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来自 University of Michigan 的课程

Applied Plotting, Charting & Data Representation in Python

2251 个评分

University of Michigan

Applied Plotting, Charting & Data Representation in Python

2251 个评分

课程 2（共 5 门，Specialization Applied Data Science with 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.

从本节课中

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.

Introduction4:29

About the Professor: Christopher Brooks1:25

Tools for Thinking about Design (Alberto Cairo)8:36

Graphical heuristics: Data-ink ratio (Edward Tufte)4:44

Graphical heuristics: Chart junk (Edward Tufte)5:11

Graphical heuristics: Lie Factor and Spark Lines (Edward Tufte)3:47

The Truthful Art (Alberto Cairo)8:40

与讲师见面

Christopher Brooks

Welcome to the second course in the Introduction to Applied Data Science with

Python specialization.

In the first course we looked, primarily, at how to manipulate data,

including loading data from structured delimited files,

such as CSVs, cleaning the data with Python pandas, and NumPy libraries, and

transforming data using functions, such as apply, aggregate, map, and pivot.

If you don't feel you have a solid understanding of the pandas toolkit,

then I would invite you to go back and review that course content.

The main topic of this course is understanding how to communicate data more

clearly to the various stakeholders who are interested in the data.

And there are many different stakeholders to whom you might be

interested in using visuals to reach.

For instance, you want to share unbiased, representations of data with

peer data scientists who are able to make their own inferences and interpretations.

But you'll also want to be able to share stories about the data with managers and

executives who might be familiar with domain but are looking for

specific actionable insights.

Finally, you'll also want to be able to communicate like a journalist to a broad

population who knows very little about your particular data and

needs to understand not only an overview of the phenomenon you are describing but

also the limitations of the approaches you have used.

This course is about specific techniques which are part of a broad subject of

information visualization.

Referred to an infovis for short,

this is a field that has rapidly developed over the past 20 years, as computing power

has made it possible to build compelling visual and interactive graphics quickly.

And while the use of visualizations to impart knowledge of data has been around

for centuries, the last 20 years have seen the development of important theoretical

basis for best practices in displaying information to various audiences.

And in this course, we're going to focus on those.

In this first week of the course, we're going to touch on some of those theories

and best practices, and talk about fundamental thinkers in the field.

In particular, Alberto Cairo and Edward Tufte.

We'll go through some examples of excellent and not so

excellent visualizations.

And develop a language for

speaking about how we might make meaningful visualizations ourselves.

This portion of the course will be largely lecture based with several readings and

peer reviewed assignment at the end of each week.

In the second and third week of the course, we'll dive into the de facto

Python library for creating charts and graphs, matplotlib.

Now matplotlib isn't the only way to create visual artifacts in Python, but

it's the basis for some emerging new web tools such as C-Born and Bokeh.

And acts as a solid foundation for our discussions.

The majority of these weeks will be spent actually working on assignments with

a few lectures to share an overview of how the toolkit itself works.

And you'll be expected to take messy raw data, process it using pandas or

other suitable techniques and render it into a meaningful, explanatory image.

And again, if you're not comfortable yet using pandas, I would encourage you to go

back to the first course in this specialization and review those videos.

The assignments for weeks two and three will be largely peer graded.

Finally, the course will culminate with a small project,

similar to the first course.

In this project, you will be given a new visualization technique and

you will be asked to extend the matplotlib code base to enhance its functionality.

This is a critical step in your journey as a data scientist.

To go beyond using tools off the shelf and instead be able to modify

them to support new ways of understanding data and problems.

This course is very similar to the first,

except the evaluation is largely peer review.

The lectures will only guide you so much in completing the assignments.

You'll need to know how to ask questions in the discussion forums of your peers.

And seek out new information through web searches and Stack Overflow.

And again, Stack Overflow is tagged specifically for

questions about matplotlib.

So I'd encourage you to use that resource as well.

Finally, for this course,

I expect that you are familiar with all of the content from Course 1.

I'm looking forward to this next course in specialization.

Let's get started.

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