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Chris Shockley
In this 1-hour long project-based course, you will learn how to (complete a training and test set using an R function, practice looking at data distribution using R and ggplot2, Apply a Random Forest model to the data using the FFTrees package in R, and...
Read more
In this 1-hour long project-based course, you will learn how to (complete a training and test set using an R function, practice looking at data distribution using R and ggplot2, Apply a Random Forest model to the data using the FFTrees package in R, and...
Read more
In this 1-hour long project-based course, you will learn how to (complete a training and test set using an R function, practice looking at data distribution using R and ggplot2, Apply a Random Forest model to the data using the FFTrees package in R, and examine the results using a Confusion Matrix.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/qhji8f/data
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possible dealbreakers
Introduces beginners to basic concepts in machine learning
Provides hands-on experience with the Random Forest model
Suitable for learners interested in gaining a practical understanding of machine learning
Course duration is relatively short
Limited scope, only covering a specific machine learning model
Requires learners to have prior knowledge of R programming
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Reviews summary
Rvest package fundamentals
Most learners report that this 1-hour project-based course provides a useful overview of using Hadley Wickham's Rvest package in R. The course is particularly well-suited for learners based in North America. However, some learners found the material to be too basic or outdated, and noted some technical issues with the workspace. Overall, learners who are new to using the Rvest package may find this course helpful as a starting point.
Good starting point for Rvest beginners.
"Good way to start learning Rvest..."
Course provides basic overview of Rvest.
"It was a very quick overview of how to scrape the data..."
Course workspace may not work as expected.
"This project is not maintained and your workspace no longer functions in the same way as the instructor's screen..."
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 Analytics: Scraping Data using Hadley Wickam's Rvest package in R with these
activities:
Review underlying Math and Statistics.
Show steps
Perform a refresher to test and refine mathematical and statistical knowledge and skills prior to beginning this course.
Browse courses on
Statistics
Show steps
-
Review linear algebra concepts
-
Complete practice problems in calculus
-
Study probability distributions
-
Work through hypothesis testing examples
Engage in discussion forums
Show steps
Foster peer learning and clarify concepts through active participation in course discussion forums.
Show steps
-
Post questions or comments to seek clarification.
-
Respond to queries and provide insights to assist peers.
-
Engage in constructive discussions and debates on course topics.
Follow regression-based tutorials
Show steps
Watch and complete interactive tutorials designed to develop foundational Regression skills
Show steps
-
Watch video tutorials on linear regression.
-
Follow written tutorials on logistic regression.
-
Complete interactive simulations to practice regression implementation.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Complete decision tree challenges
Show steps
Tackle coding challenges to enhance understanding of decision tree algorithms and implementation
Show steps
-
Solve LeetCode problems focused on decision tree algorithms.
-
Participate in online coding competitions related to decision trees.
Develop a data visualization project
Show steps
Solidify data visualization skills by creating a compelling visual representation of course concepts
Show steps
-
Identify a dataset relevant to the course.
-
Clean and prepare the data for visualization.
-
Choose appropriate data visualization techniques.
-
Create interactive or static visualizations using tools like Tableau or ggplot2.
-
Present and explain the visualization to peers or instructors.
Build a machine learning model
Show steps
Apply course concepts to a practical project by developing a machine learning model from scratch
Show steps
-
Define the problem and gather the necessary data.
-
Choose and apply appropriate machine learning algorithms.
-
Train and evaluate the model using cross-validation techniques.
-
Deploy the model and monitor its performance.
Participate in a machine learning hackathon
Show steps
Test and expand knowledge by participating in a competitive environment that requires application of course concepts
Show steps
-
Identify and register for a relevant machine learning hackathon.
-
Form a team or work individually on a project that aligns with the hackathon's theme.
-
Develop a solution that showcases your skills and understanding of machine learning techniques.
-
Present your project and compete against other participants.
Review underlying Math and Statistics.
Show steps
Perform a refresher to test and refine mathematical and statistical knowledge and skills prior to beginning this course.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review linear algebra concepts
- Complete practice problems in calculus
- Study probability distributions
- Work through hypothesis testing examples
Engage in discussion forums
Show steps
Foster peer learning and clarify concepts through active participation in course discussion forums.
Show steps
Show steps
Show steps
- Post questions or comments to seek clarification.
- Respond to queries and provide insights to assist peers.
- Engage in constructive discussions and debates on course topics.
Follow regression-based tutorials
Show steps
Watch and complete interactive tutorials designed to develop foundational Regression skills
Show steps
Show steps
Show steps
- Watch video tutorials on linear regression.
- Follow written tutorials on logistic regression.
- Complete interactive simulations to practice regression implementation.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Complete decision tree challenges
Show steps
Tackle coding challenges to enhance understanding of decision tree algorithms and implementation
Show steps
Show steps
Show steps
- Solve LeetCode problems focused on decision tree algorithms.
- Participate in online coding competitions related to decision trees.
Develop a data visualization project
Show steps
Solidify data visualization skills by creating a compelling visual representation of course concepts
Show steps
Show steps
Show steps
- Identify a dataset relevant to the course.
- Clean and prepare the data for visualization.
- Choose appropriate data visualization techniques.
- Create interactive or static visualizations using tools like Tableau or ggplot2.
- Present and explain the visualization to peers or instructors.
Build a machine learning model
Show steps
Apply course concepts to a practical project by developing a machine learning model from scratch
Show steps
Show steps
Show steps
- Define the problem and gather the necessary data.
- Choose and apply appropriate machine learning algorithms.
- Train and evaluate the model using cross-validation techniques.
- Deploy the model and monitor its performance.
Participate in a machine learning hackathon
Show steps
Test and expand knowledge by participating in a competitive environment that requires application of course concepts
Show steps
Show steps
Show steps
- Identify and register for a relevant machine learning hackathon.
- Form a team or work individually on a project that aligns with the hackathon's theme.
- Develop a solution that showcases your skills and understanding of machine learning techniques.
- Present your project and compete against other participants.
Career center
Learners who complete Data Analytics: Scraping Data using Hadley Wickam's Rvest package in R will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts play a vital role in the success of modern businesses by analyzing data to uncover trends and patterns that can help businesses make better decisions. The skills learned in this course, such as data scraping, data visualization, and statistical modeling, are essential for Data Analysts. By completing this course, you will gain a strong foundation in the skills and knowledge needed to succeed as a Data Analyst.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, analyzing, and interpreting data to help businesses make better decisions. The skills learned in this course, such as data scraping, data visualization, and statistical modeling, are essential for Data Scientists. By completing this course, you will gain a strong foundation in the skills and knowledge needed to succeed as a Data Scientist.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for developing and implementing machine learning solutions to solve real-world problems. The skills learned in this course, such as data scraping, data preprocessing, and model building, are essential for Machine Learning Engineers. By completing this course, you will gain a strong foundation in the skills and knowledge needed to succeed as a Machine Learning Engineer.
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists are responsible for creating visualizations that communicate data in a clear and concise way. The skills learned in this course, such as data visualization and statistical modeling, are essential for Data Visualization Specialists. By completing this course, you will gain a strong foundation in the skills and knowledge needed to succeed as a Data Visualization Specialist.
Data Engineer
Data Engineer
Data Engineers are responsible for designing and building the systems that store and process data. The skills learned in this course, such as data scraping, data preprocessing, and data warehousing, are essential for Data Engineers. By completing this course, you will gain a strong foundation in the skills and knowledge needed to succeed as a Data Engineer.
Statistician
Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data. The skills learned in this course, such as data scraping, data visualization, and statistical modeling, are essential for Statisticians. By completing this course, you will gain a strong foundation in the skills and knowledge needed to succeed as a Statistician.
Management Consultant
Management Consultant
Management Consultants are responsible for advising businesses on how to improve their operations. The skills learned in this course, such as data scraping, data visualization, and statistical modeling, can be helpful to Management Consultants in their work. By completing this course, you will gain a better understanding of the data analysis process and how it can be used to improve business operations.
Business Analyst
Business Analyst
Business Analysts are responsible for analyzing business processes and identifying areas for improvement. The skills learned in this course, such as data scraping, data visualization, and statistical modeling, can be helpful to Business Analysts in their work. By completing this course, you will gain a better understanding of the data analysis process and how it can be used to improve business operations.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts are responsible for analyzing operational data to identify areas for improvement. The skills learned in this course, such as data scraping, data visualization, and statistical modeling, can be helpful to Operations Research Analysts in their work. By completing this course, you will gain a better understanding of the data analysis process and how it can be used to improve operational efficiency.
Financial Analyst
Financial Analyst
Financial Analysts are responsible for analyzing financial data to make recommendations on investments and financial decisions. The skills learned in this course, such as data scraping, data visualization, and statistical modeling, can be helpful to Financial Analysts in their work. By completing this course, you will gain a better understanding of the data analysis process and how it can be used to make better financial decisions.
Marketing Analyst
Marketing Analyst
Marketing Analysts are responsible for analyzing marketing data to identify trends and patterns that can help businesses improve their marketing campaigns. The skills learned in this course, such as data scraping, data visualization, and statistical modeling, can be helpful to Marketing Analysts in their work. By completing this course, you will gain a better understanding of the data analysis process and how it can be used to improve marketing campaigns.
Actuary
Actuary
Actuaries are responsible for assessing and managing financial risk. The skills learned in this course, such as data scraping, data visualization, and statistical modeling, can be helpful to Actuaries in their work. By completing this course, you will gain a better understanding of the data analysis process and how it can be used to assess and manage financial risk.
Database Administrator
Database Administrator
Database Administrators are responsible for managing and maintaining databases. The skills learned in this course, such as data scraping, data preprocessing, and data warehousing, can be helpful to Database Administrators in their work. By completing this course, you will gain a better understanding of the data analysis process and how it can be used to improve database management.
Web Developer
Web Developer
Web Developers are responsible for designing and developing websites. The skills learned in this course, such as data scraping, data visualization, and statistical modeling, can be helpful to Web Developers in their work. By completing this course, you will gain a better understanding of the data analysis process and how it can be used to improve website design and development.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software applications. The skills learned in this course, such as data scraping, data visualization, and statistical modeling, can be helpful to Software Engineers in their work. By completing this course, you will gain a better understanding of the data analysis process and how it can be used to improve software applications.
For more career information including salaries, visit:
OpenCourser.com/course/qhji8f/data
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
Data Analytics: Scraping Data using Hadley Wickam's Rvest package in R.
Provides a comprehensive overview of statistical learning techniques. Useful as a reference or for additional reading.
Save
Provides a comprehensive overview of data science in R, including data manipulation, visualization, and modeling. Useful as a reference or for additional reading.
Covers machine learning concepts and techniques in R. Useful as a reference or for additional reading.
Provides a comprehensive overview of data science. Useful as a reference or for additional reading.
Covers data visualization in R using the ggplot2 library. Useful as a reference or for additional reading.
Provides a comprehensive overview of data science from scratch. Useful as a reference or for additional reading.
Save
Provides a comprehensive overview of deep learning. Useful for learners who want to explore advanced machine learning techniques.
Covers fundamental R programming concepts and techniques. Useful as a reference or for additional reading.
Provides a gentle introduction to data analytics. Useful as a reference or for additional reading.
Covers data analysis in Python. Useful for learners who want to explore another programming language for data science.
Save
Covers data mining techniques in R. Useful for learners who want to explore advanced data analysis techniques.
Covers big data analytics in R. Useful for learners who want to explore working with large datasets.
Provides a business-oriented perspective on data science. Useful as a reference or for additional reading.
Save
Provides a comprehensive overview of predictive modeling techniques. Useful as a reference or for additional reading.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/qhji8f/data
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Chris Shockley
Good to know
Introduces beginners to basic concepts in machine learning
Provides hands-on experience with the Random Forest model
Suitable for learners interested in gaining a practical understanding of machine learning
Course duration is relatively short
Limited scope, only covering a specific machine learning model
Requires learners to have prior knowledge of R programming
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