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Arimoro Olayinka Imisioluwa
Welcome to this project-based course Google Trends Analysis using R. In this project, you will learn how to perform extensive exploratory data analysis on Google trends data using different R packages. By the end of this 2-hour long project, you will understand how to get data from Google trends into your RStudio workspace. Also, you will learn how to use different dplyr verbs such as the select verb, filter verb, arrange verb, and mutate verb to manipulate the Google trends data about the word “Covid.” By extension, you will learn how to use the ggplot2 package and other advanced R plotting libraries to render beautiful plots and...
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Welcome to this project-based course Google Trends Analysis using R. In this project, you will learn how to perform extensive exploratory data analysis on Google trends data using different R packages. By the end of this 2-hour long project, you will understand how to get data from Google trends into your RStudio workspace. Also, you will learn how to use different dplyr verbs such as the select verb, filter verb, arrange verb, and mutate verb to manipulate the Google trends data about the word “Covid.” By extension, you will learn how to use the ggplot2 package and other advanced R plotting libraries to render beautiful plots and maps from the data returned from using the dplyr verbs. You will learn how to use the R markdown file to organize your work and how to knit your code into an HTML document for publishing. Although you do not need to be a data analyst expert or data scientist to succeed in this guided project, it requires an intermediate knowledge of using R, especially working with the dplyr and ggplot2 packages. Therefore, to complete this project, it is required that you have prior experience with using R dplyr and ggplot2 packages. Please don’t get discouraged; I’ve got you covered. If you are not familiar with working with these packages I have mentioned, then you have to take my projects on “Data Manipulation with dplyr in R” and “Data Visualization using dplyr and ggplot2 in R”. So, taking these projects will give the needed requisite to go ahead with this project on Google Trends Analysis using R. However, if you are comfortable with working with the dplyr and ggplot2 packages, please join me on this wonderful ride! Let’s get our hands dirty!
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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Instructs the use of industry-standard R packages, such as dplyr and ggplot2, which are commonly used for data manipulation and visualization
Provides a foundational understanding of Google Trends and its use in data analysis, which can be valuable for research, marketing, and business decision-making
Reinforces previous knowledge of dplyr and ggplot2 packages, making it suitable for learners who have an intermediate level of proficiency in R programming
Requires prior experience with R and its dplyr and ggplot2 packages, potentially limiting accessibility for beginners
Utilizes hands-on labs and practical examples, promoting active learning and skill development

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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 Google Trends Analysis using R with these activities:
Identify a mentor for guidance on dplyr and data manipulation
Seek guidance from an experienced individual to enhance your learning journey and gain valuable insights into dplyr and data manipulation.
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  • Attend industry events or online forums
  • Reach out to professionals in your network
  • Consider hiring a tutor or joining a mentorship program
Review R dplyr package
Review the basics of the dplyr package to ensure familiarity with its functions and syntax.
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  • Revisit the dplyr documentation
  • Complete practice problems using dplyr
Compile a list of resources on dplyr and data manipulation
Create a comprehensive resource list on dplyr and data manipulation, serving as a valuable reference for your learning and future endeavors.
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  • Conduct research to identify relevant resources
  • Organize and categorize the resources
  • Share the resource list with others
Six other activities
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Follow a tutorial on data manipulation with dplyr
Follow a structured tutorial to reinforce your understanding of dplyr functions and techniques.
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  • Identify a tutorial that covers the desired concepts
  • Follow the tutorial step-by-step
  • Experiment with the techniques learned
Participate in a peer study group for dplyr
Engage with peers to discuss concepts, share knowledge, and learn from different perspectives, enhancing your understanding of dplyr.
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  • Join or create a study group
  • Participate in discussions and share your insights
  • Review and provide feedback on others' work
Volunteer your skills on a data manipulation project
Gain practical experience and contribute to a meaningful cause by volunteering your data manipulation skills on a project.
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  • Identify organizations that need data manipulation assistance
  • Contact the organizations and offer your services
  • Participate in the project and apply your dplyr skills
Complete practice exercises on data manipulation with dplyr
Solve practice exercises to solidify your understanding of dplyr and enhance your proficiency.
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  • Find practice exercises online or in textbooks
  • Attempt to solve the exercises independently
  • Review solutions and identify areas for improvement
Write a blog post or article on a topic related to dplyr
Consolidate your understanding by creating a blog post or article, sharing your knowledge with others and reinforcing your own learning.
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Show steps
  • Choose a topic and conduct research
  • Write the blog post or article
  • Publish your work and share it with others
Create a data manipulation project using dplyr
Apply your skills by creating a project that involves data manipulation using dplyr, fostering deeper understanding and practical application.
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Show steps
  • Define the project scope and goals
  • Gather and prepare the necessary data
  • Perform data manipulation tasks using dplyr
  • Analyze and interpret the results

Career center

Learners who complete Google Trends Analysis using R will develop knowledge and skills that may be useful to these careers:
Data Analyst
Data analysts apply their skills in data analysis, modeling, and statistical programming to solve business problems. This course on Google Trends Analysis using R is an excellent resource for aspiring data analysts. The course covers the data manipulation and visualization techniques that are essential for data analysis and will provide a strong foundation for a career as a data analyst.
Data Scientist
Data scientists combine their expertise in statistics, computer science, and business to solve complex business challenges using data. This course on Google Trends Analysis using R will provide you with the skills in data analysis, visualization, and statistical modeling that are essential for a successful career as a data scientist. The course will help you develop the critical thinking and problem-solving skills that data scientists need to succeed.
Business Intelligence Analyst
Business intelligence analysts use data to help businesses make informed decisions. This course on Google Trends Analysis using R will provide you with the skills in data analysis, visualization, and statistical modeling that are essential for a successful career as a business intelligence analyst.
Market Researcher
Market researchers collect and analyze data to identify market trends and customer behavior. This course on Google Trends Analysis using R is an excellent resource for aspiring market researchers. The course covers the data analysis and visualization techniques that are essential for market research and will provide you with a strong foundation for a career as a market researcher.
Quantitative Researcher
Quantitative researchers are responsible for designing and conducting research studies that use statistical methods to analyze data. This course on Google Trends Analysis using R will provide you with the skills in data analysis, visualization, and statistical modeling that are essential for a successful career as a quantitative researcher. The course will help you develop the critical thinking and problem-solving skills that quantitative researchers need to succeed.
Data Engineer
Data engineers are responsible for designing, building, and maintaining the infrastructure that stores and processes data. This course on Google Trends Analysis using R will provide you with the skills in data analysis, visualization, and statistical modeling that are essential for a successful career as a data engineer. The course will help you develop the critical thinking and problem-solving skills that data engineers need to succeed.
Financial Analyst
Financial analysts use data to evaluate the financial performance of companies and make investment recommendations. This course on Google Trends Analysis using R will provide you with the skills in data analysis, visualization, and statistical modeling that are essential for a successful career as a financial analyst.
Statistician
Statisticians apply their skills in data analysis, modeling, and statistical programming to solve problems in a variety of fields. This course on Google Trends Analysis using R will provide you with the skills in data analysis, visualization, and statistical modeling that are essential for a successful career as a statistician.
Software Engineer
Software engineers apply their skills in computer science to design, develop, and maintain software applications. This course on Google Trends Analysis using R will provide you with the skills in data analysis, visualization, and statistical modeling that are essential for software engineers who work with data-driven applications.
Risk Analyst
Risk analysts use their skills in mathematics, statistics, and finance to assess risk and uncertainty. This course on Google Trends Analysis using R will provide you with the skills in data analysis, visualization, and statistical modeling that are essential for risk analysts.
Operations Research Analyst
Operations research analysts use their skills in mathematics, statistics, and computer science to solve problems in a variety of industries. This course on Google Trends Analysis using R will provide you with the skills in data analysis, visualization, and statistical modeling that are essential for operations research analysts.
Actuary
Actuaries use their skills in mathematics, statistics, and finance to assess risk and uncertainty. This course on Google Trends Analysis using R will provide you with the skills in data analysis, visualization, and statistical modeling that are essential for actuaries.
Machine Learning Engineer
Machine learning engineers use their skills in computer science, statistics, and mathematics to design and build machine learning models. This course on Google Trends Analysis using R will provide you with the skills in data analysis and visualization that are essential for machine learning engineers who work with data-driven models.
Data Governance Analyst
Data governance analysts use their skills in data analysis, data management, and business process analysis to ensure that data is used in a consistent and responsible manner. This course on Google Trends Analysis using R will provide you with the skills in data analysis and data management that are essential for data governance analysts.
Data Visualization Engineer
Data visualization engineers use their skills in data analysis, visualization, and computer science to create interactive data visualizations. This course on Google Trends Analysis using R will provide you with the skills in data analysis and visualization that are essential for data visualization engineers.

Reading list

We've selected seven 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 Google Trends Analysis using R.
Comprehensive guide to the dplyr package, which is one of the most popular R packages for data manipulation. It covers all of the core dplyr functions, as well as more advanced topics such as grouping, subsetting, and joining.
Provides a practical introduction to data visualization. It covers the basics of creating charts and graphs, as well as more advanced topics such as data mapping and interactive visualization.
Provides a comprehensive guide to R programming. It covers all of the core R concepts, as well as more advanced topics such as object-oriented programming and statistical modeling.
Provides a comprehensive overview of data analysis techniques, with a focus on business, economics, and finance. It covers all of the core data analysis concepts, as well as more advanced topics such as time series analysis and forecasting.
Provides a comprehensive overview of data science, with a focus on business applications. It covers all of the core data science concepts, as well as more advanced topics such as machine learning and artificial intelligence.
Provides a comprehensive introduction to Python for data analysis. It covers all of the core Python concepts, as well as more advanced topics such as data manipulation, data visualization, and machine learning.

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