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Arimoro Olayinka Imisioluwa
Increasingly, predictive analytics is shaping companies' decisions about limited resources. In this project, you will build a regression model to make predictions. We will start this hands-on project by exploring the dataset and creating visualizations for the dataset. By the end of this 2-hour-long project, you will be able to build and interpret the result of a simple linear regression model in R. Also, you will learn how to perform model assessments and check for assumptions using diagnostic plots. By extension, you will learn how to build and interpret the result of a multiple linear regression model. To succeed in this project, you need to be familiar with using R to describe data. If you are unfamiliar with R and want to learn the basics, start with my previous guided project, "Getting Started with R." However, if you are comfortable using R, please join me on this beautiful and exciting ride! Let's get our hands dirty!
Register for this course and see more details by visiting:
OpenCourser.com/course/61up9r/data
What's inside
Syllabus
Project Overview
Increasingly, predictive analytics is shaping companies' decisions about limited resources. In this project, you will build a regression model to make predictions. We will start this hands-on project by exploring the dataset and creating visualizations for the dataset. By the end of this 2-hour-long project, you will be able to build and interpret the result of a simple linear regression model in R. Also, you will learn how to perform model assessments and check for assumptions using diagnostic plots. By extension, you will learn how to build and interpret the result of a multiple linear regression model. To succeed in this project, you need to be familiar with using R to describe data. If you are unfamiliar with R and want to learn the basics, start with my previous guided project, "Getting Started with R." However, if you are comfortable using R, please join me on this beautiful and exciting ride! Let's get our hands dirty!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces the learner to the principles of predictive analytics, a valuable skill for data analysts
Provides practical experience in building and interpreting regression models, essential for data analysis
Covers model assessments and diagnostic plots, crucial for evaluating model performance
Taught by experienced instructors Arimoro Olayinka Imisioluwa, recognized for their expertise in predictive analytics and data science
Suitable for learners familiar with using R for data analysis
Assumes some background knowledge in data analysis
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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 Analysis in R: Predictive Analysis with Regression with these
activities:
Mentor junior students or peers
Show steps
Solidify your understanding of the course material by sharing your knowledge and helping others.
Browse courses on
Regression Analysis
Show steps
-
Identify opportunities to mentor junior students or peers who are struggling with the course material.
-
Provide guidance, support, and encouragement to help them overcome challenges.
-
Review their work, answer their questions, and provide constructive feedback.
Read 'Data Science for Business'
Show steps
Gain a comprehensive understanding of data science concepts and their application in business settings.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
-
Read the book thoroughly.
-
Take notes and highlight key concepts.
-
Complete the exercises and assignments provided in the book.
Review statistics concepts
Show steps
Refresh your knowledge of basic statistics concepts to strengthen your foundation for this course.
Browse courses on
Statistics
Show steps
-
Review notes from a previous statistics course or textbook.
-
Complete practice problems on topics like probability, distributions, and hypothesis testing.
-
Take an online quiz or assessment to test your understanding.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in peer study sessions
Show steps
Enhance your understanding of the course material by actively engaging with peers in study sessions.
Browse courses on
Regression Analysis
Show steps
-
Form or join a study group with other students taking this course.
-
Discuss course concepts, review assignments, and solve problems together.
-
Provide and receive feedback on each other's work.
Follow online tutorials on regression analysis
Show steps
Complement the course material by exploring online tutorials that provide step-by-step guidance on regression analysis techniques.
Browse courses on
Regression Analysis
Show steps
-
Search for reputable online tutorials on regression analysis.
-
Follow the tutorials and complete the exercises provided.
-
Apply the concepts learned in the tutorials to your own practice problems.
Solve practice regression problems
Show steps
Deepen your understanding of regression analysis by solving practice problems that cover various aspects of the topic.
Browse courses on
Regression Analysis
Show steps
-
Find practice problems online or in textbooks.
-
Attempt to solve the problems independently.
-
Check your solutions against provided answers or consult with the course instructor or a tutor for guidance.
Create a data visualization
Show steps
Solidify your understanding of regression models by creating a data visualization that demonstrates the relationship between variables.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset that aligns with the course material.
-
Use a data visualization tool to create a scatterplot, bar chart, or other appropriate visualization.
-
Analyze the visualization and identify key trends or relationships.
-
Write a brief report summarizing your findings.
Build a predictive model
Show steps
Apply the concepts learned in this course to a real-world problem by building your own predictive model using regression techniques.
Browse courses on
Machine Learning
Show steps
-
Identify a problem or dataset that can benefit from predictive modeling.
-
Explore and prepare the data.
-
Build and train a regression model.
-
Evaluate the performance of your model.
-
Deploy your model and track its performance over time.
Mentor junior students or peers
Show steps
Solidify your understanding of the course material by sharing your knowledge and helping others.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Identify opportunities to mentor junior students or peers who are struggling with the course material.
- Provide guidance, support, and encouragement to help them overcome challenges.
- Review their work, answer their questions, and provide constructive feedback.
Read 'Data Science for Business'
Show steps
Gain a comprehensive understanding of data science concepts and their application in business settings.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
Show steps
Show steps
- Read the book thoroughly.
- Take notes and highlight key concepts.
- Complete the exercises and assignments provided in the book.
Review statistics concepts
Show steps
Refresh your knowledge of basic statistics concepts to strengthen your foundation for this course.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review notes from a previous statistics course or textbook.
- Complete practice problems on topics like probability, distributions, and hypothesis testing.
- Take an online quiz or assessment to test your understanding.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in peer study sessions
Show steps
Enhance your understanding of the course material by actively engaging with peers in study sessions.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Form or join a study group with other students taking this course.
- Discuss course concepts, review assignments, and solve problems together.
- Provide and receive feedback on each other's work.
Follow online tutorials on regression analysis
Show steps
Complement the course material by exploring online tutorials that provide step-by-step guidance on regression analysis techniques.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Search for reputable online tutorials on regression analysis.
- Follow the tutorials and complete the exercises provided.
- Apply the concepts learned in the tutorials to your own practice problems.
Solve practice regression problems
Show steps
Deepen your understanding of regression analysis by solving practice problems that cover various aspects of the topic.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Find practice problems online or in textbooks.
- Attempt to solve the problems independently.
- Check your solutions against provided answers or consult with the course instructor or a tutor for guidance.
Create a data visualization
Show steps
Solidify your understanding of regression models by creating a data visualization that demonstrates the relationship between variables.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset that aligns with the course material.
- Use a data visualization tool to create a scatterplot, bar chart, or other appropriate visualization.
- Analyze the visualization and identify key trends or relationships.
- Write a brief report summarizing your findings.
Build a predictive model
Show steps
Apply the concepts learned in this course to a real-world problem by building your own predictive model using regression techniques.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify a problem or dataset that can benefit from predictive modeling.
- Explore and prepare the data.
- Build and train a regression model.
- Evaluate the performance of your model.
- Deploy your model and track its performance over time.
Career center
Learners who complete Data Analysis in R: Predictive Analysis with Regression will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs and builds machine learning models. This course will help you develop the skills you need to be successful in this role, including data engineering, model building, and model deployment.
Data Scientist
Data Scientist
A Data Scientist uses data to solve business problems. This course will help you develop the skills you need to be successful in this role, including data mining, machine learning, and statistical modeling.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data. This course will help you develop the skills you need to be successful in this role, including statistical modeling, data analysis, and experimental design.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze financial data. This course will help you develop the skills you need to be successful in this role, including financial analysis, risk management, and portfolio optimization.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and statistical models to improve business operations. This course will help you develop the skills you need to be successful in this role, including optimization, simulation, and data analysis.
Business Analyst
Business Analyst
A Business Analyst helps businesses understand their data and make better decisions. This course will help you develop the skills you need to be successful in this role, including data analysis, business intelligence, and project management.
Data Analyst
Data Analyst
A Data Analyst is responsible for collecting, cleaning, and analyzing data to identify trends and patterns. This course will help you develop the skills you need to be successful in this role, including data visualization, statistical analysis, and machine learning.
Biostatistician
Biostatistician
A Biostatistician applies statistical methods to medical research. This course will help you develop the skills you need to be successful in this role, including clinical trial design, data analysis, and statistical modeling.
Market Researcher
Market Researcher
A Market Researcher collects and analyzes data to understand consumer behavior. This course will help you develop the skills you need to be successful in this role, including survey design, data analysis, and market forecasting.
Risk Analyst
Risk Analyst
A Risk Analyst assesses and manages risks for businesses. This course will help you develop the skills you need to be successful in this role, including risk assessment, risk management, and data analysis.
Data Engineer
Data Engineer
A Data Engineer designs and builds data infrastructure. This course will help you develop the skills you need to be successful in this role, including data architecture, data management, and data warehousing.
Epidemiologist
Epidemiologist
An Epidemiologist investigates the causes of disease and develops strategies to prevent and control it. This course will help you develop the skills you need to be successful in this role, including data analysis, epidemiology, and public health.
Financial Analyst
Financial Analyst
A Financial Analyst analyzes financial data to make recommendations to investors. This course will help you develop the skills you need to be successful in this role, including financial modeling, valuation, and portfolio management.
Software Engineer
Software Engineer
A Software Engineer designs, builds, and tests software. This course may be useful for developing the data analysis skills that are increasingly important for software engineers.
Actuary
Actuary
An Actuary analyzes and manages risks for insurance companies. This course may be useful for developing the statistical skills that are used by actuaries.
For more career information including salaries, visit:
OpenCourser.com/course/61up9r/data
Reading list
We've selected 12 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 Analysis in R: Predictive Analysis with Regression.
Save
A comprehensive and authoritative reference on deep learning, covering the theory, algorithms, and applications of this rapidly evolving field. Provides a solid foundation for further exploration of deep learning techniques.
A widely used textbook that provides a broad overview of statistical learning methods, including regression, classification, and clustering. Useful as a reference for more advanced topics in predictive analytics.
A popular and well-respected textbook that provides a comprehensive overview of statistical learning methods. Offers a solid foundation in the theory and application of regression, classification, and clustering techniques.
A valuable resource for undergraduate and graduate students in statistics, econometrics, and data science, as well as a reference for practitioners in social science and business.
Save
Provides a comprehensive introduction to the use of R in data science, covering data manipulation, visualization, modeling, and communication.
A practical and comprehensive guide to machine learning in R, covering a wide range of topics from data preprocessing to model evaluation. Provides hands-on experience with real-world datasets.
A unique and insightful perspective on machine learning, written by one of the pioneers in the field. Provides a high-level understanding of the fundamental principles and challenges of machine learning.
Covers the fundamentals of interpretable machine learning, including linear models, decision trees, and ensemble methods. Provides practical guidance on how to build and interpret interpretable models in a variety of real-world applications.
A practical guide to creating elegant and effective visualizations in R using the ggplot2 package, covering various chart types and advanced techniques for data visualization.
A classic text by two well-known statisticians that covers the foundations of both linear and generalized linear regression. Provides a unique perspective on the use of statistics in the real world.
A valuable resource for undergraduate and graduate students in statistics, data analysis, and related fields. Provides a comprehensive overview of statistical methods used in data analysis, with a focus on practical applications.
A friendly and accessible introduction to machine learning, covering the basic concepts and techniques in a clear and concise manner.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/61up9r/data
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Arimoro Olayinka Imisioluwa
Language
English
Good to know
Introduces the learner to the principles of predictive analytics, a valuable skill for data analysts
Provides practical experience in building and interpreting regression models, essential for data analysis
Covers model assessments and diagnostic plots, crucial for evaluating model performance
Taught by experienced instructors Arimoro Olayinka Imisioluwa, recognized for their expertise in predictive analytics and data science
Suitable for learners familiar with using R for data analysis
Assumes some background knowledge in data analysis
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