À propos de ce cours : 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.

Les destinataires de ce cours : This course is part of “Applied Data Science with Python“ and is intended for learners who have basic python or programming background, and want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data and/or a tutorial of the matplotlib system. Only minimal statistics background is expected, and the first course contains a refresh of these basic concepts. There are no geographic restrictions. Learners with a formal training in Computer Science but without formal training in data science will still find the skills they acquire in these courses valuable in their studies and careers.

Créé par : University of Michigan

Enseigné par : Christopher Brooks

Informations de base	Cours 2 de 5 dans Applied Data Science with Python Specialization
Niveau	Intermediate
Langue	English, Sous-titres : Korean
Comment réussir	Réussissez tous les devoirs notés pour terminer le cours.
Notes des utilisateurs	4.4 étoiles Note moyenne des utilisateurs 4.4Voir ce que disent les étudiants

Programme de cours

SEMAINE 1

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.

7 vidéos, 6 lectures

Материал для самостоятельного изучения: Syllabus
Материал для самостоятельного изучения: Help us learn more about you!
Vidéo: About the Professor: Christopher Brooks
Vidéo: Tools for Thinking about Design (Alberto Cairo)
Материал для самостоятельного изучения: Notice for Coursera Learners: Assignment Submission
Элемент LTI: Hands-on Visualization Wheel
Vidéo: Graphical heuristics: Data-ink ratio (Edward Tufte)
Материал для самостоятельного изучения: Dark Horse Analytics (Optional)
Vidéo: Graphical heuristics: Chart junk (Edward Tufte)
Материал для самостоятельного изучения: Useful Junk?: The Effects of Visual Embellishment on Comprehension and Memorability of Charts
Vidéo: Graphical heuristics: Lie Factor and Spark Lines (Edward Tufte)
Vidéo: The Truthful Art (Alberto Cairo)
Вопрос для обсуждения: Must a visual be enlightening?
Материал для самостоятельного изучения: Graphics Lies, Misleading Visuals

Noté: Graphics Lies, Misleading Visuals

SEMAINE 2

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.

7 vidéos, 2 lectures

Блокнот: Module 2 Jupyter Notebook
Vidéo: Matplotlib Architecture
Материал для самостоятельного изучения: Matplotlib
Материал для самостоятельного изучения: Ten Simple Rules for Better Figures
Vidéo: Basic Plotting with Matplotlib
Vidéo: Scatterplots
Vidéo: Line Plots
Vidéo: Bar Charts
Vidéo: Dejunkifying a Plot
Блокнот: Plotting Weather Patterns

Noté: Plotting Weather Patterns

SEMAINE 3

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.

6 vidéos, 2 lectures

Блокнот: Module 3 Jupyter Notebook
Vidéo: Histograms
Материал для самостоятельного изучения: Selecting the Number of Bins in a Histogram: A Decision Theoretic Approach (Optional)
Vidéo: Box Plots
Vidéo: Heatmaps
Vidéo: Animation
Vidéo: Interactivity
Блокнот: Practice Assignment: Understanding Distributions Through Sampling
Взаимная оценка: Practice Assignment: Understanding Distributions Through Sampling
Блокнот: Building a Custom Visualization
Материал для самостоятельного изучения: Assignment Reading

Noté: Building a Custom Visualization

SEMAINE 4

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.

3 vidéos, 2 lectures

Блокнот: Module 4 Jupyter Notebook
Vidéo: Seaborn
Материал для самостоятельного изучения: Spurious Correlations
Vidéo: Becoming an Independent Data Scientist
Блокнот: Project Description
Материал для самостоятельного изучения: Post-course Survey

Noté: Becoming an Independent Data Scientist

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University of Michigan

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Notation et examens

Note moyenne 4.4 sur 5 sur 1,503 notes

very nice

Awesome Course!

After listening to this lecture, I think I learned how to use Python to express the analyzed data appropriately to people other than me. I wish you all the best for your continued development.