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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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What's inside
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.
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Traffic lights
Traffic lights
Read about what's good
what should give you pause 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
Practical statistical modeling with python
According to learners, this course offers a largely positive experience for those seeking to apply statistical models using Python. Students particularly praise the practical application of concepts through hands-on Jupyter Notebook labs and the use of real-world datasets, which helps bridge theory to practice. The course provides a solid foundation in models like linear and logistic regression, extending to multilevel and Bayesian techniques. However, some learners note the challenging pace, especially for advanced topics, suggesting it's better suited for those with prior statistical and Python knowledge, as it can be overwhelming for true beginners.
Provides clear explanations for foundational models.
"The explanations for linear and logistic regression were very clear and easy to follow."
"Excellent foundation for fitting models. The flow from independent to dependent data was logical and well-structured."
"The instructor explains complex topics with great clarity, making advanced statistics accessible."
Emphasizes applying models to real datasets effectively.
"I especially appreciated the real-world datasets used; it made the concepts much more tangible and applicable."
"This course truly delivered on its promise of combining stats with Python for practical data analysis scenarios."
"It really helped me understand how to approach modeling problems in my job. The emphasis on study design was unique and valuable."
Excellent for applying statistical theory with Python.
"This course was exactly what I needed to bridge the gap between theoretical statistics and practical application in Python."
"The hands-on coding and projects are the strongest part of the course for me, solidifying concepts with Python."
"The way they integrate Python with statistical concepts is seamless. I particularly enjoyed the real-world case studies."
Some advanced topics could benefit from more detailed coverage.
"I felt the Bayesian section was too brief. It just scratches the surface and doesn't provide enough depth to use confidently."
"I felt the section on GLMs could have been expanded. Some explanations felt a bit rushed towards the end of modules."
"Could use more in-depth coverage on complex topics or optimization techniques mentioned in later weeks."
Requires prior statistical and Python knowledge to succeed.
"As a beginner in statistics, I found myself overwhelmed, especially with the advanced topics in Week 4."
"I found this course really hard to follow without a strong statistical background. It jumps into complex models too quickly."
"The assignments require a good grasp of both Python and stats, which wasn't fully clear from the description; I struggled."
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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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
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