Applied Plotting, Charting & Data Representation in Python
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Applied Plotting, Charting & Data Representation in Python
This course is part of Applied Data Science with Python Specialization
Instructor: Christopher Brooks
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6,288 reviews
6,288 reviews
What you'll learn
Describe what makes a good or bad visualization
Understand best practices for creating basic charts
Identify the functions that are best for particular problems
Create a visualization using matplotlb
Skills you'll gain
Tools you'll learn
Details to know
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There are 4 modules in this course
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.
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.
What's included
8 videos6 readings1 peer review1 app item1 discussion prompt
8 videos•Total 38 minutes
- Introduction•4 minutes
- Updates•1 minute
- About the Professor: Christopher Brooks•1 minute
- Tools for Thinking about Design (Alberto Cairo)•9 minutes
- Graphical heuristics: Data-ink ratio (Edward Tufte)•5 minutes
- Graphical heuristics: Chart junk (Edward Tufte)•5 minutes
- Graphical heuristics: Lie Factor and Spark Lines (Edward Tufte)•4 minutes
- The Truthful Art (Alberto Cairo)•9 minutes
6 readings•Total 80 minutes
- Syllabus•10 minutes
- Help us learn more about you!•10 minutes
- Notice for Coursera Learners: Assignment Submission•10 minutes
- Dark Horse Analytics (Optional)•10 minutes
- Useful Junk?: The Effects of Visual Embellishment on Comprehension and Memorability of Charts•30 minutes
- Graphics Lies, Misleading Visuals•10 minutes
1 peer review•Total 60 minutes
- Graphics Lies, Misleading Visuals •60 minutes
1 app item•Total 30 minutes
- Hands-on Visualization Wheel•30 minutes
1 discussion prompt•Total 10 minutes
- Must a visual be enlightening?•10 minutes
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.
What's included
7 videos2 readings1 peer review2 ungraded labs
7 videos•Total 60 minutes
- Introduction•2 minutes
- Matplotlib Architecture•7 minutes
- Basic Plotting with Matplotlib•10 minutes
- Scatterplots•13 minutes
- Line Plots•13 minutes
- Bar Charts•7 minutes
- Dejunkifying a Plot•9 minutes
2 readings•Total 60 minutes
- Matplotlib•30 minutes
- Ten Simple Rules for Better Figures•30 minutes
1 peer review•Total 180 minutes
- Plotting Weather Patterns•180 minutes
2 ungraded labs•Total 120 minutes
- Module 2 Jupyter Notebooks•60 minutes
- Plotting Weather Patterns•60 minutes
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.
What's included
6 videos3 readings2 peer reviews3 ungraded labs
6 videos•Total 65 minutes
- Subplots•15 minutes
- Histograms•13 minutes
- Box Plots•10 minutes
- Heatmaps•8 minutes
- Animation•7 minutes
- Widget Demonstration•11 minutes
3 readings•Total 50 minutes
- Selecting the Number of Bins in a Histogram: A Decision Theoretic Approach (Optional)•10 minutes
- Assignment Reading•30 minutes
- Understanding Error Bars•10 minutes
2 peer reviews•Total 240 minutes
- Building a Custom Visualization •120 minutes
- Practice Assignment: Understanding Distributions Through Sampling•120 minutes
3 ungraded labs•Total 180 minutes
- Module 3 Jupyter Notebooks•60 minutes
- Practice Assignment: Understanding Distributions Through Sampling•60 minutes
- Building a Custom Visualization•60 minutes
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.
What's included
4 videos3 readings1 peer review2 ungraded labs
4 videos•Total 31 minutes
- Plotting with Pandas•8 minutes
- Seaborn•9 minutes
- Mapping and Geographic Investigation•13 minutes
- Becoming an Independent Data Scientist•2 minutes
3 readings•Total 23 minutes
- Spurious Correlations•10 minutes
- Post-course Survey•10 minutes
- 5 reasons to keep going•3 minutes
1 peer review•Total 120 minutes
- Becoming an Independent Data Scientist•120 minutes
2 ungraded labs•Total 120 minutes
- Module 4 Jupyter Notebooks•60 minutes
- Project Description•60 minutes
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Reviewed on Jun 18, 2020
It was a great learning experience as an individual is forced to explore all the official documentations of plotting and charting.The assignments were also very versatile .Loved the course!
Reviewed on Oct 1, 2017
it is a good course to help me have a glance to the data visualization area. However, I think I cannot learned a lot from the course and the homework is so easy that I haven't practice enough.
Reviewed on Jun 26, 2020
its actually a good course as it starts from fundamentals of visualization to the data visualization,the assignments this course provide are exciting and full of knowledge that you learn in course ..
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