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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.

Module 2: Basic Charting

In this module, you will delve into basic charting. For this week’s assignment, you will work with real world CSV weather data. You will manipulate the data to display the minimum and maximum temperature for a range of dates and demonstrate that you know how to create a line graph using matplotlib. Additionally, you will demonstrate the procedure of composite charts, by overlaying a scatter plot of record breaking data for a given year.

Module 3: Charting Fundamentals

In this module you will explore charting fundamentals. For this week’s assignment you will work to implement a new visualization technique based on academic research. This assignment is flexible and you can address it using a variety of difficulties - from an easy static image to an interactive chart where users can set ranges of values to be used.

Module 4: Applied Visualizations

In this module, then everything starts to come together. Your final assignment is entitled “Becoming a Data Scientist.” This assignment requires that you identify at least two publicly accessible datasets from the same region that are consistent across a meaningful dimension. You will state a research question that can be answered using these data sets and then create a visual using matplotlib that addresses your stated research question. You will then be asked to justify how your visual addresses your research question.

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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 plotting, charting & data representation

Learners say that "Applied Plotting, Charting & Data Representation in Python" is an engaging course for beginners who want to learn more about data visualization. The course covers a wide range of topics from the basics of Matplotlib and Seaborn to more advanced charting techniques. The assignments are challenging but rewarding and the peer-reviewed feedback system helps learners improve their work. However, some learners found the lectures to be too short and superficial and felt that they had to do a lot of research on their own to complete the assignments.

The assignments are challenging and time-consuming, but they are also a great way to learn the material. The peer-reviewed feedback system is helpful for improving your work.

"It's normally very time-consuming, but it's also very rewarding. The assignments were fun to do and very beneficial to my learning."

"It could be challenging if you are willing to dig into fancier plotting options. But in the end, I had a super satisfying experience, 5 ⭐s! Looking back at the projects, I was amazed how I accomplished them!."

"This course requires you to look up the material yourself. Although the lectures provide a baseline, they are not at all useful for understanding matplotlib. However I find this useful because I did the research and was able to learn myself."

The instructor is an experienced data scientist and researcher. He is passionate about teaching and makes the material easy to understand.

"Dr Brooks, himself an eminent researcher in Data Science himself, has made a well-laid out plan towards the data science learning track."

"The theory behind each application is also explained appropriately for better understanding of each topic."

The assignments are practical andhands-on. They force you to apply the concepts you learn in the lectures.

"The assignments are really challenging and force you to work hard."

"The final assignment give opportunity to do a little independent data gathering / wrangling / visualisation mini-project."

"It’s up to you."

The course is well-structured and organized. The lectures are short and to the point, and the assignments are challenging but manageable.

"The course is well structured and high quality lectures."

"The course is well structured. I went through online search to understand all details mentioned in video, moreover I gained knowledge by looking at the assignment and solving myself with the help of stack overflow."

"The course is extraordinary. The videos, quizzes and assignments are pretty fun to dive into and, this is the first course in which I see Peer Assignment (because it makes sense) and it was great to learn with other people from all around the world!"

You will need to do a lot of research on your own to complete the assignments. This can be frustrating at times, but it is also a good way to learn.

"The course teaches you about important plotting and design skills but the peer reviews and lack of clarity for the assignments makes this class less useful than it could be."

"I thought the video lecture did not go into enough depth on how to manipulate matplotlib (eg. ax objects and ticks markers are still not clear) and even in general, the explanations were lacking."

"It would be helpful if the course is modified in a similar style to Python courses."

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.

Browse courses on Data Visualization

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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.

Browse courses on Data Visualization

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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.

Browse courses on Data Visualization

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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 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 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

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

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

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

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.

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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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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

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

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.

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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.

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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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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.

See salaries and explore the career path for Financial Analyst

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

Good to know

Save this course

Reviews summary

Applied plotting, charting & data representation

Activities

Career center

Reading list

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