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Applied Plotting, Charting & Data Representation in Python

This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will be a tutorial of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data.

This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.

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What's inside

Syllabus

Module 1: Principles of Information Visualization

In this module, you will get an introduction to principles of information visualization. We will be introduced to tools for thinking about design and graphical heuristics for thinking about creating effective visualizations. All of the course information on grading, prerequisites, and expectations are on the course syllabus, which is included in this module.

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Introduces information visualization basics with a focus on reporting & charting using matplotlib

Emphasizes design & information literacy, exploring effective visualization principles

Provides hands-on practice with matplotlib, guiding learners in creating basic charts & realizing design decisions

Covers a range of basic statistical charts, helping learners identify appropriate methods for different problems

Exposes learners to various forms of data structuring & visualization, broadening their understanding

Recommends taking Introduction to Data Science in Python before this course

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Reviews summary

Applied python plotting and data visualization

According to learners, this course is a valuable next step in the Applied Data Science specialization, offering a solid introduction to data visualization using the matplotlib library. Many students highlight the hands-on assignments and the practical final project as the strongest aspects, providing essential skills for creating charts and plots. Reviewers appreciate that the course covers both the mechanics of coding visualizations and the principles of effective data representation. While it provides a strong foundation, some reviewers note that the library versions used in lectures can be slightly outdated, requiring minor adjustments. Overall, it's seen as a useful and practical course for anyone needing to visualize data in Python.

Includes theory on effective data visualization.

"Appreciated the discussion on what makes a good vs. bad visualization."

"The module on design principles was a helpful bonus."

"It's not just about coding, but also thinking about how to present data effectively."

"Some parts felt a bit theoretical, but overall useful context for why we visualize data."

Fits well within the specialization path.

"This course is a perfect follow-up to the Introduction course and essential for the rest of the specialization."

"It builds nicely on the pandas knowledge gained in the first course."

"A necessary stepping stone if you're planning to complete the Applied Data Science certificate."

"Makes sense as the second course in the series, providing visualization tools for data analysis."

Provides a good grasp of matplotlib basics.

"This course gave me a much better understanding of matplotlib's core concepts than I had before."

"It covers the fundamentals needed to start creating various plot types."

"I feel much more comfortable navigating the matplotlib documentation now."

"Explains the basic syntax and different ways to customize plots effectively."

Hands-on projects reinforce learning effectively.

"The assignments were incredibly practical and forced me to really understand how to use matplotlib."

"I especially enjoyed the final project where I had to find my own data and tell a story visually."

"Working through the notebooks and assignments is where the real learning happens."

"The projects were challenging but gave me confidence in my plotting abilities."

Some code or library versions may be outdated.

"Had minor issues getting some code examples to run due to library version differences."

"It would be great if the course could be updated to the latest versions of pandas and matplotlib."

"Some functions used are deprecated in newer versions, requiring a quick web search to fix."

"The core concepts are sound, but be prepared for small compatibility hiccups."

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 Applied Plotting, Charting & Data Representation in Python with these activities:

Create a comprehensive study guide

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Enhance your understanding and retention of course materials by creating a comprehensive study guide.

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Gather all course materials, including slides, notes, and assignments.
Organize the materials into logical sections.
Summarize key concepts and definitions.
Include practice questions and exercises.

Become a mentor for aspiring data visualization enthusiasts

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Reinforce your knowledge and help others by mentoring aspiring data visualization learners.

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Join online forums or platforms where beginners seek guidance.
Offer your help and provide support to those with questions.
Share your knowledge and experiences in data visualization.

Participate in peer review sessions

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Improve your visualization skills by receiving and providing feedback from peers.

Browse courses on Data Visualization

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Find a study partner or group.
Share your visualizations with each other.
Provide constructive feedback on the design, clarity, and impact of the visualizations.
Incorporate feedback into your visualizations.

Four other activities

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Create an interactive data visualization dashboard

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Develop your skills in creating engaging and informative data visualizations by building a dashboard.

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Gather data from various sources and clean the data.
Design the dashboard layout and choose appropriate charts.
Implement the dashboard using matplotlib and Jupyter Notebook.
Test the dashboard's functionality and make necessary adjustments.

Explore advanced charting techniques using Seaborn

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Enhance your data visualization skills by learning and applying advanced techniques with the Seaborn library.

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Install and import the Seaborn library.
Follow tutorials on advanced Seaborn charts such as heatmaps, histograms, and scatterplots.
Create visualizations using Seaborn's built-in datasets or your own data.

Participate in data visualization competitions

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Challenge yourself and showcase your skills by participating in data visualization competitions.

Browse courses on Data Visualization

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Find relevant data visualization competitions.
Download the data and explore the problem statement.
Create compelling visualizations to address the problem statement.
Submit your visualizations and receive feedback from experts.

Build a data visualization portfolio

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Showcase your data visualization skills and enhance your employability by creating a portfolio.

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Collect your best data visualizations from assignments and projects.
Create a website or online gallery to display your portfolio.
Write brief descriptions and provide context for each visualization.

Career center

Learners who complete Applied Plotting, Charting & Data Representation in Python will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist takes in raw data and transforms it into meaningful insights that can help a company understand its customers, make better decisions, and improve its operations. The course will help you develop the skills necessary to become a successful Data Scientist, including data cleaning, data analysis, and data visualization.

See salaries and explore the career path for Data Scientist

Data Visualization Specialist

A Data Visualization Specialist is responsible for creating visualizations that communicate data insights to a wide range of audiences. The course will help you develop the skills necessary to become a successful Data Visualization Specialist, including data analysis, data visualization, and design.

See salaries and explore the career path for Data Visualization Specialist

Data Analyst

A Data Analyst uses data to identify trends, patterns, and insights that can help a company improve its operations. The course will help you develop the skills necessary to become a successful Data Analyst, including data cleaning, data analysis, and data visualization.

See salaries and explore the career path for Data Analyst

Data Engineer

A Data Engineer is responsible for the design, construction, and maintenance of data pipelines. The course will help you develop the skills necessary to become a successful Data Engineer, including data management, data analysis, and data visualization.

See salaries and explore the career path for Data Engineer

Business Analyst

A Business Analyst uses data to identify and solve business problems. The course will help you develop the skills necessary to become a successful Business Analyst, including data gathering, data analysis, and data visualization.

See salaries and explore the career path for Business Analyst

Information Systems Manager

An Information Systems Manager is responsible for the planning, implementation, and management of an organization's information systems. The course will help you develop the skills necessary to become a successful Information Systems Manager, including data management, data analysis, and data visualization.

See salaries and explore the career path for Information Systems Manager

Statistician

A Statistician uses data to make inferences about a population. The course will help you develop the skills necessary to become a successful Statistician, including data analysis, data visualization, and probability.

See salaries and explore the career path for Statistician

Database Administrator

A Database Administrator is responsible for the maintenance and administration of an organization's databases. The course will help you develop the skills necessary to become a successful Database Administrator, including data management, data analysis, and data visualization.

See salaries and explore the career path for Database Administrator

Market Researcher

A Market Researcher uses data to understand the needs and wants of customers. The course will help you develop the skills necessary to become a successful Market Researcher, including data gathering, data analysis, and data visualization.

See salaries and explore the career path for Market Researcher

Operations Research Analyst

An Operations Research Analyst uses data to improve the efficiency of operations. The course will help you develop the skills necessary to become a successful Operations Research Analyst, including data analysis, data visualization, and modeling.

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Financial Analyst

A Financial Analyst uses data to make investment decisions. The course will help you develop the skills necessary to become a successful Financial Analyst, including data analysis, data visualization, and finance.

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Web Developer

A Web Developer designs and develops websites. The course will help you develop the skills necessary to become a successful Web Developer, including HTML, CSS, and JavaScript.

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Software Engineer

A Software Engineer designs, develops, and maintains software applications. The course will help you develop the skills necessary to become a successful Software Engineer, including data structures, algorithms, and software development.

See salaries and explore the career path for Software Engineer

Actuary

An Actuary uses mathematics and statistics to assess risk. The course will help you develop the skills necessary to become a successful Actuary, including data analysis, data visualization, and probability.

See salaries and explore the career path for Actuary

Computer Scientist

A Computer Scientist researches and develops new computer technologies. The course will help you develop the skills necessary to become a successful Computer Scientist, including data structures, algorithms, and computer architecture.

See salaries and explore the career path for Computer Scientist

Reading list

We've selected 25 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 Applied Plotting, Charting & Data Representation in Python.

Reflections on Slope Stability Engineering

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This classic book on data visualization must-read for anyone who wants to learn more about the principles of effective visual communication. Tufte provides a wealth of insights into how to design charts and graphs that are both informative and visually appealing.

Applied Plotting, Charting & Data Representation in Python

What's inside

Syllabus

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Reviews summary

Applied python plotting and data visualization

Activities

Career center

Reading list

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