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Jose Luis Rodriguez
By the end of this project, you will create, clean, explore and analyze San Francisco’s building permit public data. We will use OpenStreetMap API to find the geo-coordinates of buildings using R and RStudio and we will analyze the final results in Tableau. You will learn basic data cleaning techniques using R, create a function to make requests to the OpenStreeMaps API and leverage Tableau to generate insights.
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/iamlmu/analyze
What's inside
Syllabus
Project Overview
On this project you will create, clean and explore San Francisco’s building permit public data. We will use OpenStreetMap API to find the geo coordinates of buildings and analyze the final results in Tableau. By the end of this project you will be familiar with some basic data cleaning techniques using R and you will be capable of leveraging Tableau to generate insights.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches fundamental data cleaning techniques using R, which is standard in the data analysis industry
Develops proficiency in leveraging Tableau to generate insights, which is a highly sought-after skill in data analytics
Emphasizes practical application through the use of real-world San Francisco building permit data, providing learners with hands-on experience
Requires familiarity with basic programming concepts and statistical analysis, catering to learners with some prior experience in data handling
Focuses on a specific domain (San Francisco building permits), which may limit its applicability to other contexts
Course content may need to be updated as the OpenStreetMap API and Tableau software evolve over time
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Save Analyze City Data Using R and Tableau to your list so you can find it easily later:
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Reviews summary
Data analysis with r and tableau
Learners say that they have mixed feelings about this course that uses R and Tableau to analyze city data. While there are positive experiences, there are some negative experiences to note, especially for those who are new to R. Still, most students enjoyed the course and found it helpful.
Learners found the combination of R and Tableau helpful.
"Excellent course, I really got a lot out of it."
"Probably, this is one of the shortest guiding projects that teaches you fetching data with R and displaying them in Tableau."
The course may pose challenges for those new to R.
"If you are not currently comfortable working with R, I wouldn't recommend this class."
A few learners found that the course had some technical issues.
"The response time of the Rhythm platform is too slow."
"Cloud desktop is very laggy even writing something is not responsive."
"Candidates are facing problems in the code chunks given and there is no response from the moderator/teacher."
There were mixed thoughts on the instructor's engagement with students.
"Thank you Jose Luis Rodriguez"
"Candidates are facing problems in the code chunks given and there is no response from the moderator/teacher."
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 Analyze City Data Using R and Tableau with these
activities:
Review basic data analysis concepts
Show steps
Review basic data analysis concepts, such as descriptive statistics, data cleaning, and data visualization, to ensure a strong foundation for the course.
Browse courses on
Data Analysis
Show steps
-
Read textbooks or online articles on data analysis fundamentals
-
Review lecture notes or slides from previous data analysis courses
-
Complete practice problems or exercises on data analysis concepts
Read 'Spatial Data Analysis with R'
Show steps
Read 'Spatial Data Analysis with R' to gain a comprehensive understanding of spatial data analysis techniques and their application in R.
View
Applied Spatial Data Analysis with R
on Amazon
Show steps
-
Read the assigned chapters or sections from the book
-
Take notes and highlight important concepts
-
Complete the practice exercises or problems
-
Discuss the concepts with peers or instructors
Participate in peer-led discussions
Show steps
Engage in peer-led discussions to exchange knowledge, clarify concepts, and provide support to fellow learners.
Show steps
-
Join or create a study group with other students enrolled in the course
-
Meet regularly to discuss course materials, solve problems, and exchange ideas
-
Collaborate on assignments and provide feedback to improve understanding
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice data cleaning techniques
Show steps
Practice data cleaning techniques in R to improve proficiency in handling and preparing data for analysis.
Browse courses on
Data Cleaning
Show steps
-
Download and explore a dataset from a public repository
-
Identify and remove duplicate or missing values
-
Convert data types and handle categorical variables
-
Validate the cleaned dataset and ensure data integrity
Follow tutorials on spatial analysis with R
Show steps
Follow tutorials on spatial analysis with R to enhance understanding of geospatial data and its analysis techniques.
Browse courses on
Spatial Analysis
Show steps
-
Identify reputable online tutorials or courses on spatial analysis with R
-
Follow the tutorials step-by-step and complete the exercises
-
Apply the learned techniques to a small dataset
-
Discuss or share your findings with peers or instructors
Start a data analysis project
Show steps
Start a data analysis project to apply the concepts and techniques learned in the course to a real-world problem.
Show steps
-
Identify a problem or dataset that interests you
-
Define the research question and objectives
-
Plan and design the analysis approach
-
Collect and clean the data
-
Conduct the analysis and interpret the results
Create a data visualization dashboard
Show steps
Create a data visualization dashboard in Tableau to demonstrate proficiency in presenting data insights effectively and visually.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset and identify the key metrics to visualize
-
Design the dashboard layout and select appropriate chart types
-
Implement the dashboard using Tableau and connect to the dataset
-
Refine the visualizations and add interactivity
-
Present the dashboard and share insights
Contribute to an open-source GIS project
Show steps
Contribute to an open-source GIS project to gain hands-on experience and connect with the GIS community.
Show steps
-
Review the project documentation and codebase
-
Identify a suitable open-source GIS project to contribute to
-
Identify an area where you can make a contribution
-
Implement your changes and submit a pull request
-
Collaborate with other contributors and maintain your contributions
Review basic data analysis concepts
Show steps
Review basic data analysis concepts, such as descriptive statistics, data cleaning, and data visualization, to ensure a strong foundation for the course.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Read textbooks or online articles on data analysis fundamentals
- Review lecture notes or slides from previous data analysis courses
- Complete practice problems or exercises on data analysis concepts
Read 'Spatial Data Analysis with R'
Show steps
Read 'Spatial Data Analysis with R' to gain a comprehensive understanding of spatial data analysis techniques and their application in R.
View
Applied Spatial Data Analysis with R
on Amazon
Show steps
Show steps
Show steps
- Read the assigned chapters or sections from the book
- Take notes and highlight important concepts
- Complete the practice exercises or problems
- Discuss the concepts with peers or instructors
Participate in peer-led discussions
Show steps
Engage in peer-led discussions to exchange knowledge, clarify concepts, and provide support to fellow learners.
Show steps
Show steps
Show steps
- Join or create a study group with other students enrolled in the course
- Meet regularly to discuss course materials, solve problems, and exchange ideas
- Collaborate on assignments and provide feedback to improve understanding
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice data cleaning techniques
Show steps
Practice data cleaning techniques in R to improve proficiency in handling and preparing data for analysis.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Download and explore a dataset from a public repository
- Identify and remove duplicate or missing values
- Convert data types and handle categorical variables
- Validate the cleaned dataset and ensure data integrity
Follow tutorials on spatial analysis with R
Show steps
Follow tutorials on spatial analysis with R to enhance understanding of geospatial data and its analysis techniques.
Browse courses on
Spatial Analysis
Show steps
Show steps
Show steps
- Identify reputable online tutorials or courses on spatial analysis with R
- Follow the tutorials step-by-step and complete the exercises
- Apply the learned techniques to a small dataset
- Discuss or share your findings with peers or instructors
Start a data analysis project
Show steps
Start a data analysis project to apply the concepts and techniques learned in the course to a real-world problem.
Show steps
Show steps
Show steps
- Identify a problem or dataset that interests you
- Define the research question and objectives
- Plan and design the analysis approach
- Collect and clean the data
- Conduct the analysis and interpret the results
Create a data visualization dashboard
Show steps
Create a data visualization dashboard in Tableau to demonstrate proficiency in presenting data insights effectively and visually.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset and identify the key metrics to visualize
- Design the dashboard layout and select appropriate chart types
- Implement the dashboard using Tableau and connect to the dataset
- Refine the visualizations and add interactivity
- Present the dashboard and share insights
Contribute to an open-source GIS project
Show steps
Contribute to an open-source GIS project to gain hands-on experience and connect with the GIS community.
Show steps
Show steps
Show steps
- Review the project documentation and codebase
- Identify a suitable open-source GIS project to contribute to
- Identify an area where you can make a contribution
- Implement your changes and submit a pull request
- Collaborate with other contributors and maintain your contributions
Career center
Learners who complete Analyze City Data Using R and Tableau will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. This course can help you develop the skills needed to succeed in this role by providing you with a foundation in data cleaning techniques and data analysis using R and Tableau.
Business Analyst
Business Analyst
Business Analysts use data to identify opportunities and solve problems within businesses. This course can help you develop the skills needed to succeed in this role by providing you with a foundation in data analysis and visualization using R and Tableau.
Data Scientist
Data Scientist
Data Scientists use data to build models and solve complex problems. This course can help you develop the skills needed to succeed in this role by providing you with a foundation in data cleaning, data analysis, and visualization using R and Tableau.
Data Engineer
Data Engineer
Data Engineers are responsible for building and maintaining the infrastructure that supports data analysis. This course can help you develop the skills needed to succeed in this role by providing you with a foundation in data cleaning and data management using R.
Statistician
Statistician
Statisticians use data to make inferences and predictions. This course can help you develop the skills needed to succeed in this role by providing you with a foundation in data analysis and visualization using R and Tableau.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course may be useful for Software Engineers who are interested in developing data-driven applications.
GIS Analyst
GIS Analyst
GIS Analysts use geographic information systems (GIS) to analyze and visualize data. This course can help you develop the skills needed to succeed in this role by providing you with a foundation in data analysis and visualization using R and Tableau.
Urban Planner
Urban Planner
Urban Planners use data to plan and design cities and towns. This course can help you develop the skills needed to succeed in this role by providing you with a foundation in data analysis and visualization using R and Tableau.
Transportation Planner
Transportation Planner
Transportation Planners use data to plan and design transportation systems. This course can help you develop the skills needed to succeed in this role by providing you with a foundation in data analysis and visualization using R and Tableau.
Environmental Scientist
Environmental Scientist
Environmental Scientists use data to study and protect the environment. This course may be useful for Environmental Scientists who are interested in using data to analyze environmental issues.
Public Policy Analyst
Public Policy Analyst
Public Policy Analysts use data to analyze and evaluate public policies. This course may be useful for Public Policy Analysts who are interested in using data to inform policy decisions.
Market Researcher
Market Researcher
Market Researchers use data to understand consumer behavior and market trends. This course may be useful for Market Researchers who are interested in using data to improve marketing campaigns.
Financial Analyst
Financial Analyst
Financial Analysts use data to analyze financial performance and make investment decisions. This course may be useful for Financial Analysts who are interested in using data to make better investment decisions.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data to improve the efficiency and effectiveness of organizations. This course may be useful for Operations Research Analysts who are interested in using data to solve business problems.
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists use data visualization tools to communicate data insights to stakeholders. This course can help you develop the skills needed to succeed in this role by providing you with a foundation in data visualization using Tableau.
For more career information including salaries, visit:
OpenCourser.com/course/iamlmu/analyze
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
Analyze City Data Using R and Tableau.
Provides a comprehensive introduction to data science, covering topics such as data cleaning, data exploration, data visualization, and machine learning. It valuable resource for anyone who wants to learn more about data science and its applications in business.
Save
Provides a comprehensive introduction to R, a popular programming language for data science and analysis. It covers topics such as data import and export, data manipulation, data visualization, and machine learning.
Save
Provides a comprehensive introduction to R, a popular programming language for data science and analysis. It covers topics such as data import and export, data manipulation, data visualization, and machine learning.
Save
Provides a comprehensive introduction to spatial data analysis using R, a popular programming language for data science and analysis. It covers topics such as spatial data structures, spatial statistics, and geospatial visualization.
Provides a comprehensive introduction to Tableau, a popular data visualization and analysis software. It covers topics such as data preparation, chart creation, and dashboard design.
Provides a comprehensive introduction to GIS (geographic information systems), a software that allows users to create, manage, and analyze geospatial data. It covers topics such as data collection, data management, data analysis, and visualization.
Provides a comprehensive introduction to geostatistics, a branch of statistics that deals with data that has a spatial component. It covers topics such as spatial data structures, spatial statistics, and geospatial visualization.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/iamlmu/analyze
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Effort
90 Minutes
Level
Intermediate
Via
Coursera
Institution
University of Illinois at Urbana-Champaign
Instructor
Jose Luis Rodriguez
Language
English
Good to know
Teaches fundamental data cleaning techniques using R, which is standard in the data analysis industry
Develops proficiency in leveraging Tableau to generate insights, which is a highly sought-after skill in data analytics
Emphasizes practical application through the use of real-world San Francisco building permit data, providing learners with hands-on experience
Requires familiarity with basic programming concepts and statistical analysis, catering to learners with some prior experience in data handling
Focuses on a specific domain (San Francisco building permits), which may limit its applicability to other contexts
Course content may need to be updated as the OpenStreetMap API and Tableau software evolve over time
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