Best Practices for Creating Useful Dashboards – The Data School by Chartio
An optimal dashboard is Accurate, Clear, Empowering, and Succinct. These key tenets can be remembered with the acronym ACES. Accurate A dashboard lives and dies by the trust the viewers have in what they are seeing. If the viewers doubt a dashboard’s accuracy, it will not be used to make decisions. Working with an inaccurate dashboard can cause people to be more hesitant to trust any dashboard the
How to visualize decision trees
1 How to visualize decision trees Terence Parr and Prince Grover (Terence is a tech lead at Google and ex-Professor of computer/data science in University of San Francisco's MS in Data Science program and Prince is an alumnus. You might know Terence as the creator of the ANTLR parser generator.) Please send comments, suggestions, or fixes to Terence. Update July 2020 Tudor Lapusan has become a maj
Model Tuning and the Bias-Variance Tradeoff | A VISUAL INTRODUCTION TO MACHINE LEARNING PART II
A visual introduction to machine learning—Part II Model Tuning and the Bias-Variance Tradeoff The goal of modeling is to approximate real-life situations by identifying and encoding patterns in data. Models make mistakes if those patterns are overly simple or overly complex. In Part 1, we created a model that distinguishes homes in San Francisco from those in New York. Now, we'll talk about tuning
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ＴＰＰ　巨大経済åœã®å®ŸåŠ› 環太平洋経済連æºå”定（ＴＰＰ）ã®æ‰¿èªæ¡ˆã¨é–¢é€£æ³•æ¡ˆã®å›½ä¼šå¯©è°ãŒå§‹ã¾ã£ãŸã€‚ï¼´ï¼°ï¼°ãŒç™ºåŠ¹ã™ã‚Œã°ä¸–ç•Œã®å›½å†…ç·ç”Ÿç”£ï¼ˆï¼§ï¼¤ï¼°ï¼‰ã®ï¼”割をå ã‚る巨大経済åœãŒå®Ÿç¾ã™ã‚‹ã€‚ã—ã‹ã—ã€ç±³å›½ã§ã¯å¤§çµ±é ˜é¸ã§äº‰ã†å…±å’Œå…šã®ãƒˆãƒ©ãƒ³ãƒ—æ°ã¨æ°‘主党ã®ã‚¯ãƒªãƒ³ãƒˆãƒ³æ°ã®ä¸¡å€™è£œãŒãã‚ã£ã¦æ‰¹å‡†å対を表明ã—ã¦ãŠã‚Šã€æ—©æœŸç™ºåŠ¹ã«ã¯æš—雲ãŒåž‚ã‚Œã“ã‚ã¦ã„る。 ï¼´ï¼°ï¼°å‚åŠ å›½ã®ï¼§ï¼¤ï¼°ã¯ä¸–ç•Œã®ï¼”割をå ã‚ã‚‹ 日本や米国ãªã©12カ国ãŒå‚åŠ ã™ã‚‹ï¼´ï¼°ï¼°ã¯ã€2015å¹´10月ã«å¤§ç‹åˆæ„ã‚’ã¨ã‚Šã¤ã‘ã€16年２月ã«å„国ãŒå”定ã«ç½²åã—ãŸã€‚ç½²åã‹ã‚‰ï¼’年以内ã«12カ国ã™ã¹ã¦ã§å›½å†…手続ããŒå®Œäº†ã™ã‚‹ã¨ã€å”定ãŒç™ºåŠ¹ã™ã‚‹ã€‚２年以内ã«æ‰‹ç¶šããŒçµ‚ã‚らãªã„å ´åˆã€12カ国ã®ã†ã¡ã€ï¼§ï¼¤ï¼°ã®85％以上をå ã‚る６カ国以上ãŒæ‰‹ç¶šãを終ãˆã‚‹å¿…è¦ãŒã‚る。ãã®ãŸã‚ＧＤＰã®60％超をå ã‚る米国ã®æ‰¹å‡†ãŒå¿…é ˆã 。次ã®å¤§çµ±é ˜å€™è£œãŒãã‚ã£ã¦å対姿勢を示ã™ãªã‹ã€ã‚ªãƒãƒžå¤§çµ±é ˜åœ¨ä»»ä¸ã®ç±³å›½ã®æ‰¹å‡†ãŒç„¦ç‚¹
Utilization Heat Maps
Device utilization is a key metric for performance analysis and capacity planning. On this page I'll illustrate different ways to visualize device utilization across multiple devices, and how that utilization is changing over time. As a system to study, I'll examine a production cloud environment that contains over 5,000 virtual CPUs (over 600 physical processors). I'll show how well different vis
Postgres Query Plan Visualization - tatiyants.com
After a recent stint in query optimization, I once again found myself wanting a better way to view query plans produced by EXPLAIN. So, I finally decided to do something about it and the result is Postgres EXPLAIN Visualizer (or Pev): Why Pev I wanted a tool that can make plans simple to understand and be visually pleasing. More specifically, I wanted: minimal visual noise insights high degree of
Pricing | Mention
Monitor Track any topic on social media and the web and gain relevant insights instantly. 1 billion sources Real time monitoring Data up to 2 years Analyze Get the big picture on any topic, measure your impact with social listening metrics. Ready-to-use templates Sentiment analysis Share of voice Engage Interact with your community on social media and improve your reach and engagement. AI tools Pu
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見ã¦ã„ã¦æ¥½ã—ã„æ„Ÿã˜ã ã£ãŸã®ã§ã”紹介。マニアックã ãŒã€‚ Algomationã¯ã•ã¾ã–ã¾ãªã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ をアニメーションã§è§£èª¬ã—ã¦ãれるサイトã 。 よãã‚るソート系ã®ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã‚„ã€ã‚ªã‚»ãƒã‚’é¡Œæã«ã—ãŸäººå·¥çŸ¥èƒ½ã®ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã¾ã§ç¶²ç¾…ã—ã¦ã„る。 ã¾ãŸè‡ªåˆ†ã§ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ を作ã£ã¦æŠ•ç¨¿ã™ã‚‹ã“ã¨ã‚‚å¯èƒ½ã 。ã“れ系ã®æ€è€ƒå®Ÿé¨“ãŒå¥½ããªäººã¯è¦—ã„ã¦ã¿ã‚‹ã¨ã„ã„ã§ã™ã。
JVM! JVM! JVM!
10. jmc Java Mission Control ◠メモリ統計ã€ã‚¹ãƒ¬ãƒƒãƒ‰æ•°ã€ã‚¯ãƒ©ã‚¹æ•°ã‚’グラフィカルã«è¡¨ 示 â— jconsoleã¨ä¼¼ãŸã‚ˆã†ãªæ„Ÿã˜ã ã‘ã©jmcã®æ–¹ãŒãªã†ã„？ ◠ダッシュボードã®ã‚«ã‚¹ã‚¿ãƒžã‚¤ã‚ºï¼ˆã‚°ãƒ©ãƒ•ã®è¿½åŠ ）ãŒå¯èƒ½ â— Flight Recorderã¨ã„ã†ãƒ—ãƒãƒ•ã‚¡ã‚¤ãƒªãƒ³ã‚°ãƒ„ールãŒã‚ る。ãŒå•†ç”¨ãƒ©ã‚¤ã‚»ãƒ³ã‚¹ãŒå¿…è¦ï¼ˆã‚‰ã—ã„ â— -XX:+UnlockCommercialFeatures -XX:+FlightRecorder â— Eclipseプラグインã¨ã—ã¦ã‚‚利用ã§ãる（らã—ã„
Tableau: Business Intelligence and Analytics Software
Why Tableau Toggle sub-navigation What Is Tableau Build a Data Culture Tableau Economy The Tableau Community The Salesforce Advantage Our Customers About Tableau Toggle sub-navigation Mission Tableau Research Awards and Recognition Tableau Foundation Equality at Tableau Products Toggle sub-navigation Tableau Artificial Intelligence Toggle sub-navigation Tableau Pulse Tableau Agent Embedded Analyti
Explain Git with D3
We are going to skip instructing you on how to add your files for commit in this explanation. Let's assume you already know how to do that. If you don't, go read some other tutorials. Pretend that you already have your files staged for commit and enter git commit as many times as you like in the terminal box. git branch name will create a new branch named "name". Creating branches just creates a n
ã“ã® Visualization ãŒã™ã”ã„2014 〜データ世界を彩るツール6é¸ã€œ
1. 1 「ã“ã® Visualiza)on ツールãŒã™ã”ã„2014〠〜データ世界を彩る美ã—ãツール6é¸ã€œ March 2nd, 2014　データå¯è¦–化勉強会 Presented by Takahiro Inoue – Chief Data Scien:st 2. 2 Agenda 1. 2. 3. 4. 5. 6. 7. 8. Introduc:on Learning Visualiza:on 表計算部門：aabel3 Takahiro noue – Chief Data Dashboard 部門：Metric IInsights Scien:st BI 部門：tableau 統計ソフト部門：ggplot2 抽象化部門：Neo4j（Graph Database） プãƒã‚°ãƒ©ãƒŸãƒ³ã‚°éƒ¨é–€ï¼šd3.js（çœç•¥ï¼‰ Presented by 4. 4 Introduc)on •  Takahiro I
Data Structure Visualization
Currently, we have visualizations for the following data structures and algorithms: Basics Stack: Array Implementation Stack: Linked List Implementation Queues: Array Implementation Queues: Linked List Implementation Lists: Array Implementation (available in java version) Lists: Linked List Implementation (available in java version) Recursion Factorial Reversing a String N-Queens Problem Indexing
JavaScriptã®ã‚³ãƒ¼ãƒ‰ãƒ•ãƒãƒ¼ã‚’視覚化ã™ã‚‹TraceGL
Spring Bootã«ã‚ˆã‚‹APIãƒãƒƒã‚¯ã‚¨ãƒ³ãƒ‰æ§‹ç¯‰å®Ÿè·µã‚¬ã‚¤ãƒ‰ 第2版 何åƒäººã‚‚ã®é–‹ç™ºè€…ãŒã€InfoQã®ãƒŸãƒ‹ãƒ–ック「Practical Guide to Building an API Back End with Spring Bootã€ã‹ã‚‰ã€Spring Bootを使ã£ãŸREST API構築ã®åŸºç¤Žã‚’å¦ã‚“ã 。ã“ã®æœ¬ã§ã¯ã€å‡ºç‰ˆæ™‚ã«æ–°ã—ãリリースã•ã‚ŒãŸãƒãƒ¼ã‚¸ãƒ§ãƒ³ã§ã‚ã‚‹ Spring Boot 2 を使用ã—ã¦ã„る。ã—ã‹ã—ã€Spring Boot3ãŒæœ€è¿‘リリースã•ã‚Œã€é‡è¦ãªå¤‰...
Every Noise at Once
A book about how streaming changes music, by Glenn McDonald; Canbury Press, 2024
Git Cheatsheet
stash workspace index local repository upstream repository status Displays paths that have differences between the index file and the current HEAD commit, paths that have differences between the workspace and the index file, and paths in the workspace that are not tracked by git. diff Displays the differences not added to the index. diff commit or branch View the changes you have in your workspace
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