c# 数据可视化

by Siena Duplan

通过锡耶纳·杜普兰(Siena Duplan)

#OpenVisConf上的22位数据可视化从业者的10点收获 (10 Takeaways from 22 Data Visualization Practitioners at #OpenVisConf)

Update | May 13, 2016: Videos of all talks are officially live!

更新| 2016年5月13日： 所有讲座的视频正式上线！

1.“数据可视化不是您的创意来源； 数据可视化使数据易于理解。” (1. “Data visualization is not your creative outlet; data visualization is making data understandable.”)

rachel binx from NASA’s Jet Propulsion Laboratory eloquently described every data viz practitioner’s struggle: compromising between art and utility.

来自NASA喷气推进实验室的rachel binx雄辩地描述了每个数据，即从业者的努力：在艺术和实用性之间妥协。

Visualization literacy and data literacy take time. In fact, Tony Chu taught us how data visualization is the use of space to control time (spacetime pun?).

可视化素养和数据素养需要时间。 实际上， Tony Chu教我们如何利用空间来控制时间 (时空双关语)来实现数据可视化。

Intuitive data exposition is constrained by attention — what Daniel Kahneman, author of Thinking, Fast and Slow, would call our System 2 thinking.

什么丹尼尔·卡尼曼，一书的作者-直观的数据博览会是由注意力限制思维，快速和慢速 ，会叫我们的系统思路2。

Tony advocates using techniques like animation and pacing (think scrollytelling) to feed bite-size bits of information (pun intended) to those digesting our visualizations, while Rachel focuses on using data viz to render tools instead of to tell stories or paint a picture.

托尼(Tony)提倡使用动画和步调(想想滚动显示 )之类的技术来将一口大小的信息(双关语意味 )提供给那些消化我们的可视化效果的人，而雷切尔(Rachel)则专注于使用数据可视化来渲染工具，而不是讲述故事或描绘图片。

2.然而，在寻找可视化复杂数据问题的解决方案时需要投入大量的创造力。 (2. Yet, so much creativity is involved in finding solutions to visualizing complex data problems.)

Every speaker probed the realms of creativity in their mesmerizing visuals and approaches to technical rigor. Mariko Kosaka transformed polynomial functions into “pixel playgrounds” and brought kernel convolution to life with a “pixel social graph.”

每个演讲者都以令人着迷的视觉效果和严谨的技术方法探讨了创造力的领域。 小坂麻里子(Mariko Kosaka)将多项式函数转换为“像素运动场”，并通过“像素社交图”将内核卷积变为现实。

Shirley Wu abused the force (d3’s force-directed graph layout) in a number of ways to simplify Illumio’s Illumination product (Shirley’s slides distill this complex idea into beautiful, understandable simplicity).

Shirley Wu以多种方式 滥用了该力 (d3的力导向图形布局)来简化Illumio的Illumination产品( Shirley的幻灯片将这种复杂的想法提炼为美观，易于理解的简单方式)。

Amelia McNamara sneakily taught us some statistics — particularly, a creative solution to distinguishing real patterns in data from randomly generated data.

阿米莉亚·麦克纳马拉(Amelia McNamara)偷偷地教了我们一些统计信息-特别是一种区分随机数据和真实数据的创造性解决方案。

Nicky Case cleverly applied systems thinking to real-world phenomena like disease proliferation, ecosystem regeneration, and racial segregation. Nicky employed emojis in simulations to show that the world is not linear; it’s loopy. (Side note: emojis made a record-breaking number of appearances in this year’s talks.)

尼基·凯斯(Nicky Case)巧妙地将系统思维应用于现实世界中的现象，例如疾病扩散，生态系统再生和种族隔离。 尼基在仿真中使用表情符号来表明世界不是线性的。 循环。 (旁注：表情符号在今年的会谈中出现了创纪录的出现次数。)

And Nadieh Bremer meticulously decoded SVG properties like gradient, animation, the glow filter, motion blur, the gooey filter, and color blending. These eye candy features spruce up even the most basic data visualizations.

纳迪埃·布雷默(Nadieh Bremer)精心解码了SVG属性，例如渐变，动画，辉光滤镜，运动模糊，胶粘滤镜和色彩混合。 这些令人眼花candy乱的功能甚至可以简化最基本的数据可视化。

3.数据可视化的未来是虚拟的，物理的和声音的。 (3. The future of data visualization is virtual, physical, and sonic.)

“Data visualization” is quickly becoming an understatement for the field. The OpenVis Conference exploited much more than just our visual cortexes. Our ears sampled the building blocks of music in Kyle McDonald’s talk and we experienced a physics lesson in Ana Asnes Becker’s 3D ride along the Nasdaq.

“数据可视化”正Swift成为该领域的轻描淡写。 OpenVis会议不仅开发了视觉皮层，还开发了许多其他功能。 在凯尔·麦克唐纳 ( Kyle McDonald )的演讲中，我们的耳朵采样了音乐的构成要素，而我们在Ana Asnes Becker 沿纳斯达克(Nasdaq)进行的3D 骑行中体验了物理课。

Virtual reality is building a new playground for data visualization — side effects may include nausea. Patricio Gonzalez Vivo showcased the future of 2D and 3D mapmaking using shaders (check out Mapzen’s Tangram).

虚拟现实正在建立一个新的游乐场以进行数据可视化-副作用可能包括恶心。 Patricio Gonzalez Vivo使用着色器展示了2D和3D地图制作的未来(请查看Mapzen的Tangram )。

We also witnessed robots produce physical data visualizations and data art during breaks at the conference.

我们还目睹了机器人在会议休息期间产生的物理数据可视化和数据艺术。

4.特征工程是主要的 ？ (4. Feature engineering is major ?.)

At the foundation of every well-fit model, successful predictive algorithm, and effective data visualization is hours of feature engineering and data scrubbing.

在每个良好拟合模型的基础上，成功的预测算法以及有效的数据可视化都是数小时的特征工程和数据清理工作。

Oftentimes, the visualization is just the tip of the iceberg. Sometimes, a single line of code is just the tip of the iceberg (*cough, cough* rachel binx)! Data visualizers are all under-the-hood engineers as well as designers and artists.

通常，可视化只是冰山一角。 有时候，单行代码只是冰山一角(*咳嗽，咳嗽* 瑞秋宾克斯 )！ 数据可视化工具都是高级工程师以及设计师和艺术家。

Zan Armstrong taught us how to explore time series data to select the appropriate seasonality before modeling. Fernanda Viégas and Martin Wattenberg heavily reinforced the importance of wise feature selection and engineering prior to building a successful machine learning algorithm.

Zan Armstrong教我们如何在建模之前探索时间序列数据以选择适当的季节。 在构建成功的机器学习算法之前，FernandaViégas和Martin Wattenberg充分强调了明智的特征选择和工程设计的重要性。

5.神经网络是2016年最性感的算法，它们既是数学又是魔术 ✨。 (5. Neural networks are 2016’s sexiest algorithm and they are part math, part magic ✨.)

Fernanda Viégas and Martin Wattenberg from Google’s data visualization group demystified neural nets by demo-ing their recently released TensorFlow playground.

Google数据可视化小组的FernandaViégas和Martin Wattenberg通过演示最近发布的TensorFlow游乐场对神经网络进行了神秘化处理。

With the right amount of hidden layers, nodes, and training data, a neural net can classify just about anything… well, except for images of mugs (again, feature engineering is key).

有了适当数量的隐藏层，节点和训练数据，神经网络几乎可以对任何事物进行分类……除了杯子的图像(同样，特征工程是关键)。

6.文本分析将超越词云。 (6. Text analysis is about to blow up beyond word clouds.)

Christopher Collins led us through a text analysis and visualization spree. In the limited time he had, Christopher whisked us through parallel tag clouds of our country’s legal cases, sunburst diagrams of barbie’s lexicon, and how anomalous information spreads across the web. Christopher’s techniques are novel and mark the launch of a new era of text analysis.

克里斯托弗·柯林斯带领我们通过文本分析和可视化大礼包 。 在有限的时间内，克里斯托弗(Christopher)通过我们国家法律案件的平行标签云，芭比娃娃词典的朝阳图以及异常信息如何在网络上传播，使我们着迷。 克里斯托弗的技术新颖，标志着文本分析新时代的开始。

7. 自动化是不可避免的 。 (7. Automation is inevitable.)

One seventh of the 21 talks focused on components of automation in data visualization. Adam Pearce’s tiny tools developed at Bloomberg Graphics drastically reduce the time spent in iterative tasks like writing redundant lines of code.

在21个演讲中，有七分之一集中于数据可视化中的自动化组件。 由Bloomberg Graphics开发的Adam Pearce的微型工具极大地减少了在重复任务(如编写冗余代码行)中花费的时间。

David Yanofsky’s charting platform uses d3.js and react.js to create brutally simple point-and-click dashboards and charts. Arvind Satyanarayan demo’ed Vega, the reactive programming tool that allows users to declaratively define interactivity and describe features of a visualization directly in a JSON.

David Yanofsky的制图平台使用d3.js和react.js来创建残酷简单的点击式仪表盘和图表。 Arvind Satyanarayan演示了Vega，这是一种React式编程工具，使用户可以声明性地定义交互性并直接在JSON中描述可视化的功能。

8.数据可视化不遗余力 。 (8. Data visualization leaves no stone left unturned.)

By stone, I mean field of study. We saw disruptive data visualization techniques in a breadth of fields from wine and song to outer space and tennis. We, as data visualization experts, get to play in everyone’s backyard.

石头是指研究领域。 我们在葡萄酒，歌曲，外太空和网球等广泛领域中看到了破坏性的数据可视化技术。 作为数据可视化专家，我们可以在每个人的后院发挥作用。

Kim Albrecht deconstructed the relationship between performance and popularity in professional tennis. Kim explained the intersection between data science and data visualization and how visualization unites scientists and sparks public dialogues.

金·阿尔布雷希特(Kim Albrecht)解构了职业网球的表现与知名度之间的关系。 Kim解释了数据科学与数据可视化之间的交叉点，以及可视化如何团结科学家并引发公众对话。

Christine Waigl launched us into orbit around the planet and probed into regions of the world affected by climate change and natural disaster. We voyaged through her visualization trajectory from retrieving satellite data to generating quality raster images.

克里斯汀·威格(Christine Waigl)将我们带入环绕地球的轨道，并探究了受气候变化和自然灾害影响的世界各个地区。 我们浏览了她的可视化轨迹，从检索卫星数据到生成高质量的栅格图像。

9.良好的数据可视化要遵循心理学，神经科学和哲学的原则，这些原则涉及我们的视觉皮层如何与大脑的其余部分进行通信，以及如何从道德上传达不确定性。 (9. Good data visualization nods to principles from psychology, neuroscience, and philosophy about how our visual cortex communicates with the rest of our brain and about how we ethically communicate uncertainty.)

kennedy elliott showed us how pre-attentive processing of image attributes like color, shape, angles, position, curvature, volume, shading, area, length, and direction deeply impacts how humans interpret graphics.

肯尼迪·埃利奥特(Kennedy elliott)向我们展示了图像属性(如颜色，形状，角度，位置，曲率，体积，阴影，面积，长度和方向)的预先注意处理如何深刻影响人类如何解释图形。

As designers and data experts, our job requires us to be aware of the inherent biases in graphics to produce accurate and useful tools.

作为设计者和数据专家，我们的工作要求我们意识到图形中固有的偏差，以生成准确而有用的工具。

Mona Chalabi reminded us to humanize our data and to be receptive to channels of critique like comment sections. Mona would agree that the medium is both the message and the messenger.

莫娜·查拉比(Mona Chalabi)提醒我们要人性化数据，并接受评论部分等批评渠道。 蒙娜娜(Mona)同意媒介既是信息也是信使 。

In real life, we learn to navigate uncertainty from repeated experiences. Jessica Hullman used hypothetical outcome plots to place us in the midst of uncertainty. According to Jessica, “HOPs enable a user to experience uncertainty in terms of countable events, just like we experience probability in our day to day lives.”

在现实生活中，我们学会从反复的经历中克服不确定性。 杰西卡·霍尔曼(Jessica Hullman)使用假设的结果图将我们置于不确定性之中。 根据Jessica的说法，“ HOP使用户能够体验到可数事件带来的不确定性，就像我们在日常生活中体验到概率一样。”

10.数据书呆子是我的人民！ (10. Data nerds are my people!)

The handsdown most valuable part of the conference was meeting the wildly accomplished and equally curious data visualization and data science explorers.

会议最有价值的部分是会见了表现出色且同样好奇的数据可视化和数据科学探索者。

I am so grateful to Irene Ros, the rest of the team at Bocoup, and the sponsors for putting together an outstandingly organized conference. Post-conference depression is a real thing, I learned. Thank you for the knowledge, friends, skills, and ideas. Also, huge thank you to Salesforce for sponsoring me and my awesome manager Ernest for supporting my attendance!

我非常感谢Irco Ros ，Bocoup团队的其他成员以及主办方组织的出色会议。 我了解到，会后抑郁是一件实事。 感谢您的知识，朋友，技能和想法。 另外，非常感谢Salesforce赞助我，以及我出色的经理Ernest支持我的出勤！

One of my favorite stories from the conference happened at the party. I was standing near Mariko who prefaced her talk by sharing how she married her love for knitting sweaters with her love for coding. A data viz engineer at Apple turned to me and asked if I was a knitter myself. Being the R queen that I am, I assumed he was wondering if I was a knitr user — as in R’s report-generating package ‘knitr’ — to which I responded positively, “Yes! I am a big knitr slash sweave fan.”

会议上我最喜欢的故事之一发生在聚会上。 我正站在丸子(Mariko )的旁边，她通过分享她如何将自己对针织毛衣的热爱与对编码的热爱相结合 ，开始了她的演讲。 苹果公司的一位数据可视化工程师转向我，问我自己是否是编织者。 作为我的R女王，我以为他想知道我是否是氮肥使用者，就像R的报告生成包“ knitr”一样，对此我做出了积极的回答：“是的！ 我是编织斜线编织的狂热粉丝。”

At any other party, I probably would have received a strange look and spent the rest of the night wondering why no one would talk to me. But a data nerd party is different. He understood me immediately, laughed at my misunderstanding, and followed up with more questions about knitr ?.

在任何其他聚会上，我可能都会有一个陌生的表情，并花了整整一整夜想知道为什么没人会跟我说话。 但是数据书呆子却不同。 他立即理解了我，嘲笑我的误解，并随后提出了更多有关knitr？的问题。

翻译自: https://www.freecodecamp.org/news/10-takeaways-from-22-data-visualization-practitioners-at-openvisconf-a4a3a5b96fcd/

c# 数据可视化