techniques and their applications, offering a practical perspective on data analysis and model building.
A widely used textbook on statistical learning, covering fundamental concepts, methods, and applications of modern statistical techniques.
A comprehensive textbook on reinforcement learning, covering both theoretical foundations and practical applications.
Save
A comprehensive exploration of logistic regression, covering both theoretical foundations and practical implementation, with a focus on medical and social science applications.
A practical, hands-on guide to machine learning using Python libraries, covering a wide range of topics from data preparation to model evaluation.
A practical guide to natural language processing using Python, covering a wide range of topics from text classification to machine translation.
A comprehensive guide to statistical methods used in data science, focusing on practical applications and real-world case studies.
A practical guide to computer vision using Python, covering a wide range of topics from image processing to object detection.
A comprehensive guide to data mining techniques using Python, covering both theoretical concepts and practical applications.
A practical guide to using Python for data analysis, covering data cleaning, manipulation, visualization, and machine learning techniques.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/8fwgeo/fitting
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Effort
4 weeks; 4-6 hours/week
Level
Intermediate
Via
Coursera
Institution
University of Michigan
Instructors
Brenda Gunderson
Brady T. West
Kerby Shedden
Language
English
Transcripts
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آذربایجان دیلی
This course belongs to a
collection
called
Statistics with Python. It's comprised of three courses:
- Inferential Statistical Analysis with Python
- Fitting Statistical Models to Data with Python (viewing)
- Understanding and Visualizing Data with Python
Good to know
Explores how to connect research questions to data analysis methods
Emphasizes different modeling approaches for different types of data
Introduces learners to Python libraries like Statsmodels, Pandas, and Seaborn
Taught by experienced instructors recognized for their work
Covers both theoretical concepts and practical applications
Suitable for learners with a foundational understanding of statistics
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