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.
Ce cours fait partie de la Spécialisation Applied Data Science with Python
Applied Plotting, Charting & Data Representation in Python
proposé par
À propos de ce cours
4.5
2,385 notes
Ce que vous allez apprendre
Create a visualization using matplotlb
Describe what makes a good or bad visualization
Identify the functions that are best for particular problems
Understand best practices for creating basic charts
Compétences que vous acquerrez
Python ProgrammingData VirtualizationData Visualization (DataViz)Matplotlib
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
1Module 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.
7 vidéos (Total 37 min), 6 lectures, 2 quiz
7 vidéos
Introduction4 min
About the Professor: Christopher Brooks1 min
Tools for Thinking about Design (Alberto Cairo)8 min
Graphical heuristics: Data-ink ratio (Edward Tufte)4 min
Graphical heuristics: Chart junk (Edward Tufte)5 min
Graphical heuristics: Lie Factor and Spark Lines (Edward Tufte)3 min
The Truthful Art (Alberto Cairo)8 min
6 lectures
Syllabus10 min
Help us learn more about you!10 min
Notice for Coursera Learners: Assignment Submission10 min
Dark Horse Analytics (Optional)10 min
Useful Junk?: The Effects of Visual Embellishment on Comprehension and Memorability of Charts30 min
Graphics Lies, Misleading Visuals10 min
Semaine
2Module 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.
7 vidéos (Total 42 min), 2 lectures, 1 quiz
7 vidéos
Introduction1 min
Matplotlib Architecture6 min
Basic Plotting with Matplotlib7 min
Scatterplots8 min
Line Plots8 min
Bar Charts4 min
Dejunkifying a Plot3 min
2 lectures
Matplotlib30 min
Ten Simple Rules for Better Figures30 min
Semaine
3Module 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.
6 vidéos (Total 39 min), 2 lectures, 2 quiz
2 lectures
Selecting the Number of Bins in a Histogram: A Decision Theoretic Approach (Optional)10 min
Assignment Reading10 min
Semaine
4Module 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.
3 vidéos (Total 18 min), 2 lectures, 1 quiz
2 lectures
Spurious Correlations10 min
Post-course Survey10 min
4.5
415 avis35%
a commencé une nouvelle carrière après avoir terminé ces cours
39%
a bénéficié d'un avantage concret dans sa carrière grâce à ce cours
16%
a obtenu une augmentation de salaire ou une promotion
Meilleurs avis
par SB•Nov 3rd 2017
Loved the course! This course teaches you details about matplotlib and enables you to produce beautiful and accurate graphs.. Assignments are challanging, and helps to build a solid foundation.
par ML•Jun 28th 2017
Good course to learned matplotlib and other Graphs libraries, but the course goes further than Python and also encourages the studies to create more meaningful and beautiful Graphic views.
À propos de Université du Michigan
The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
À propos de la Spécialisation Applied Data Science with Python
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.
Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.
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