Introduction to Data Visualization

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來自 IBM 的課程

Data Visualization with Python

298 個評分

IBM

Data Visualization with Python

298 個評分

課程 6（共 9 門，Specialization IBM Data Science Professional Certificate）

"A picture is worth a thousand words". We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the analysis of increasingly large datasets. Data visualization plays an essential role in the representation of both small and large-scale data. One of the key skills of a data scientist is the ability to tell a compelling story, visualizing data and findings in an approachable and stimulating way. Learning how to leverage a software tool to visualize data will also enable you to extract information, better understand the data, and make more effective decisions. The main goal of this Data Visualization with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually but in this course, we will be using several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.

從本節課中

Introduction to Data Visualization Tools

In this module, you will learn about data visualization and some of the best practices to keep in mind when creating plots and visuals. You will also learn about the history and the architecture of Matplotlib and learn about basic plotting with Matplotlib. In addition, you will learn about the dataset on immigration to Canada, which will be used extensively throughout the course. Finally, you will briefly learn how to read csv files into a pandas dataframe and process and manipulate the data in the dataframe, and how to generate line plots using Matplotlib.

Introduction to Data Visualization4:36

Introduction to Matplotlib6:26

Basic Plotting with Matplotlib4:39

Dataset on Immigration to Canada2:43

與講師見面

Alex Aklson
Ph.D., Data Scientist
IBM Developer Skills Network

Hello everyone and welcome to the first module of the data visualization with

Python course. In this video, we're gonna introduce data visualization and go over

an example of transforming a given visual into one which is more effective

attractive and impactive. So let's get started. Now one might ask why would I

need to learn how to visualize data. Well data visualization is a way to show a

complex data in a form that is graphical and easy to understand. This can be

especially useful when one is trying to explore the data and getting acquainted

with it. Also since a picture is worth a thousand words, then plots and graphs can

be very effective in conveying a clear description of the data especially when

disclosing findings to an audience or sharing the data with other peer data

scientists. Also, they can be very valuable when it comes to supporting any

recommendations you make to clients managers or other decision-makers in

your field. Darkhorse Analytics is a company that spun out of a research lab

at the University of Alberta in 2008 and has done fascinating work on data

visualization. Darkhorse Analytics specializes in quantitative consulting

in several areas including data visualization and geo spatial analysis.

Their approach when creating a visual revolves around three key points: less is

more effective, it is more attractive, and it is more impactive. In other words, any

feature or design you incorporate in your plot to make it more attractive or

pleasing should support the message that the plot is meant to get across and not

distract from it. Let's take a look at an example. So here is a pie chart of what

looks like people's preferences when it comes to different types of pig meat. The

charts message is almost half of the people surveyed preferred bacon over the

other types of pig meat. But I'm sure that almost all of you agree that there

is a lot going on in this pie chart and we're not even sure it features such as

the blue background or 3d orientation are meant to convey

anything. In fact, these additional unnecessary features distract from the

main message and can be confusing to the audience. So let's apply Darkhorse

Analytics approach to transform this into a visual that's more effective,

attractive, and impactive. As I mentioned earlier, the message here is that people

are most likely to choose bacon over other types of pig meat, so let's get rid

of everything that can be distracting from this core message. The first thing

is let's get rid of the blue background and the gray background. Let's also get

rid of borders as they do not convey any extra information. Also let's drop the

redundant legend since the pie chart is already color coded. 3D isn't adding any

extra information so let's say bye to it. Text bolding is also unnecessary and

let's get rid of the different colors and the wedges.

But let's thicken the lines to make them more meaningful. Now this looks a

little familiar. Yes! This is a bar graph after all, one with horizontal bars. And

finally, let's emphasize bacon so that it stands out among the other types of pig

meat. Now let's juxtapose the pie chart and the bar graph and compare which is

better and easy to understand. I hope that we unanimously agree that the bar

graph is the better of the two. It is simple, cleaner, less distracting, and much

easier to read. In fact, pie charts have recently come under fire from data

visualization experts who argue that they are relevant only in the rarest of

circumstances. Bar graphs and charts on the other hand are argued to be far

superior ways to quickly get a message across. But don't worry about this for

now, we will come back to this point when we learn how to create pie charts and

bar graphs with Matplotlib. For more similar and interesting

examples, check out Darkhorse Analytics website. They have a couple more examples

on how to clean bar graphs and maps of geospatial data. All these examples

reinforce the concept of less is more effective, attractive, and impactive.

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