Data Driven Documents A JavaScript DOM manipulation library - PowerPoint PPT Presentation

Oct 23, 2022 •183 likes •248 views

Data Driven Documents A JavaScript DOM manipulation library Developed by Mike Bostock https://d3js.org/ one of the overall most popular projects on GitHub! Widely used for Data Vis Scalable Vector Graphics (SVG) D3 works well together with

Data Driven Documents A JavaScript DOM manipulation library Developed by Mike Bostock https://d3js.org/ one of the overall most popular projects on GitHub!
Widely used for Data Vis
Scalable Vector Graphics (SVG) D3 works well together with SVG Refresher: http://dataviscourse.net/2016/lectures/ lecture-html/ SVG is used to define vector-based graphics for the Web SVG defines the graphics in XML format SVG graphics do NOT lose any quality if they are zoomed or resized Every element and every attribute in SVG files can be animated
Key D3 Concepts Selections Data Binding Scales Axes Layouts Maps
D3 Gallery
Tutorials http://dataviscourse.net/tutorials/ SVG Exercise: https://jsbin.com/yuzomev/edit? html,js,output D3 Exercise: https://jsbin.com/yuzomev/edit? html,js,output D3 Exercise Solution: https://jsbin.com/wimonaxewa/ edit?html,css,js,output

Download Presentation

Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Data Preparation Data cleaning Data integration and transformation (Data

Data Preprocessing Why preprocess the data? Data Preparation Data cleaning Data integration and transformation (Data preprocessing) Data reduction, Feature selection Discretization and concept hierarchy generation 2

891 views • 21 slides

DATA MINING LECTURE 2 What is data? The data mining pipeline What is Data Mining? Data

DATA MINING LECTURE 2 What is data? The data mining pipeline What is Data Mining? Data mining is the use of efficient techniques for the analysis of very large collections of data and the extraction of useful and possibly unexpected

2.4k views • 94 slides

Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Inf1-DA 20102011 III: 68 / 91 Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval Statistical Analysis of Data: III.2 Data scales and summary statistics III.3 Hypothesis testing and correlation III.4 2

579 views • 8 slides

Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Inf1-DA 20102011 III: 28 / 89 Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval Statistical Analysis of Data: III.2 Data scales and summary statistics III.3 Hypothesis testing and correlation III.4

455 views • 23 slides

Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Inf1-DA 20102011 III: 51 / 89 Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval Statistical Analysis of Data: III.2 Data scales and summary statistics III.3 Hypothesis testing and correlation III.4

344 views • 17 slides

Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

729 views • 24 slides

Data Preparation Data cleaning Discretization (Data preprocessing) Data

Data Preprocessing Why preprocess the data? Data Preparation Data cleaning Discretization (Data preprocessing) Data integration and transformation Data reduction, Feature selection 2 Why Prepare Data? Why Prepare

462 views • 15 slides

DATA QUALITY AND DATA DATA QUALITY AND DATA PROGRAMMING PROGRAMMING "Data cleaning and

DATA QUALITY AND DATA DATA QUALITY AND DATA PROGRAMMING PROGRAMMING "Data cleaning and repairing account for about 60% of the work of data scientists." Christian Kaestner Required reading: Schelter, S., Lange, D., Schmidt, P.,

1.1k views • 86 slides

Data stories with The Pudding So what is data storytelling? Data academia & data science

Data stories with The Pudding So what is data storytelling? Data academia & data science dense complex static/non-interactive often not accompanied by an easy-to-follow narrative Data art beautiful abstract

647 views • 5 slides

Data Acquisition Chapter 2 Data Acquisition 1 st step: get data Usually data gathered by

Data Acquisition Chapter 2 Data Acquisition 1 st step: get data Usually data gathered by some geophysical device Most surveys are comprised of linear traverses or transects Typically constant data spacing Perpendicular to

432 views • 12 slides

Data Collection and Aggregation Data Collection and Aggregation 1 Challenges: data Challenges:

Data Collection and Aggregation Data Collection and Aggregation 1 Challenges: data Challenges: data Data type: numerical sensor readings. Rich and massive data, spatially distributed and correlated. Data dynamics: data

713 views • 48 slides

Business Statistics CONTENTS The role of data The data matrix Data types Aspects of data

DATA Business Statistics CONTENTS The role of data The data matrix Data types Aspects of data Obtaining data Further study THE ROLE OF DATA Data refers to observed facts there are 82 persons in this train the weight of

683 views • 22 slides

DataCamp Data Types for Data Science DataCamp Data Types for Data Science Data types Data type

DataCamp Data Types for Data Science DataCamp Data Types for Data Science Data types Data type system sets the stage for the capabilities of the language Understanding data types empowers you as a data scientist DataCamp Data Types for Data

526 views • 23 slides

q.Datum Data Exchange The marketplace for big data Data is the new oil of the Internet and the

q.Datum Data Exchange The marketplace for big data Data is the new oil of the Internet and the new currency of the digital world . World Economic Forum 1 Problem: access to big data Data accessed & analyzed Data in organizations

543 views • 30 slides

Concatenating data Cleaning Data in Python Combining data Data may not always come in 1

CLEANING DATA IN PYTHON Concatenating data Cleaning Data in Python Combining data Data may not always come in 1 huge file 5 million row dataset may be broken into 5 separate datasets Easier to store and share May have

425 views • 26 slides

Prioritizing Data and Purpose of Data Points What data do I have? What data do I trust? What

Prioritizing Data and Purpose of Data Points What data do I have? What data do I trust? What data do I use most? Session Target: After todays session, you will be able to: 1. Insert/include data opportunities into PL models. 2.

710 views • 12 slides

2019 Oct. Cleanup Effort: G-2 Svc. Bldg., Transfer Hall MVA alcove, SSB Jason St. John

2019 Oct. Cleanup Effort: G-2 Svc. Bldg., Transfer Hall MVA alcove, SSB Jason St. John External Beam Dept. Meeting Fermilab 2019.11.14 Before, After, and on the way Work plan documented in JHAs: 2164 and 2154 in HA database

60 views • 4 slides

S u pport Vectors L IN E AR C L ASSIFIE R S IN P YTH ON Michael ( Mike ) Gelbart Instr u ctor ,

S u pport Vectors L IN E AR C L ASSIFIE R S IN P YTH ON Michael ( Mike ) Gelbart Instr u ctor , The Uni v ersit y of British Col u mbia What is an SVM ? Linear classi ers ( so far ) Trained u sing the hinge loss and L 2 reg u lari z ation

262 views • 25 slides

Computer Graphics 3080, GV10 Lecturers Jan Kautz, j.kautz@cs.ucl.ac.uk David Swapp,

10/2/09 Computer Graphics 3080, GV10 Lecturers Jan Kautz, j.kautz@cs.ucl.ac.uk David Swapp, d.swapp@cs.ucl.ac.uk Course information http://www.cs.ucl.ac.uk/staff/j.kautz/teaching/3080 Mailing lists You need to register to one

320 views • 12 slides

Einfhrung in Visual Computing Clipping Werner Purgathofer Institut fr Computergraphik und

Einfhrung in Visual Computing Clipping Werner Purgathofer Institut fr Computergraphik und Algorithmen 6. Vorlesungseinheit Computergraphik Clipping in the Rendering Pipeline Clipping object capture/creation partly visible or

363 views • 9 slides

Lecture 4: Tableau, D3 DS 4200 F ALL 2020 Prof. Cody Dunne N ORTHEASTERN U NIVERSITY Slides and

Lecture 4: Tableau, D3 DS 4200 F ALL 2020 Prof. Cody Dunne N ORTHEASTERN U NIVERSITY Slides and inspiration from Michelle Borkin, Krzysztof Gajos, Hanspeter Pfister, 1 Miriah Meyer, Jonathan Schwabish, and David Sprague C HECK - IN 3 P

651 views • 31 slides

the long web JSON HTML SVG CSS ABC JavaScript RSS X: 1 T: Chief O'Neill's Favourite R:

1.19k views • 87 slides

Reporting and analyzing bugs How to communicate efficiently to the programmer EOT1 Bug

Reporting and analyzing bugs How to communicate efficiently to the programmer EOT1 Bug reporting Testers report bugs to programmers Problem Report forms are commonly used If the report is not clear and understandable, the bug will

2.74k views • 75 slides

Using Git Hooks to Help Your 40 40 40 Joo Santos Engineering Teams Work Software Engineer

255 105 0 149 149 149 225 225 225 68 84 106 84 28 0 0 0 153 65 0 102 Using Git Hooks to Help Your 40 40 40 Joo Santos Engineering Teams Work Software Engineer joao.santos@zalando.de Autonomously @joaomcsantos Europython 2015

511 views • 40 slides

Data Driven Documents A JavaScript DOM manipulation library - PowerPoint PPT Presentation

Data Driven Documents A JavaScript DOM manipulation library Developed by Mike Bostock https://d3js.org/ one of the overall most popular projects on GitHub! Widely used for Data Vis Scalable Vector Graphics (SVG) D3 works well together with

Data Preparation Data cleaning Data integration and transformation (Data

DATA MINING LECTURE 2 What is data? The data mining pipeline What is Data Mining? Data

Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Data Preparation Data cleaning Discretization (Data preprocessing) Data

DATA QUALITY AND DATA DATA QUALITY AND DATA PROGRAMMING PROGRAMMING &quot;Data cleaning and

Data stories with The Pudding So what is data storytelling? Data academia &amp; data science

Data Acquisition Chapter 2 Data Acquisition 1 st step: get data Usually data gathered by

Data Collection and Aggregation Data Collection and Aggregation 1 Challenges: data Challenges:

Business Statistics CONTENTS The role of data The data matrix Data types Aspects of data

DataCamp Data Types for Data Science DataCamp Data Types for Data Science Data types Data type

q.Datum Data Exchange The marketplace for big data Data is the new oil of the Internet and the

Concatenating data Cleaning Data in Python Combining data Data may not always come in 1

Prioritizing Data and Purpose of Data Points What data do I have? What data do I trust? What

2019 Oct. Cleanup Effort: G-2 Svc. Bldg., Transfer Hall MVA alcove, SSB Jason St. John

S u pport Vectors L IN E AR C L ASSIFIE R S IN P YTH ON Michael ( Mike ) Gelbart Instr u ctor ,

Computer Graphics 3080, GV10 Lecturers Jan Kautz, j.kautz@cs.ucl.ac.uk David Swapp,

Einfhrung in Visual Computing Clipping Werner Purgathofer Institut fr Computergraphik und

Lecture 4: Tableau, D3 DS 4200 F ALL 2020 Prof. Cody Dunne N ORTHEASTERN U NIVERSITY Slides and

the long web JSON HTML SVG CSS ABC JavaScript RSS X: 1 T: Chief O'Neill's Favourite R:

Reporting and analyzing bugs How to communicate efficiently to the programmer EOT1 Bug

Using Git Hooks to Help Your 40 40 40 Joo Santos Engineering Teams Work Software Engineer

DATA QUALITY AND DATA DATA QUALITY AND DATA PROGRAMMING PROGRAMMING "Data cleaning and

Data stories with The Pudding So what is data storytelling? Data academia & data science