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Julie Pai
Welcome to Predictive Modeling, Model Fitting, and Regression Analysis. In this course, we will explore different approaches in predictive modeling, and discuss how a model can be either supervised or unsupervised. We will review how a model can be fitted, trained and scored to apply to both historical and future data in an effort to address business objectives. Finally, this course includes a hands-on activity to develop a linear regression model.
Register for this course and see more details by visiting:
OpenCourser.com/course/9xyn2q/predictive
What's inside
Syllabus
Predictive Modeling
Welcome to Module 1, Predictive Modeling. In this module we will begin with a comparison of predictive and descriptive analytics, and discuss what can be learned from both. We will also discuss supervised and unsupervised modeling, two foundational models in analytics and machine learning.
Read more
Syllabus
Predictive Modeling
Welcome to Module 1, Predictive Modeling. In this module we will begin with a comparison of predictive and descriptive analytics, and discuss what can be learned from both. We will also discuss supervised and unsupervised modeling, two foundational models in analytics and machine learning.
Read more
Data Dimensionality and Classification Analysis
Welcome to Module 2, Data Dimensionality and Classification Analysis. In this module we will explore how data can be classified and how decision trees can be leveraged as a fast, easy to use a model that is easy to interpret, explain, and visualize.
Model Fitting
Welcome to Module 3, Model Fitting. In this module we will explore the concept of model fitting and how creating a generalized model that is able to fit both historical and future data is the ultimate goal. We will also review how a model can be trained or scored to apply to new and unlabeled data.
Regression Analysis
Welcome to Module 4, Regression Analysis. In this module we will begin with an explanation of regression analytics, a popular technique used by data science professionals to make predictions. We will also discuss how achieving model fit is not a guarantee that a model can help solve a business problem, and how even a good model can sometimes lead to unactionable outcomes.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Meets the industry-standard pattern of comparing both predictive and descriptive analytics
Provides an hands-on activity to develop a linear regression model
Teaches data dimensionality and classification analysis, topics commonly used in machine learning
Provides a reminder that a good model fit is not a guarantee that a model can help solve a business problem
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Reviews summary
Regression analysis fundamentals
Learners say Regression Analysis Fundamentals offers a good overview of model fitting and prediction. The course is well-received by learners. Students say the course is concise and easy to understand. It is a good course for beginners.
Course content is clear and easy to follow.
"course content is very concise and easy to understand"
"Information very thin and there are annoying mistakes."
"good"
Course is suitable for those learning regression analysis for the first time.
"good course for beginners"
"It's very very basic level that you can easily find on google"
"Rather short, but still comprehensive enough for a beginner."
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 Predictive Modeling, Model Fitting, and Regression Analysis with these
activities:
Review mathematics
Show steps
Reviewing mathematics will help you understand the foundational concepts of predictive modeling.
Browse courses on
Algebra
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-
Review basic algebra
-
Review basic calculus
-
Review basic probability
Find a mentor who can help you with predictive modeling
Show steps
Having a mentor can help you learn from someone who has experience in predictive modeling.
Browse courses on
Predictive Modeling
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-
Network with people in the field of predictive modeling
-
Ask for referrals to potential mentors
-
Interview potential mentors
-
Choose a mentor who you feel comfortable with and who can help you achieve your goals
Solve regression problems
Show steps
Solving regression problems will help you practice applying the concepts of predictive modeling.
Browse courses on
Regression Analysis
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-
Find a dataset with regression problems
-
Use a regression model to solve the problems
Four other activities
Expand to see all activities and additional details
Show all seven activities
Attend a workshop on predictive modeling
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Attending a workshop on predictive modeling will help you learn about the latest techniques and best practices in the field.
Browse courses on
Predictive Modeling
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-
Find a workshop on predictive modeling
-
Attend the workshop
Mentor someone who is new to predictive modeling
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Mentoring someone else can help you reinforce your own understanding of predictive modeling.
Browse courses on
Predictive Modeling
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-
Network with people who are new to predictive modeling
-
Offer to help mentor someone who is new to the field
-
Provide guidance and support to your mentee
Write a blog post about predictive modeling
Show steps
Writing a blog post about predictive modeling will help you solidify your understanding of the concepts.
Browse courses on
Predictive Modeling
Show steps
-
Write a blog post about predictive modeling
-
Research predictive modeling
Build a predictive model for a real-world problem
Show steps
Building a predictive model for a real-world problem will help you apply the concepts of predictive modeling to a practical situation.
Browse courses on
Predictive Modeling
Show steps
-
Identify a real-world problem that you can solve with predictive modeling
-
Collect data for your model
-
Build a predictive model
-
Evaluate your model
Review mathematics
Show steps
Reviewing mathematics will help you understand the foundational concepts of predictive modeling.
Browse courses on
Algebra
Show steps
Show steps
Show steps
- Review basic algebra
- Review basic calculus
- Review basic probability
Find a mentor who can help you with predictive modeling
Show steps
Having a mentor can help you learn from someone who has experience in predictive modeling.
Browse courses on
Predictive Modeling
Show steps
Show steps
Show steps
- Network with people in the field of predictive modeling
- Ask for referrals to potential mentors
- Interview potential mentors
- Choose a mentor who you feel comfortable with and who can help you achieve your goals
Solve regression problems
Show steps
Solving regression problems will help you practice applying the concepts of predictive modeling.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Find a dataset with regression problems
- Use a regression model to solve the problems
Four other activities
Expand to see all activities and additional details
Show all seven activities
Attend a workshop on predictive modeling
Show steps
Attending a workshop on predictive modeling will help you learn about the latest techniques and best practices in the field.
Browse courses on
Predictive Modeling
Show steps
Show steps
Show steps
- Find a workshop on predictive modeling
- Attend the workshop
Mentor someone who is new to predictive modeling
Show steps
Mentoring someone else can help you reinforce your own understanding of predictive modeling.
Browse courses on
Predictive Modeling
Show steps
Show steps
Show steps
- Network with people who are new to predictive modeling
- Offer to help mentor someone who is new to the field
- Provide guidance and support to your mentee
Write a blog post about predictive modeling
Show steps
Writing a blog post about predictive modeling will help you solidify your understanding of the concepts.
Browse courses on
Predictive Modeling
Show steps
Show steps
Show steps
- Write a blog post about predictive modeling
- Research predictive modeling
Build a predictive model for a real-world problem
Show steps
Building a predictive model for a real-world problem will help you apply the concepts of predictive modeling to a practical situation.
Browse courses on
Predictive Modeling
Show steps
Show steps
Show steps
- Identify a real-world problem that you can solve with predictive modeling
- Collect data for your model
- Build a predictive model
- Evaluate your model
Career center
Learners who complete Predictive Modeling, Model Fitting, and Regression Analysis will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts collect and analyze data to help organizations make better decisions. They use statistical methods and modeling techniques to identify trends and patterns in data, and they develop visualizations to communicate their findings to stakeholders. This course can help you build the skills you need to become a Data Analyst by providing you with a foundation in predictive modeling, model fitting, and regression analysis. You will learn how to collect and clean data, how to develop and fit models, and how to interpret and communicate your results.
Data Scientist
Data Scientist
Data Scientists use their knowledge of mathematics, statistics, and computer science to solve business problems. They develop and apply machine learning models to predict outcomes and make recommendations. This course can help you build the skills you need to become a Data Scientist by providing you with a foundation in predictive modeling, model fitting, and regression analysis. You will learn how to develop and evaluate machine learning models, and how to use them to solve real-world problems.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical methods to analyze financial data. They develop and use models to predict financial outcomes, and they make recommendations to investors. This course can help you build the skills you need to become a Quantitative Analyst by providing you with a foundation in predictive modeling, model fitting, and regression analysis. You will learn how to develop and evaluate financial models, and how to use them to make investment decisions.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and implement machine learning systems. They work with Data Scientists to develop and evaluate machine learning models, and they deploy these models into production. This course can help you build the skills you need to become a Machine Learning Engineer by providing you with a foundation in predictive modeling, model fitting, and regression analysis. You will learn how to develop and evaluate machine learning models, and how to deploy them into production.
Actuary
Actuary
Actuaries analyze risks and uncertainties to help organizations make informed decisions. They use mathematical and statistical methods to develop models that can predict future events, and they use these models to develop insurance policies and other financial products. This course can help you build the skills you need to become an Actuary by providing you with a foundation in predictive modeling, model fitting, and regression analysis. You will learn how to develop and evaluate actuarial models, and how to use them to make decisions about insurance and other financial products.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical methods to improve the efficiency of organizations. They develop and use models to optimize business processes, and they make recommendations to managers about how to improve operations. This course can help you build the skills you need to become an Operations Research Analyst by providing you with a foundation in predictive modeling, model fitting, and regression analysis. You will learn how to develop and evaluate operations research models, and how to use them to improve the efficiency of organizations.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. They use statistical methods to draw conclusions about populations based on samples. This course can help you build the skills you need to become a Statistician by providing you with a foundation in predictive modeling, model fitting, and regression analysis. You will learn how to collect and clean data, how to develop and fit models, and how to interpret and communicate your results.
Market Research Analyst
Market Research Analyst
Market Research Analysts collect and analyze data about consumer behavior. They use this data to develop marketing strategies and products. This course can help you build the skills you need to become a Market Research Analyst by providing you with a foundation in predictive modeling, model fitting, and regression analysis. You will learn how to collect and clean data, how to develop and fit models, and how to interpret and communicate your results.
Business Analyst
Business Analyst
Business Analysts use data to help organizations make better decisions. They analyze data to identify trends and patterns, and they develop recommendations to improve business processes. This course can help you build the skills you need to become a Business Analyst by providing you with a foundation in predictive modeling, model fitting, and regression analysis. You will learn how to collect and clean data, how to develop and fit models, and how to interpret and communicate your results.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment recommendations. They analyze financial statements and other data to identify investment opportunities. This course can help you build the skills you need to become a Financial Analyst by providing you with a foundation in predictive modeling, model fitting, and regression analysis. You will learn how to develop and evaluate financial models, and how to use them to make investment decisions.
Data Engineer
Data Engineer
Data Engineers design and build systems for storing, processing, and managing data. They work with Data Scientists and other data professionals to ensure that data is available and accessible for analysis. This course can help you build the skills you need to become a Data Engineer by providing you with a foundation in predictive modeling, model fitting, and regression analysis. You will learn how to develop and implement data pipelines, and how to use data engineering tools and technologies.
Biostatistician
Biostatistician
Biostatisticians use statistical methods to analyze biological and medical data. They work with scientists and researchers to design studies, collect data, and interpret results. This course can help you build the skills you need to become a Biostatistician by providing you with a foundation in predictive modeling, model fitting, and regression analysis. You will learn how to develop and evaluate statistical models, and how to use them to interpret biological and medical data.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with other engineers and stakeholders to ensure that software meets the needs of users. This course can help you build the skills you need to become a Software Engineer by providing you with a foundation in predictive modeling, model fitting, and regression analysis. You will learn how to develop and test software applications, and how to work with data and algorithms.
Epidemiologist
Epidemiologist
Epidemiologists study the distribution and determinants of health-related states or events in specified populations. They use statistical methods to identify risk factors for disease and to develop prevention strategies. This course may be useful to you if you are interested in becoming an Epidemiologist by providing you with a foundation in model fitting, and regression analysis. You will learn how to develop and evaluate statistical models, and how to use them to analyze health-related data.
Economist
Economist
Economists study the production, distribution, and consumption of goods and services. They use statistical methods to analyze economic data and to develop economic models. This course may be useful to you if you are interested in becoming an Economist by providing you with a foundation in predictive modeling, model fitting, and regression analysis. You will learn how to develop and evaluate economic models, and how to use them to analyze economic data.
For more career information including salaries, visit:
OpenCourser.com/course/9xyn2q/predictive
Reading list
We've selected 14 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
Predictive Modeling, Model Fitting, and Regression Analysis.
Provides a comprehensive introduction to statistical learning, covering both supervised and unsupervised methods. It valuable reference for anyone interested in learning more about predictive modeling.
More advanced treatment of statistical learning, covering a wide range of topics in machine learning. It valuable reference for anyone interested in learning more about the theory and practice of predictive modeling.
Save
Practical guide to data mining, covering a wide range of topics in machine learning. It valuable resource for anyone interested in learning more about the practical aspects of predictive modeling.
Save
Provides a comprehensive overview of predictive analytics, covering a wide range of topics in machine learning. It valuable resource for anyone interested in learning more about the practical aspects of predictive modeling.
Provides a comprehensive overview of machine learning from a probabilistic perspective, covering a wide range of topics in machine learning. It valuable resource for anyone interested in learning more about the theoretical aspects of predictive modeling.
Provides a comprehensive overview of pattern recognition and machine learning, covering a wide range of topics in machine learning. It valuable resource for anyone interested in learning more about the theoretical aspects of predictive modeling.
Provides a comprehensive overview of data science for business, covering a wide range of topics in machine learning. It valuable resource for anyone interested in learning more about the practical aspects of predictive modeling.
Provides a comprehensive overview of machine learning with R, covering a wide range of topics in machine learning. It valuable resource for anyone interested in learning more about the practical aspects of predictive modeling.
Provides a comprehensive overview of machine learning with Python, covering a wide range of topics in machine learning. It valuable resource for anyone interested in learning more about the practical aspects of predictive modeling.
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Provides a comprehensive overview of deep learning, covering a wide range of topics in machine learning. It valuable resource for anyone interested in learning more about the theoretical aspects of predictive modeling.
Provides a comprehensive overview of reinforcement learning, covering a wide range of topics in machine learning. It valuable resource for anyone interested in learning more about the theoretical aspects of predictive modeling.
Provides a comprehensive overview of Bayesian reasoning and machine learning, covering a wide range of topics in machine learning. It valuable resource for anyone interested in learning more about the theoretical aspects of predictive modeling.
Provides a comprehensive overview of Gaussian processes for machine learning, covering a wide range of topics in machine learning. It valuable resource for anyone interested in learning more about the theoretical aspects of predictive modeling.
Provides a comprehensive overview of machine learning for hackers, covering a wide range of topics in machine learning. It valuable resource for anyone interested in learning more about the practical aspects of predictive modeling.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/9xyn2q/predictive
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Level
Intermediate
Via
Coursera
Institution
University of California, Irvine
Instructor
Julie Pai
Language
English
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This course belongs to a
collection
called
Data Science Fundamentals. It's comprised of four courses:
- Predictive Modeling, Model Fitting, and Regression Analysis (viewing)
- Natural Language Processing and Capstone Assignment
- Intro to Analytic Thinking, Data Science, and Data Mining
- Cluster Analysis, Association Mining, and Model Evaluation
Good to know
Meets the industry-standard pattern of comparing both predictive and descriptive analytics
Provides an hands-on activity to develop a linear regression model
Teaches data dimensionality and classification analysis, topics commonly used in machine learning
Provides a reminder that a good model fit is not a guarantee that a model can help solve a business problem
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