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Rafael Irizarry
Statistical inference and modeling are indispensable for analyzing data affected by chance, and thus essential for data scientists. In this course, you will learn these key concepts through a motivating case study on election forecasting.
Read more
Statistical inference and modeling are indispensable for analyzing data affected by chance, and thus essential for data scientists. In this course, you will learn these key concepts through a motivating case study on election forecasting.
Read more
Statistical inference and modeling are indispensable for analyzing data affected by chance, and thus essential for data scientists. In this course, you will learn these key concepts through a motivating case study on election forecasting.
This course will show you how inference and modeling can be applied to develop the statistical approaches that make polls an effective tool and we'll show you how to do this using R. You will learn concepts necessary to define estimates and margins of errors and learn how you can use these to make predictions relatively well and also provide an estimate of the precision of your forecast.
Once you learn this you will be able to understand two concepts that are ubiquitous in data science: confidence intervals, and p-values. Then, to understand statements about the probability of a candidate winning, you will learn about Bayesian modeling. Finally, at the end of the course, we will put it all together to recreate a simplified version of an election forecast model and apply it to the 2016 election.
Register for this course and see more details by visiting:
OpenCourser.com/course/w75pzo/data
What's inside
Learning objectives
- The concepts necessary to define estimates and margins of errors of populations, parameters, estimates and standard errors in order to make predictions about data
- How to use models to aggregatedata from different sources
- The very basics of bayesian statistics and predictive modeling
- The concepts necessary to define estimates and margins of errors of populations, parameters, estimates and standard errors in order to make predictions about data
- How to use models to aggregatedata from different sources
- The very basics of bayesian statistics and predictive modeling
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
This course provides an easy-to-understand, step-by-step introduction to the foundational concepts of statistical inference and modeling. It provides a strong grounding for beginners who are interested in data science
The curriculum is designed for those who want to learn how to analyze and interpret data and make predictions in the field
If you're new to statistics and want to gain a solid foundation, this course is an excellent starting point. It covers the basics in a clear and concise manner
Save this course
Save Data Science: Inference and Modeling to your list so you can find it easily later:
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Reviews summary
Clear content, easy learning
Learners say this Data Science course offers clear content that is easy to learn with good efficiency.
You can learn a lot in a short amount of time.
"The material is good quality, so you get quite a good efficiency between time spent and concepts/practical knowledge learnt on the subject."
The course has clear content.
"Easy but clear Course on Inference."
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 Data Science: Inference and Modeling with these
activities:
The Signal and the Noise: Why So Many Predictions Fail—but Some Don't
Show steps
Expand your understanding of statistical inference and modeling by reading this book, which provides real-world examples and insights into the challenges and successes of prediction.
View
The Signal and the Noise: The Art and Science...
on Amazon
Show steps
-
Read the book and take notes
-
Summarize key concepts and ideas
Organize course materials for future reference
Show steps
Enhance your ability to retain information and quickly access relevant materials by organizing your notes, assignments, and other course materials in a systematic manner.
Show steps
-
Create folders and subfolders for different topics or modules
-
File and save materials in the appropriate folders
Review the concept of sampling distributions
Show steps
Review statistical vocabulary and concepts about sampling distributions to refresh your memory before taking this course. This will help you catch up and be prepared to fully engage with the course materials from the very beginning.
Show steps
-
Review lecture notes or chapters about sampling distributions
-
Practice solving problems related to sampling distributions
Five other activities
Expand to see all activities and additional details
Show all eight activities
Estimate means and standard errors of estimates
Show steps
Practice estimating means and standard errors of estimates to strengthen your comprehension and improve your ability to make accurate predictions.
Browse courses on
Estimation
Show steps
-
Find sample data and calculate means and standard errors
-
Compare your results to known values or use simulation to check accuracy
Calculate confidence intervals
Show steps
Go over various scenarios to improve your understanding of how to calculate confidence intervals using different methods. This will give you the opportunity to gain proficiency and identify areas that need improvement.
Browse courses on
Confidence Intervals
Show steps
-
Solve 5-10 practice problems to calculate confidence intervals
Explore Bayesian statistics and predictive modeling
Show steps
Expand your knowledge by exploring Bayesian statistics and predictive modeling. These advanced concepts will enhance your understanding of election forecasting and provide you with additional tools for data analysis.
Browse courses on
Bayesian Statistics
Show steps
-
Find online tutorials or courses on Bayesian statistics
-
Follow along with the tutorials and complete practice exercises
Create visualizations of election results and trends
Show steps
Connect your understanding of election forecasting to its practical application by creating your own visualizations of election results and trends. This will help you synthesize your knowledge and solidify your understanding.
Show steps
-
Gather election results and data
-
Clean and prepare the data for visualization
-
Create visualizations using tools like Tableau or Python
Build a simple election forecast model
Show steps
Test your skills and apply your knowledge by building a simplified version of an election forecast model. This will provide you with valuable hands-on experience and reinforce the concepts covered in the course.
Show steps
-
Design and plan the model
-
Gather and prepare data
-
Build and train the model
-
Test and evaluate the model
The Signal and the Noise: Why So Many Predictions Fail—but Some Don't
Show steps
Expand your understanding of statistical inference and modeling by reading this book, which provides real-world examples and insights into the challenges and successes of prediction.
View
The Signal and the Noise: The Art and Science...
on Amazon
Show steps
Show steps
Show steps
- Read the book and take notes
- Summarize key concepts and ideas
Organize course materials for future reference
Show steps
Enhance your ability to retain information and quickly access relevant materials by organizing your notes, assignments, and other course materials in a systematic manner.
Show steps
Show steps
Show steps
- Create folders and subfolders for different topics or modules
- File and save materials in the appropriate folders
Review the concept of sampling distributions
Show steps
Review statistical vocabulary and concepts about sampling distributions to refresh your memory before taking this course. This will help you catch up and be prepared to fully engage with the course materials from the very beginning.
Show steps
Show steps
Show steps
- Review lecture notes or chapters about sampling distributions
- Practice solving problems related to sampling distributions
Five other activities
Expand to see all activities and additional details
Show all eight activities
Estimate means and standard errors of estimates
Show steps
Practice estimating means and standard errors of estimates to strengthen your comprehension and improve your ability to make accurate predictions.
Browse courses on
Estimation
Show steps
Show steps
Show steps
- Find sample data and calculate means and standard errors
- Compare your results to known values or use simulation to check accuracy
Calculate confidence intervals
Show steps
Go over various scenarios to improve your understanding of how to calculate confidence intervals using different methods. This will give you the opportunity to gain proficiency and identify areas that need improvement.
Browse courses on
Confidence Intervals
Show steps
Show steps
Show steps
- Solve 5-10 practice problems to calculate confidence intervals
Explore Bayesian statistics and predictive modeling
Show steps
Expand your knowledge by exploring Bayesian statistics and predictive modeling. These advanced concepts will enhance your understanding of election forecasting and provide you with additional tools for data analysis.
Browse courses on
Bayesian Statistics
Show steps
Show steps
Show steps
- Find online tutorials or courses on Bayesian statistics
- Follow along with the tutorials and complete practice exercises
Create visualizations of election results and trends
Show steps
Connect your understanding of election forecasting to its practical application by creating your own visualizations of election results and trends. This will help you synthesize your knowledge and solidify your understanding.
Show steps
Show steps
Show steps
- Gather election results and data
- Clean and prepare the data for visualization
- Create visualizations using tools like Tableau or Python
Build a simple election forecast model
Show steps
Test your skills and apply your knowledge by building a simplified version of an election forecast model. This will provide you with valuable hands-on experience and reinforce the concepts covered in the course.
Show steps
Show steps
Show steps
- Design and plan the model
- Gather and prepare data
- Build and train the model
- Test and evaluate the model
Career center
Learners who complete Data Science: Inference and Modeling will develop knowledge and skills
that may be useful to these careers:
Statistician
Statistician
Statisticians collect, analyze, and interpret data to provide insights into real-world problems. This course will help build a foundation in statistical inference and modeling, which are essential skills for statisticians. You will learn how to use R to develop and evaluate statistical models, which are skills that are in high demand in the statistics profession.
Data Scientist
Data Scientist
Data scientists use statistical modeling and machine learning to solve real-world problems. This course will provide you with a strong foundation in the statistical concepts and techniques that are essential for data scientists. You will learn how to use R to develop and evaluate statistical models, which are skills that are in high demand in the data science industry.
Market Researcher
Market Researcher
Market researchers use statistical techniques to collect and analyze data about consumer behavior. This course will provide you with a strong foundation in the statistical concepts and techniques that are essential for market researchers. You will learn how to use R to develop and evaluate statistical models, which are skills that are in high demand in the market research industry.
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers design and build machine learning models. This course will provide you with a strong foundation in the statistical concepts and techniques that are essential for machine learning engineers. You will learn how to use R to develop and evaluate statistical models, which are skills that are in high demand in the machine learning industry.
Biostatistician
Biostatistician
Biostatisticians use statistical techniques to analyze data in the medical field. This course will help build a foundation in statistical inference and modeling, which are essential skills for biostatisticians. You will learn how to use R to develop and evaluate statistical models, which are skills that are in high demand in the biostatistics profession.
Operations Research Analyst
Operations Research Analyst
Operations research analysts use statistical techniques to solve problems in operations management. This course will provide you with a strong foundation in the statistical concepts and techniques that are essential for operations research analysts. You will learn how to use R to develop and evaluate statistical models, which are skills that are in high demand in the operations research industry.
Risk Analyst
Risk Analyst
Risk analysts use statistical techniques to assess risk and uncertainty. This course will help build a foundation in statistical inference and modeling, which are essential skills for risk analysts. You will learn how to use R to develop and evaluate statistical models, which are skills that are in high demand in the risk management industry.
Actuary
Actuary
Actuaries use statistical techniques to assess risk and uncertainty. This course will help build a foundation in statistical inference and modeling, which are essential skills for actuaries. You will learn how to use R to develop and evaluate statistical models, which are skills that are in high demand in the actuarial profession.
Financial Analyst
Financial Analyst
Financial analysts use statistical techniques to analyze financial data and make investment recommendations. This course will provide you with a strong foundation in the statistical concepts and techniques that are essential for financial analysts. You will learn how to use R to develop and evaluate statistical models, which are skills that are in high demand in the financial industry.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts use statistical techniques to analyze financial data and make investment recommendations. This course will provide you with a strong foundation in the statistical concepts and techniques that are essential for quantitative analysts. You will learn how to use R to develop and evaluate statistical models, which are skills that are in high demand in the quantitative finance industry.
Data Engineer
Data Engineer
Data engineers design and build systems to store and process data. This course will provide you with a strong foundation in the statistical concepts and techniques that are essential for data engineers. You will learn how to use R to develop and evaluate statistical models, which are skills that are in high demand in the data engineering industry.
Data Analyst
Data Analyst
Data analysts apply statistical techniques to extract meaningful insights from data. If you are interested in a career as a Data Analyst, this course may help you build a solid foundation in statistical inference and modeling. You will learn how to use R to develop statistical models and make predictions about data, which are essential skills for data analysts.
Postdoctoral Researcher
Postdoctoral Researcher
If you are interested in a career as a postdoctoral researcher in Data Science or statistics, this course may be useful for you. The course will provide you with a strong foundation in the statistical concepts and techniques that are essential for postdoctoral research in Data Science or statistics.
Professor
Professor
If you are interested in a career as a professor who teaches Data Science or statistics, this course may be useful for you. The course will provide you with a strong foundation in the statistical concepts and techniques that are essential for teaching Data Science or statistics.
Teacher
Teacher
If you are interested in a career as a teacher who teaches Data Science or statistics, this course may be useful for you. The course will provide you with a strong foundation in the statistical concepts and techniques that are essential for teaching Data Science or statistics.
For more career information including salaries, visit:
OpenCourser.com/course/w75pzo/data
Reading list
We've selected 24 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
Data Science: Inference and Modeling.
Widely-used textbook for Bayesian statistics, which is essential for understanding the Bayesian modeling component of the course. It provides a thorough introduction to Bayesian concepts and methods.
Save
Classic textbook on time series analysis, which is essential for understanding the time-dependent nature of election data. It provides a comprehensive overview of time series models and forecasting techniques.
Save
Provides a comprehensive overview of statistical modeling from basic to advanced topics. It is particularly useful for understanding the underlying principles and concepts of statistical modeling, which can supplement the course's focus on election forecasting.
Is an excellent textbook for graduate-level statistical inference, which provides solid background and prerequisite knowledge for advanced topics in statistical inference and modeling.
Save
Practical guide to using R for data science tasks, including data manipulation, visualization, and modeling. It valuable resource for those who want to apply R to real-world data analysis problems.
Provides an in-depth treatment of regression modeling techniques, including linear regression, logistic regression, and survival analysis. It valuable resource for understanding the regression methods used in election forecasting.
Save
This classic textbook provides a comprehensive overview of machine learning and pattern recognition techniques. It covers topics such as supervised and unsupervised learning, neural networks, and support vector machines, and is considered an essential reference for practitioners in the field.
This comprehensive textbook provides a detailed introduction to machine learning algorithms and their applications in data science. It covers a wide range of topics, including supervised and unsupervised learning, dimensionality reduction, and ensemble methods.
Save
Comprehensive guide to the R programming language, which is used in the course for data analysis and modeling. It provides a solid foundation for those who are new to R or want to enhance their programming skills.
This renowned textbook provides a comprehensive overview of modern statistical methods for data analysis and machine learning. It covers a wide range of topics, including supervised and unsupervised learning, model selection, and regularization techniques, and is considered an essential reference for practitioners in the field.
This practical guide provides hands-on experience with machine learning algorithms and tools. It covers topics such as data preprocessing, model training, and evaluation, and includes code examples in Python using popular libraries like Scikit-Learn, Keras, and TensorFlow.
This comprehensive textbook provides a detailed introduction to the Python programming language for data analysis. It covers topics such as data wrangling, visualization, statistical analysis, and machine learning.
Save
This classic textbook provides a comprehensive overview of data mining techniques and algorithms. It covers topics such as data preprocessing, clustering, classification, and association rule mining, and is considered an essential reference for practitioners in the field.
Provides a comprehensive overview of statistical methods commonly used in social science research, including descriptive statistics, hypothesis testing, and regression analysis. It offers a solid foundation for understanding the statistical concepts used in election forecasting.
Save
Provides a comprehensive overview of statistical concepts and methods in German. It offers a solid foundation for understanding the statistical principles used in election forecasting.
Covers a comprehensive range of modeling techniques commonly used in predictive analytics, including linear regression, logistic regression, decision trees, and Bayesian networks. It includes practical examples and case studies to demonstrate the application of these techniques in real-world data analysis.
Save
This practical guide provides a comprehensive overview of predictive modeling techniques and best practices. It covers topics such as model selection, evaluation, and deployment, and includes real-world case studies.
This seminal book provides an accessible introduction to Bayesian statistics, a powerful method for modeling uncertainty and inference. It covers the foundational principles of Bayesian statistics and demonstrates their application in various fields.
This practical guide provides an overview of data science principles and best practices for business professionals. It covers topics such as data preparation, model building, and communicating data-driven insights.
This textbook focuses on data mining techniques for massive datasets, which are often encountered in real-world applications. It covers topics such as distributed computing, graph mining, and stream mining.
This popular textbook provides a gentle introduction to statistical learning methods and their applications. It covers topics such as supervised and unsupervised learning, model selection, and regularization techniques, and is written in a clear and accessible style.
Save
This innovative textbook provides an introduction to Bayesian statistics using Python code and interactive examples. It covers topics such as probability theory, Bayesian inference, and Markov chain Monte Carlo methods.
While this book focuses on machine learning, it offers valuable insights into data modeling techniques that are applicable to election forecasting. It provides hands-on exercises and case studies to enhance understanding.
This undergraduate textbook provides a comprehensive introduction to the fundamental concepts of probability and statistics. It covers topics such as descriptive statistics, probability distributions, hypothesis testing, and regression analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/w75pzo/data
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Effort
1 to 2 hours per week for 8 weeks
Level
Introductory
Via
edX
Institution
Harvard University
Instructor
Rafael Irizarry
Sessions
October 16, 2024
- June 18, 2025
April 17, 2024
- December 18, 2024
Language
English
Transcripts
English
Español
Français
Português
Deutsch
中文
العربية
हिंदी
Türkçe
తెలుగు
Bahasa Indonesia
Kiswahili
This course belongs to a
collection
called
Data Science. It's comprised of nine courses:
- Data Science: Inference and Modeling (viewing)
- Data Science: Capstone
- Data Science: Visualization
- Data Science: Wrangling
- Data Science: R Basics
- Data Science: Probability
- Data Science: Linear Regression
- Data Science: Machine Learning
- Data Science: Productivity Tools
Good to know
This course provides an easy-to-understand, step-by-step introduction to the foundational concepts of statistical inference and modeling. It provides a strong grounding for beginners who are interested in data science
The curriculum is designed for those who want to learn how to analyze and interpret data and make predictions in the field
If you're new to statistics and want to gain a solid foundation, this course is an excellent starting point. It covers the basics in a clear and concise manner
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