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Dan Appleman
This course covers the fundamentals of data visualization that every developer needs to know regardless of architecture, platform, tools, or language.
Read more
This course covers the fundamentals of data visualization that every developer needs to know regardless of architecture, platform, tools, or language.
Read more
This course covers the fundamentals of data visualization that every developer needs to know regardless of architecture, platform, tools, or language.
At some point in your software development career, you may be called upon to produce a graph or a chart. Delivering on even a simple request can be remarkably complex. How much data is there and where does it live? What kind of chart should you use? Should the chart be rendered on a server or on the client? What kind of clients should you support, and what are the consequences of that choice? This course introduces every aspect of visualization, from story, to numbers, to architecture, to code.
Register for this course and see more details by visiting:
OpenCourser.com/course/ep5gqr/data
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What's inside
Syllabus
Introduction
Charting - 1A
Business Intelligence
Visualization Architecture
Read more
Syllabus
Introduction
Charting - 1A
Business Intelligence
Visualization Architecture
Read more
Hands-On Visualization
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops data visualization skills, which help developers convey information effectively
Taught by Dan Appleman, who are recognized for their work in data visualization
Explores data visualization concepts, which are standard in software development
Covers visualization architecture, which is essential for designing effective data visualizations
Provides hands-on experience with data visualization, which is crucial for practical implementation
Save this course
Save Data Visualization for Developers to your list so you can find it easily later:
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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 Data Visualization for Developers with these
activities:
Read "Data Visualization: A Practical Introduction"
Show steps
Gain a comprehensive foundation by reading this introductory book, providing a clear and engaging overview of data visualization concepts and techniques.
View
Data Visualization: A Successful Design Process...
on Amazon
Show steps
-
Purchase or borrow a copy of the book.
-
Read through the chapters.
-
Take notes and highlight important concepts.
Organize and review course materials
Show steps
Build a strong foundation by organizing and reviewing course materials, ensuring you have a clear understanding of the concepts and topics covered.
Show steps
-
Create a dedicated folder or notebook for course materials.
-
File and organize lecture notes, slides, and assignments.
-
Review materials regularly to reinforce your learning.
Review previous knowledge of data visualization
Show steps
Help refresh your knowledge of data visualization concepts, ensuring a solid foundation for the course.
Browse courses on
Data Visualization
Show steps
-
Review your notes from previous data visualization courses.
-
Read articles or blog posts on data visualization best practices.
-
Complete online quizzes or exercises on data visualization.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete online exercises on data visualization
Show steps
Reinforce your understanding by completing interactive exercises, testing your knowledge and identifying areas for improvement.
Show steps
-
Find online platforms or resources that offer data visualization exercises.
-
Complete a variety of exercises, covering different concepts and techniques.
-
Review your performance and identify areas where you need more practice.
Follow tutorials on data visualization tools
Show steps
Supplement course content by exploring tutorials on specific data visualization tools, enhancing your practical skills.
Show steps
-
Identify a data visualization tool covered in the course.
-
Find online tutorials for that tool.
-
Follow the tutorials, completing any exercises or examples.
Participate in a data visualization study group
Show steps
Enhance your learning through collaboration by joining a study group, discussing concepts, sharing resources, and providing feedback to peers, improving your understanding and critical thinking skills.
Show steps
-
Find or create a data visualization study group.
-
Attend regular meetings.
-
Participate in discussions and share your insights.
-
Collaborate on projects or assignments.
Create data visualizations for a real-world dataset
Show steps
Apply course concepts to a practical project by creating data visualizations for a dataset of your choice, deepening your understanding and solidifying your skills.
Show steps
-
Choose a dataset that interests you.
-
Clean and prepare the data.
-
Select appropriate data visualization techniques.
-
Create the visualizations using a data visualization tool.
-
Present your findings.
Build a personal data visualization portfolio
Show steps
Showcase your skills and deepen your understanding by creating a portfolio of data visualizations, demonstrating your ability to effectively communicate insights through visualization.
Show steps
-
Collect a variety of data visualization examples.
-
Create a website or online platform to showcase your work.
-
Write descriptions for each visualization, explaining the purpose and methodology.
-
Share your portfolio with potential employers or clients.
Read "Data Visualization: A Practical Introduction"
Show steps
Gain a comprehensive foundation by reading this introductory book, providing a clear and engaging overview of data visualization concepts and techniques.
View
Data Visualization: A Successful Design Process...
on Amazon
Show steps
Show steps
Show steps
- Purchase or borrow a copy of the book.
- Read through the chapters.
- Take notes and highlight important concepts.
Organize and review course materials
Show steps
Build a strong foundation by organizing and reviewing course materials, ensuring you have a clear understanding of the concepts and topics covered.
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for course materials.
- File and organize lecture notes, slides, and assignments.
- Review materials regularly to reinforce your learning.
Review previous knowledge of data visualization
Show steps
Help refresh your knowledge of data visualization concepts, ensuring a solid foundation for the course.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Review your notes from previous data visualization courses.
- Read articles or blog posts on data visualization best practices.
- Complete online quizzes or exercises on data visualization.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete online exercises on data visualization
Show steps
Reinforce your understanding by completing interactive exercises, testing your knowledge and identifying areas for improvement.
Show steps
Show steps
Show steps
- Find online platforms or resources that offer data visualization exercises.
- Complete a variety of exercises, covering different concepts and techniques.
- Review your performance and identify areas where you need more practice.
Follow tutorials on data visualization tools
Show steps
Supplement course content by exploring tutorials on specific data visualization tools, enhancing your practical skills.
Show steps
Show steps
Show steps
- Identify a data visualization tool covered in the course.
- Find online tutorials for that tool.
- Follow the tutorials, completing any exercises or examples.
Participate in a data visualization study group
Show steps
Enhance your learning through collaboration by joining a study group, discussing concepts, sharing resources, and providing feedback to peers, improving your understanding and critical thinking skills.
Show steps
Show steps
Show steps
- Find or create a data visualization study group.
- Attend regular meetings.
- Participate in discussions and share your insights.
- Collaborate on projects or assignments.
Create data visualizations for a real-world dataset
Show steps
Apply course concepts to a practical project by creating data visualizations for a dataset of your choice, deepening your understanding and solidifying your skills.
Show steps
Show steps
Show steps
- Choose a dataset that interests you.
- Clean and prepare the data.
- Select appropriate data visualization techniques.
- Create the visualizations using a data visualization tool.
- Present your findings.
Build a personal data visualization portfolio
Show steps
Showcase your skills and deepen your understanding by creating a portfolio of data visualizations, demonstrating your ability to effectively communicate insights through visualization.
Show steps
Show steps
Show steps
- Collect a variety of data visualization examples.
- Create a website or online platform to showcase your work.
- Write descriptions for each visualization, explaining the purpose and methodology.
- Share your portfolio with potential employers or clients.
Career center
Learners who complete Data Visualization for Developers will develop knowledge and skills
that may be useful to these careers:
Data Visualization Developer
Data Visualization Developer
The Data Visualization Developer creates interactive data visualizations to help users understand complex data. Taking this course will introduce you to the fundamentals of data visualization, including the different types of charts and graphs, the principles of design, and the use of color and typography.
Marketing Analyst
Marketing Analyst
The Marketing Analyst is responsible for collecting, analyzing, and interpreting data to support marketing campaigns. They use data visualization techniques to communicate their findings to stakeholders, so this course can help develop your skills in this area.
Front-End Developer
Front-End Developer
The Front-End Developer is responsible for the design and development of the user interface of a website or application. Data visualization is often prominent in user interfaces, so this course can help deepen your skills in that area.
Business Intelligence Analyst
Business Intelligence Analyst
The Business Intelligence Analyst is responsible for collecting, analyzing, and interpreting data to support decision-making. They use data visualization techniques to communicate their findings to stakeholders, so this course can help develop skills in this area.
Machine Learning Engineer
Machine Learning Engineer
The Machine Learning Engineer is responsible for the design, development, and maintenance of machine learning models. They often use data visualization techniques to debug and profile their models, so this course may be helpful in developing these skills.
Data Engineer
Data Engineer
The Data Engineer is responsible for the design, development, and maintenance of data pipelines. They use data visualization techniques to understand the flow of data through their pipelines, so this course may be helpful in building a foundation in this area.
Data Architect
Data Architect
The Data Architect is responsible for the design and maintenance of the data architecture of an organization. Taking this course would help in deepening your understanding of data visualization and how it fits into the larger data architecture of an organization.
Financial Analyst
Financial Analyst
The Financial Analyst is responsible for evaluating and making recommendations on investments. They use data visualization techniques to analyze financial data and make investment decisions, so this course may be helpful in building a foundation in this area.
Data Scientist
Data Scientist
The Data Scientist is responsible for using data to solve business problems. They have a deep understanding of data analysis and visualization, so this course may be helpful in developing these skills.
User Experience (UX) Designer
User Experience (UX) Designer
The UX Designer is responsible for the design of the user experience of a website or application. They use data visualization techniques to understand how users interact with a product, so this course may be helpful in developing these skills.
Quantitative Analyst (Quant)
Quantitative Analyst (Quant)
The Quantitative Analyst is responsible for using mathematical and statistical models to analyze data and make investment decisions. They often use data visualization techniques to communicate their findings to stakeholders, so this course may be helpful in building a foundation in this area.
Database Administrator (DBA)
Database Administrator (DBA)
The Database Administrator is responsible for the design, development, and maintenance of databases. They use data visualization techniques to understand the performance of their databases, so this course may be helpful in building a foundation in this area.
Software Engineer
Software Engineer
The Software Engineer is responsible for the design, development, and maintenance of software applications. They often use data visualization techniques to debug and profile their code, so this course may be helpful in developing these skills.
Data Science Engineer
Data Science Engineer
The Data Science Engineer is an IT professional who designs, develops, and maintains the infrastructure and tools used for data science. They have a deep understanding of data analysis and visualization, so this course may be helpful in building a foundation in these areas.
Product Manager
Product Manager
The Product Manager is responsible for the development and management of a product. They use data visualization techniques to understand how users interact with a product, so this course may be helpful in building a foundation in this area.
For more career information including salaries, visit:
OpenCourser.com/course/ep5gqr/data
Reading list
We've selected 13 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 Visualization for Developers.
Save
This classic book on data visualization provides a comprehensive overview of the principles of data visualization. It covers topics such as data types, chart types, and design principles. It valuable resource for developers who want to learn more about the theory and practice of data visualization.
Provides a practical guide to creating effective data visualizations. It covers topics such as design principles, color theory, and storytelling. It valuable resource for developers who want to create data visualizations that are both informative and visually appealing.
Save
Provides a comprehensive overview of deep learning. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for developers who want to learn more about deep learning and how to use it to solve real-world problems.
Provides a comprehensive overview of reinforcement learning. It covers topics such as Markov decision processes, value functions, and reinforcement learning algorithms. It valuable resource for developers who want to learn more about reinforcement learning and how to use it to solve real-world problems.
Provides a comprehensive overview of natural language processing with Python. It covers topics such as text preprocessing, natural language understanding, and natural language generation. It valuable resource for developers who want to learn more about natural language processing and how to use it to solve real-world problems.
Provides a comprehensive overview of speech and language processing. It covers topics such as speech recognition, natural language understanding, and speech synthesis. It valuable resource for developers who want to learn more about speech and language processing and how to use it to solve real-world problems.
Provides a comprehensive overview of information theory, inference, and learning algorithms. It covers topics such as probability theory, Bayesian inference, and machine learning. It valuable resource for developers who want to learn more about the theoretical foundations of machine learning and how to use them to solve real-world problems.
Provides a comprehensive overview of data science with Python. It covers topics such as data wrangling, machine learning, and data visualization. It valuable resource for developers who want to learn more about data science and how to use Python for data analysis.
Provides a practical guide to creating data visualizations with Python and JavaScript. It covers topics such as data wrangling, chart types, and interactive visualizations. It valuable resource for developers who want to create data visualizations with code.
Provides a practical guide to using ggplot2 for data visualization. It covers topics such as data wrangling, chart types, and interactive visualizations. It valuable resource for developers who want to create data visualizations with R.
Provides a practical guide to using D3.js for data visualization. It covers topics such as data wrangling, chart types, and interactive visualizations. It valuable resource for developers who want to create data visualizations with JavaScript.
Save
Provides a practical guide to using R for data visualization. It covers topics such as data wrangling, chart types, and interactive visualizations. It valuable resource for developers who want to create data visualizations with R.
Save
Provides a practical guide to using Tableau for data visualization. It covers topics such as data preparation, chart types, and dashboard design. It valuable resource for developers who want to use Tableau to create data visualizations.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ep5gqr/data
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Time
115 hours
Level
Intermediate
Via
Pluralsight
Instructor
Dan Appleman
Language
English
Good to know
Develops data visualization skills, which help developers convey information effectively
Taught by Dan Appleman, who are recognized for their work in data visualization
Explores data visualization concepts, which are standard in software development
Covers visualization architecture, which is essential for designing effective data visualizations
Provides hands-on experience with data visualization, which is crucial for practical implementation
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