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Manohar Mulchandani
This is the 3rd Guided Project in the "Programming for Everyone" series. In this Guided Project, we will learn how to work with data. Specifically, we will learn how to organize data in data frames and use the "dplyr" package to process data.
We will also learn how Shiny lays out the UI for Web Apps.
We will put this knowledge to use to enhance the Shiny app that we created in the 2nd Guided Project. Specifically we will add the ability to support multiple cities.
This Guided Project was created by a Coursera community member.
Register for this course and see more details by visiting:
OpenCourser.com/course/p012ln/programming
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Designed for individuals with foundational knowledge in data processing
Provides practical experience with dataframes using the dplyr package
Extends existing Shiny app to enhance functionality
Taught by experienced instructors, Manohar Mulchandani
Part of the comprehensive "Programming for Everyone" series
Fits within academic curricula, building upon core data science concepts
Save this course
Save Programming for Everyone : Working with Data to your list so you can find it easily later:
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Reviews summary
Easy intro to working with data
The single review of this course states that it is very interesting and well suited for beginners who want to learn about R and RStudio. If you are a beginner who wants to work with data and learn R and RStudio, this course is likely a good choice for you.
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 Programming for Everyone : Working with Data with these
activities:
Join a study group or participate in online forums for the course
Show steps
Engaging with peers can provide diverse perspectives, clarify concepts, and enhance your learning experience.
Show steps
-
Join a study group and schedule regular meetings to discuss course material.
-
Participate in online forums to ask questions, share insights, and connect with classmates.
Practice working with Shiny for interactive data visualization
Show steps
Refreshing your skills in Shiny will ensure that you can effectively use it to create interactive data visualizations in the course.
Browse courses on
Shiny
Show steps
-
Review the basics of Shiny app development.
-
Create a simple Shiny app to visualize data.
-
Explore the Shiny documentation and resources to learn about advanced features.
Review the book 'Data Visualization: A Practical Introduction'
Show steps
This book provides a comprehensive overview of data visualization principles and techniques, which will enhance your understanding of data organization and manipulation in R.
View
Data Visualization: Representing Information on...
on Amazon
Show steps
-
Read Chapter 1-3 to gain a foundational understanding of data visualization concepts.
-
Complete the exercises in Chapter 3 to practice creating basic visualizations.
-
Review the case studies in Chapter 4 to learn how data visualization is applied in real-world scenarios.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice using RStudio for data exploration and visualization
Show steps
RStudio is the primary tool used in this course. Practicing with RStudio will enhance your proficiency in data manipulation and visualization tasks.
Browse courses on
RStudio
Show steps
-
Explore the RStudio interface and familiarize yourself with its components.
-
Load a dataset into RStudio and practice basic data exploration techniques.
-
Create a simple visualization using ggplot2.
Complete the Data Visualization with R tutorial series
Show steps
This tutorial series provides a structured approach to learning data visualization in R. It will complement the course material and reinforce your understanding of key concepts.
Browse courses on
Data Visualization
Show steps
-
Watch the introductory video to get an overview of the tutorial series.
-
Follow along with the tutorial videos to create various types of visualizations.
-
Practice creating visualizations based on the examples provided in the tutorials.
Create a data visualization dashboard for a specific dataset
Show steps
Creating a dashboard will allow you to apply your data visualization skills to a practical scenario, enhancing your understanding of data presentation and communication.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset and define the key metrics you want to visualize.
-
Design the layout and structure of your dashboard.
-
Create interactive visualizations using Shiny.
-
Present your dashboard to peers or instructors for feedback.
Volunteer at a data science or visualization organization
Show steps
Volunteering will provide you with hands-on experience and expose you to real-world data science and visualization projects.
Show steps
-
Research data science or visualization organizations in your area.
-
Identify projects that align with your interests and skills.
-
Contact the organization and express your interest in volunteering.
Join a study group or participate in online forums for the course
Show steps
Engaging with peers can provide diverse perspectives, clarify concepts, and enhance your learning experience.
Show steps
Show steps
Show steps
- Join a study group and schedule regular meetings to discuss course material.
- Participate in online forums to ask questions, share insights, and connect with classmates.
Practice working with Shiny for interactive data visualization
Show steps
Refreshing your skills in Shiny will ensure that you can effectively use it to create interactive data visualizations in the course.
Browse courses on
Shiny
Show steps
Show steps
Show steps
- Review the basics of Shiny app development.
- Create a simple Shiny app to visualize data.
- Explore the Shiny documentation and resources to learn about advanced features.
Review the book 'Data Visualization: A Practical Introduction'
Show steps
This book provides a comprehensive overview of data visualization principles and techniques, which will enhance your understanding of data organization and manipulation in R.
View
Data Visualization: Representing Information on...
on Amazon
Show steps
Show steps
Show steps
- Read Chapter 1-3 to gain a foundational understanding of data visualization concepts.
- Complete the exercises in Chapter 3 to practice creating basic visualizations.
- Review the case studies in Chapter 4 to learn how data visualization is applied in real-world scenarios.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice using RStudio for data exploration and visualization
Show steps
RStudio is the primary tool used in this course. Practicing with RStudio will enhance your proficiency in data manipulation and visualization tasks.
Browse courses on
RStudio
Show steps
Show steps
Show steps
- Explore the RStudio interface and familiarize yourself with its components.
- Load a dataset into RStudio and practice basic data exploration techniques.
- Create a simple visualization using ggplot2.
Complete the Data Visualization with R tutorial series
Show steps
This tutorial series provides a structured approach to learning data visualization in R. It will complement the course material and reinforce your understanding of key concepts.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Watch the introductory video to get an overview of the tutorial series.
- Follow along with the tutorial videos to create various types of visualizations.
- Practice creating visualizations based on the examples provided in the tutorials.
Create a data visualization dashboard for a specific dataset
Show steps
Creating a dashboard will allow you to apply your data visualization skills to a practical scenario, enhancing your understanding of data presentation and communication.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset and define the key metrics you want to visualize.
- Design the layout and structure of your dashboard.
- Create interactive visualizations using Shiny.
- Present your dashboard to peers or instructors for feedback.
Volunteer at a data science or visualization organization
Show steps
Volunteering will provide you with hands-on experience and expose you to real-world data science and visualization projects.
Show steps
Show steps
Show steps
- Research data science or visualization organizations in your area.
- Identify projects that align with your interests and skills.
- Contact the organization and express your interest in volunteering.
Career center
Learners who complete Programming for Everyone : Working with Data will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data from various sources. Using the skills they develop in this course, Data Analysts may be able to efficiently process and organize data in data frames, using the "dplyr" package to process data to prepare it for analysis. This course is suggested for professionals who are looking to enter this field or advance their current role in it.
Data Scientist
Data Scientist
Data Scientists use statistical and analytical methods to extract insights from data. The skills learned in this course may help aspiring Data Scientists in their study, as they may be able to apply their understanding of data frames and the "dplyr" package to real-world datasets they work with. This course can provide foundational knowledge that is relevant to this role.
Statistician
Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data, which may include using statistical software to organize and process data. This course can help build a foundation for those interested in a career as a Statistician, providing them with an understanding of data frames and the "dplyr" package, which can be useful tools for handling large datasets.
Market Researcher
Market Researcher
Market Researchers help businesses understand their target market and make informed decisions based on data analysis. This course may be helpful for Market Researchers, as it can provide them with skills in organizing and processing data in data frames. The "dplyr" package is commonly used for data manipulation and analysis, making this course relevant to this role.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course can help build foundational skills for those considering this role, providing an introduction to handling and processing data in data frames, which is essential for data analysis in this field.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software applications. While this course may not directly relate to all aspects of software engineering, the skills learned in working with data frames and the "dplyr" package may be useful for software engineers involved in data-related projects.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers work on developing and deploying machine learning models for various applications. This course may be helpful for those interested in this role, as understanding data organization and manipulation in data frames is essential for preparing data for machine learning algorithms.
Data Engineer
Data Engineer
Data Engineers are responsible for designing, building, and maintaining data systems that store and process large amounts of data. This course can provide foundational knowledge for aspiring Data Engineers, introducing them to the concepts of data organization and manipulation using data frames.
Business Analyst
Business Analyst
Business Analysts help businesses understand their operations and make data-driven decisions. This course may be helpful for those considering this role, as it can provide them with skills in organizing and analyzing data to gain insights for business decision-making.
Financial Analyst
Financial Analyst
Financial Analysts use data analysis to make investment recommendations. This course may be helpful for those interested in this role, as it can provide them with essential skills in organizing and processing financial data in data frames.
Information Technology Manager
Information Technology Manager
IT Managers plan, implement, and oversee the use of technology within an organization. While this course may not directly relate to all aspects of IT management, the skills learned in working with data frames and the "dplyr" package may be useful for managing data-related projects and initiatives.
Healthcare Data Analyst
Healthcare Data Analyst
Healthcare Data Analysts analyze data to improve patient care and healthcare outcomes. This course can help build a foundation for those interested in this role, as it can provide an understanding of data frames and the "dplyr" package, which are commonly used for data analysis in healthcare.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data analysis to improve the efficiency of business operations. This course may be helpful for those considering this role, as it can provide them with skills in data organization and analysis, which are essential for optimizing business processes.
Web Developer
Web Developer
Web Developers are responsible for designing, developing, and maintaining websites. While this course may not directly relate to all aspects of web development, the skills learned in working with data frames and the "dplyr" package may be useful for web developers who work on data-driven websites or applications.
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists create visual representations of data to make it more accessible and easier to understand. This course may be helpful for those interested in this role, as it can provide them with skills in organizing and manipulating data in data frames, which is essential for creating effective data visualizations.
For more career information including salaries, visit:
OpenCourser.com/course/p012ln/programming
Reading list
We've selected ten 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
Programming for Everyone : Working with Data.
Save
Provides a comprehensive introduction to the R programming language and its application to data science. It covers the basics of data manipulation, visualization, and statistical modeling, and it provides many examples and exercises to help readers learn the material.
Provides a comprehensive guide to ggplot2, a powerful data visualization library for R. It covers the basics of ggplot2, as well as more advanced topics such as creating complex visualizations and dashboards. It valuable resource for students and researchers who want to learn more about ggplot2 and its application to data science.
Provides a comprehensive guide to data manipulation with R, covering topics such as data import and export, data cleaning, and data transformation. It valuable resource for students and researchers who want to learn more about data manipulation with R.
Provides a comprehensive guide to statistical inference with R, covering topics such as hypothesis testing, confidence intervals, and regression analysis. It valuable resource for students and researchers who want to learn more about statistical inference with R.
Provides a comprehensive guide to machine learning with R, covering topics such as supervised learning, unsupervised learning, and model evaluation. It valuable resource for students and researchers who want to learn more about machine learning with R.
Provides a comprehensive guide to deep learning with R, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for students and researchers who want to learn more about deep learning with R.
Save
Provides a comprehensive guide to natural language processing with R, covering topics such as text mining, sentiment analysis, and machine translation. It valuable resource for students and researchers who want to learn more about natural language processing with R.
Save
Provides a comprehensive guide to time series analysis with R, covering topics such as time series decomposition, forecasting, and model evaluation. It valuable resource for students and researchers who want to learn more about time series analysis with R.
Provides a comprehensive guide to R programming for data science, covering topics such as data manipulation, visualization, and statistical modeling. It valuable resource for students and researchers who want to learn more about R and its application to data science.
Save
Classic guide to the R language, providing a comprehensive overview of the language and its application to data science. It valuable resource for students and researchers who want to learn more about the fundamentals of the R language.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/p012ln/programming
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Time
1 hours
Via
Coursera
Institution
Coursera Community Project Network
Instructor
Manohar Mulchandani
Good to know
Designed for individuals with foundational knowledge in data processing
Provides practical experience with dataframes using the dplyr package
Extends existing Shiny app to enhance functionality
Taught by experienced instructors, Manohar Mulchandani
Part of the comprehensive "Programming for Everyone" series
Fits within academic curricula, building upon core data science concepts
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