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CPSC 547: Information Visualization, Sep 2017. CPSC 547: Information Visualization, Sep 2017 Remember to reload the page, changes are frequent Instructor: Tamara MunznerFirst Class: Tue Sep 12 2017Last Class: Tue Dec 5 2017Time/Location: Tue 2-5, FSC 2330A Office Hours: Tue 5-6 in classroom or by appointment in X661 ICICS/CS Bldg (X Wing). UBC Cal Page: CPSC 547Canvas Page: 547 Canvas Page This page: Jump to Current Day | Short Syllabus | Detailed Syllabus | Previous Versions Other pages: Projects | Presentations | Project Description | Structure | Resources Short Weekly Syllabus Detailed Syllabus Syllabus tentative, final changes will be made by previous class (for readings). Required Reading: Visualization Analysis and Design, Tamara Munzner (A K Peters Visualization Series, CRC Press, 2014) is the course textbook.

The UBC library has multiple ebook licenses so you can read it for free: library catalog page, EZProxy direct link. All additional readings are research papers available online for free, links posted below. Required Reading none. Visualization Courses | Designing for People. MIT 6.S191: Introduction to Deep Learning.

Stanford CS 230: Deep Learning

Stanford Vision Lab. CS 424 F2016 : Visualization and Visual Analytics 1 : Angus Forbes. Week 1 — Course overview & policies — Goals of visualization — Information Visualization vs. Visual Analytics — Programming assessment quiz — Slides: Tu Th Assignments: — Complete survey & quiz — Read Munzner, chapters 1 & 2 — Set up D3.js environment and pick one example of your choice from bl.ocks.org to explain in class 8/30 — Begin data collection for Project 1 Week 2 — Introduction to Javascript and D3.js — Data abstraction, data types Links: — JS Bin examples: Basic D3v4 template SVG example Shapes from data Scatterplots Scaling Dynamic Axes - Standalone javascript/JSON examples .zip (requires a webserver: e.g. . — Munzner, chapter 3 — Murray, chapters 2,3, and 4 (if you aren't familiar with web basics); chapters 5,6,7, and 8 (for a more detailed introduction to D3) — Finish tasks 2a and 2b for Project 1 Week 3 — Visual communication — Dataset types and data types — Slides: Tu Th — Munzner, chapter 5 — Finish tasks 3 and 4 for Project 1, due Wed, 9/14 at 11:59pm.

Week 4 Week 5. UCSC Creative Coding. This course investigates advanced topics in contemporary computer graphics, with an emphasis on the creation of computational aesthetics projects. We will investigate current hardware-accelerated programming techniques using OpenGL and GLSL, as well as a range of tools that facilitate creating interactive graphics applications (such as WebGL, Three.js, and Unity3D). We will read widely from both seminal work in the fields of graphics and visualization as well as from recent papers from top-tier conferences and journals (TOG, SIGGRAPH, CG&A, TVCG, CHI, etc) and curated editions (GPU Pro, GPU Gems, ShaderX). In addition to the completion of weekly programming and writing assignments, students will be responsible for two projects that involve the creation, demonstration, and documentation of novel interactive graphics techniques.

Danielle Szafir. INFO 4602/5602 Information Visualization Spring 2018 SyllabusSpring 2017 Syllabus Data is everywhere. Charts, graphs, and other types of information visualizations help people to make sense of this data. This course explores the design, development, and evaluation of these information visualizations. INFO 3401 Information Exploration Fall 2017 Syllabus Uses DataCamp for self-study Information empowers people to build deeper understandings of the world and make more informed decisions. INFO 1201 Computational Reasoning I Fall 2016 Syllabus Introduces principles of computational thinking through the manipulation, transformation and creation of media artifacts, such as images, sound and web pages. Human-Computer Interaction Teaching Assistant, University of Wisconsin-Madison Introduction to Computer Programming Laboratory Instructor, University of Wisconsin-Madison Math and English Instructional Assistant, Kumon of Redmond.

Tamara Munzner: Talks. See also many course slides posted online. InfoVis Group Research huawei18: Huawei, Burnaby BC 2/18 Slides: pdf, pdf 16up, key 344-outro17: CPSC 344 Outro, Vancouver BC 11/17 Slides: pdf, pdf 16up, key Data Visualization as a Driver for Visual Cognition Research opam17: Workshop on Object Perception, Attention, and Memory (OPAM) 2017, Interdisciplinary Research Panel: Discover Pasteur's Quadrant: Four research communities that will inspire your work. Slides: pdf, pdf 16up, key Some Challenges of Color vad17color-short: THINK Conference 2017, Santa Cruz CA 11/17 Slides: pdf, pdf 16up, key HIBAR Meeting Reflections hibar17: HIBAR Meeting, Vancouver BC, 11/17 Slides: pdf, pdf 16up, key, Statement - Reflection on Reflection in Design Studies vis17reflect: VIS 17 Panel: Reflection on Reflection in Design Studies, Phoenix AZ 10/17 Slides: pdf Visualization Analysis & Design (80 minutes, 68 slides) vad17stat545: UBC STAT 545 Guest Lecture, Vancouver BC 10/17 Slides: pdf, pdf 16up, key Slides: pdf, pdf 16up, key.

Tamara Munzner, UBC Home Page. Tamara Munzner InfoVis Group Professor Department of Computer Science, University of British ColumbiaImager Graphics, Visualization and HCI Lab Email: tmm (at) cs.ubc.ca, Phone: 604-827-5200, Fax: 604-822-5485, Twitter: @tamaramunzner Snailmail: 201-2366 Main Mall, Vancouver, BC, V6T 1Z4, Canada Office: X661, in X-Wing extension behind ICICS/CS buildingOffice Hours: by appointment (email me) Calendar: My free/busy calendar. CS 638 Fall 2016: Introduction to Data Science - AnHai's Group. TR 2:30-3:45pm in 1221 CS Building, 3 Credits Announcements The class mailing list is compsci638-1-f16@lists.wisc.eduThe class's Piazza page is at This is a forum for the students. We will monitor occasionally but do not have enough man power to answer all questions posted to this page. Instructor & TAs AnHai Doan, contact information available from my homepage. Office hours: Thursdays 4-5pm and by appointment (pls send email, thanks)TAs: Rashi Jalan <rjalan@wisc.edu>, office hours: Mon 11-12 and Wed 4-5, Room 1306.and Sidharth Mudgal <sidharth@cs.wisc.edu>, office hours: Tue 11-12 and Fri 2-3, Room 1304.Course Description, Prerequisites, and FAQs Course Format & Grading See the above course descriptionMidterm: Thur Oct 27, in class at usual time/roomFinal: Mon Dec 19, 7:25pm-9:25pmOther important dates: first class: Tue Sept 6, Thanksgiving break: Nov 24-27, last class: Thur Dec 15Grading: midterm: 30%, final: 30%, project: 40% Project Resources.

Teaching. CS109 Data Science. Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data management to be able to access big data quickly and reliably; exploratory data analysis to generate hypotheses and intuition; prediction based on statistical methods such as regression and classification; and communication of results through visualization, stories, and interpretable summaries.

We will be using Python for all programming assignments and projects. All lectures will be posted here and should be available 24 hours after meeting time. The course is also listed as AC209, STAT121, and E-109. Lectures and Labs Lectures are 2:30-4pm on Tuesdays & Thursdays in Northwest B103 Labs are 10am-12pm on Fridays, Room: Geological Museum 100 Instructors Rafael Irizarry, Biostatistics Verena Kaynig-Fittkau, Computer Science Guest Lecturer Marc Streit Staff. Introduction to Data Science. Some datasets for teaching data science · Simply Statistics. In this post I describe the dslabs package, which contains some datasets that I use in my data science courses. A much discussed topic in stats education is that computing should play a more prominent role in the curriculum.

I strongly agree, but I think the main improvement will come from bringing applications to the forefront and mimicking, as best as possible, the challenges applied statisticians face in real life. I therefore try to avoid using widely used toy examples, such as the mtcars dataset, when I teach data science. However, my experience has been that finding examples that are both realistic, interesting, and appropriate for beginners is not easy. After a few years of teaching I have collected a few datasets that I think fit this criteria. To facilitate their use in introductory classes, I include them in the dslabs package: install.packages("dslabs") Below I show some example of how you can use these datasets.

Library("dslabs") data(package="dslabs") Learn R, Python & Data Science Online. Introduction to the Tidyverse. Fast.ai · Making neural nets uncool again. Data Visualization. Modules: Data Visualization Announcement: You can check out The DataVis YouTube Channel for supplementary material, tutorials on data visualization, and help with test preparation. An amazing collection of data visualization tools is here: Another amazing collection is here: All students taking the Data Visualization module are welcome to come join us every Thursday at The Visible Lunch Recommended Co-Requisites for Masters Students Recommended Co-Requisite: CS_M37 Graphics Surveys and Research Methodology Recommended Co-Requisite: CS_M67 Graphics Processor Programming Recommended Co-Requisite: CS_M57 Computer Graphics Visual Computing Project (MRes only) All of the lecture material, including the material for the assessed coursework, can be found by logging onto Blackboard.

Links to Visualization Books: Interactive Data Visualization by M. Links related to Assessed Coursework material: Cognitive Class - Free Data Science and Cognitive Computing Courses. Data Analysis and Visualization. Deeplearning.ai. T005x Course Info. I ranked every Intro to Data Science course on the internet, based on thousands of data points. A year ago, I dropped out of one of the best computer science programs in Canada. I started creating my own data science master’s program using online resources. I realized that I could learn everything I needed through edX, Coursera, and Udacity instead. And I could learn it faster, more efficiently, and for a fraction of the cost.

I’m almost finished now. For the first guide in the series, I recommended a few coding classes for the beginner data scientist. Now onto introductions to data science. (Don’t worry if you’re unsure of what an intro to data science course entails. For this guide, I spent 10+ hours trying to identify every online intro to data science course offered as of January 2017, extracting key bits of information from their syllabi and reviews, and compiling their ratings. Since 2011, Class Central founder Dhawal Shah has kept a closer eye on online courses than arguably anyone else in the world.

How we picked courses to consider Each course must fit three criteria: 1. 2. Calling Bullshit. Teaching — Enrico Bertini. I have taught Information Visualization at NYU Tandon every year since 2012. The course focuses on how to design, develop and evaluate interactive data visualization solutions for complex data analysis problems. This page links to material I developed for the course. Feel free to use it in your course or to study visualization on your own. Lecture Slides Google folder containing my slides: Exercises I designed these exercises for my flipped-classroom version of the course: Data Abstraction (describe data in ways useful to vis design)Data Analysis (perform data analysis with a goal)Chart Encoding and Decoding (deconstruct a chart and encode the same data in different ways)Vis Design: Ballot Maps (design a visualization for a specific problem)Vis Design: Twitter Sentiment (design a visualization for a specific problem)Course Recap (recall main concepts from the course) Course Diary.

StatGraphCourse < Main < Vanderbilt Biostatistics Wiki. SFU Statistics - Stat 201. Stat-201 - Statistics for the Life Sciences NOTE: Because of an "upgrade" to our webserver, all access controls were modified. Unfortunately, SFU has decided not provide any resources to fix this problem. Consequently, these pages are no longer available to SFU students. Welcome to Stat-201. Appreciate the ubiquitous role of variability in real life. This will be updated as the course progresses. Instructors Class room etiquette; Course expectations Course Information Evaluation policy Term test dates Policy on Academic dis-honesty Where to get the JMP software Course outline Assignments Policy on late assignments How assignments are graded The actual assignments and their solutions.

Course Notes and other reference material Exams and term tests Study guides for exam this term Copies of old exams (with solutions) Multiple Choice, Short Answer, Long Answer questions Sample multiple choice, short answer, and long answer questions with solutions. Visual Storytelling | The Idea Center.

UoW - CSE512 - Data Visualization

CSE 5334:002 Data Mining - Home. CSE4334/5334 Data Mining. Course Description: This is an introductory course on data mining. Data Mining refers to the process of automatic discovery of patterns and knowledge from large data repositories, including databases, data warehouses, Web, document collections, and data streams. We will study the basic topics of data mining, including data preprocessing, data warehousing and OLAP, data cube, frequent pattern and association rule mining, correlation analysis, classification and prediction, and clustering, as well as advanced topics covering the techniques and applications of data mining in Web, text, big data, social networks, and computational journalism.

Student Learning Outcomes: A solid understanding of the basic concepts, principles, and techniques in data mining; an ability to analyze real-world applications, to model data mining problems, and to assess different solutions; an ability to design, implement, and evaluate data mining software. Assignments and Deadlines.

Harvard CS 109 - Data Science

Dataquest. Visualization - Courses · Stanford HCI Group. Updates as the year goes along. HCI Program Sheets note Project courses often require applications. Factor that into your plans. (e.g., don't create a degree plan that only works if you get into a limited-enrollment course. Make sure to have a backup plan.)cs193 classes do not count towards degree requirements.To get quarterly announcements about project presentations, join the HCI friends list. related courses outside cs The below courses are likely of interest to HCI students.

Mechanical Engineering (Design Division) ME28 Professional Design Practices: Portfolio Building ME101 Visual Thinking ME115A Introduction to Human Values in Design (Undergraduate Only) ME208 Patent Law and Strategy for Innovators and Entrepreneurs ME216A-C Advanced product design ME310A-C Project-Based Engineering Design, Innovation, and Development ME313 Human Values and Innovation in Design Complete ME design division course listing Management Science and Engineering. Cs448b-fa16-wiki. Main Page - CS 294-10 Visualization Fa13. Chap3. CSE 591 - Visual Analytics. Klaus Mueller's homepage. Evergreen Data Academy. Wannabe Data Scientist! Here are 8 free online courses to start… – Medium. Online Learning. Advanced Data Structures (6.851) Google Computer Science Education.

Linkedin. LinkedIn Learning: Online Courses for Creative, Technology, Business Skills. Google Computer Science Education. Seminars · Stanford HCI Group - CS547 Spring 2016. Metis. Big Data University | Data Science Courses. Information Design and Visualization | Art + Design | College of Arts, Media and Design. Data Visualization for Business. Accenture Labs: Data Insights. Dear Data: Business School Edition | PolicyViz. Big Data for Smart Cities. Learn Data Analysis - Free Curriculum | Springboard. Springboard - Learn Data Science & UX Design online.

Data Visualization with Python | Los Angeles, Irvine, CA. TDWI Online Learning. Visual Literacy: An E-Learning Tutorial on Visualization for Communication, Engineering and Business. Error Bars Considered Harmful | UW Graphics Group. About to teach Statistical Graphics and Visualization course at CMU | Civil Statistician. Statistical Graphics and Visualization course materials | Civil Statistician. Tapestry 2016 materials: LOs and Rubrics for teaching Statistical Graphics and Visualization | Civil Statistician. Information Visualization (Online MPS) | MICA.

Journalism in the Age of Data: A Video Report on Data Visualization by Geoff McGhee.