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Data Analytics

Scraping Data using Hadley Wickam's Rvest package in R

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 examine the results using a Confusion Matrix. Note: This course works best for learners who are based in the North America region. Weâ€™re currently working on providing the same experience in other regions.

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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 However, some learners found the material to be too basic or outdated, and noted some technical issues with the workspace.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.

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Perform a refresher to test and refine mathematical and statistical knowledge and skills prior to beginning this course.

Browse courses on Statistics

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Review linear algebra concepts
Complete practice problems in calculus
Study probability distributions
Work through hypothesis testing examples

Engage in discussion forums

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Foster peer learning and clarify concepts through active participation in course discussion forums.

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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

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Watch and complete interactive tutorials designed to develop foundational Regression skills

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Watch video tutorials on linear regression.
Follow written tutorials on logistic regression.
Complete interactive simulations to practice regression implementation.

Four other activities

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Complete decision tree challenges

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Tackle coding challenges to enhance understanding of decision tree algorithms and implementation

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Solve LeetCode problems focused on decision tree algorithms.
Participate in online coding competitions related to decision trees.

Develop a data visualization project

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Solidify data visualization skills by creating a compelling visual representation of course concepts

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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

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Apply course concepts to a practical project by developing a machine learning model from scratch

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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

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Test and expand knowledge by participating in a competitive environment that requires application of course concepts

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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 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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Data Visualization Specialist

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.

See salaries and explore the career path for Data Engineer

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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Management Consultant

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.

See salaries and explore the career path for Business 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.

See salaries and explore the career path for Operations Research 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.

See salaries and explore the career path for Financial 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.

See salaries and explore the career path for Marketing Analyst

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.

See salaries and explore the career path for Actuary

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.

See salaries and explore the career path for Database Administrator

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.

See salaries and explore the career path for Web Developer

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.

See salaries and explore the career path for Software Engineer

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.

The Elements of Statistical Learning

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Provides a comprehensive overview of statistical learning techniques. Useful as a reference or for additional reading.

The Elements of Statistical Learning: Data Mining,...

Hardcover

$$$

Data Analytics

Scraping Data using Hadley Wickam's Rvest package in R

Three deals to help you save

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Reviews summary

Rvest package fundamentals

Activities

Career center

Reading list

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