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Matthew Ragan

Function list : Functions - Google Docs Help - 1 views

  • Frequency distribution
  • FREQUENCY(data, classes)
  • FILTER(sourceArray, arrayCondition_1, arrayCondition_2, ..., arrayCondition_30)
  • ...21 more annotations...
  • SORT(data, keyColumn_1, ascOrDesc_1, keyColumn_2, ascOrDesc_2, ..., keyColumn_30, ascOrDesc_30)
  • Cross-workbook referenceImportRange(spreadsheet_key, [sheet!]range)
  • Elements based on criteriaCOUNTIF(range, criteria)
  • RANDBETWEEN (bottom, top)Returns an integer random number between bottom and top (inclusive).
  • ROUND(number, count)Rounds the given number to a certain number of decimal places according to valid mathematical criteria. Count (optional) is the number of the places to which the value is to be rounded. If the count parameter is negative, only the whole number portion is rounded. It is rounded to the place indicated by the count.
  • RAND()Returns a random number between 0 and 1.
  • AVERAGE(number_1, number_2, ... number_30)Returns the average of the arguments. Number_1, number_2, ... number_30 are numerical values or ranges. Text is ignored.
  • CONFIDENCE(alpha, STDEV, size)Returns the (1-alpha) confidence interval for a normal distribution. Alpha is the level of the confidence interval. STDEV is the standard deviation for the total population. Size is the size of the total population.
  • CORREL(data_1, data_2)Returns the correlation coefficient between two data sets. Data_1 is the first data set. Data_2 is the second data set.
  • COUNT(value_1, value_2, ... value_30)Counts how many numbers are in the list of arguments. Text entries are ignored. Value_1, value_2, ... value_30 are values or ranges which are to be counted.
  • COUNTA(value_1, value_2, ... value_30)Counts how many values are in the list of arguments. Text entries are also counted, even when they contain an empty string of length 0. If an argument is an array or reference, empty cells within the array or reference are ignored. value_1, value_2, ... value_30 are up to 30 arguments representing the values to be counted.
  • MAX(number_1, number_2, ... number_30)Returns the maximum value in a list of arguments. Number_1, number_2, ... number_30 are numerical values or ranges.
  • MEDIAN(number_1, number_2, ... number_30)Returns the median of a set of numbers. Number_1, number_2, ... number_30 are values or ranges, which represent a sample. Each number can also be replaced by a reference.
  • MIN(number_1, number_2, ... number_30)Returns the minimum value in a list of arguments. Number_1, number_2, ... number_30 are numerical values or ranges.
  • MODE(number_1, number_2, ... number_30)Returns the most common value in a data set. Number_1, number_2, ... number_30 are numerical values or ranges. If several values have the same frequency, it returns the smallest value. An error occurs when a value does not appear twice.
  • PERCENTILE(data, alpha)Returns the alpha-percentile of data values in an array. Data is the array of data. Alpha is the percentage of the scale between 0 and 1.
  • QUARTILE(data, type)Returns the quartile of a data set. Data is the array of data in the sample. Type is the type of quartile. (0 = Min, 1 = 25%, 2 = 50% (Median), 3 = 75% and 4 = Max.)
  • RANK(value, data, type)Returns the rank of the given Value in a sample. Data is the array or range of data in the sample. Type (optional) is the sequence order, either ascending (0) or descending (1).
  • STDEV(number_1, number_2, ... number_30)Estimates the standard deviation based on a sample. Number_1, number_2, ... number_30 are numerical values or ranges representing a sample based on an entire population.
  • STDEVP(number_1, number_2, ... number_30) Calculates the standard deviation based on the entire population. Number_1, number_2, ... number_30 are numerical values or ranges representing a sample based on an entire population.
  • Combines text stringsCONCATENATE(text_1, text_2, ..., text_30)Combines several text strings into one string. Text_1, text_2, ... text_30 are text passages that are to be combined into one string.
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    Google Spreadsheets Formula Help
Jenny Darrow

How to be a data journalist | News | guardian.co.uk - 0 views

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    How to be a data journalist Data journalism trainer and writer Paul Bradshaw explains how to get started in data journalism, from getting to the data to visualising it * Guardian data editor Simon Rogers explains how our data journalism operation works
Judy Brophy

Telling Stories with Data, A VisWeek 2010 Workshop - 0 views

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    How does visualization support telling a story with data? How do journalists think about data visualization as part of their stories? How can visualization tools help data storytellers construct narratives? Interactive journalism
Judy Brophy

BBC News - Finding truth and beauty in data - 0 views

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    Even better, the data visualisation tools that can manipulate and present that information are getting easier to use and available to anyone. And it is in the social and political aspects of data visualisation that its real value emerges. "If you make these tools and data available to a broader range of people you are just going to get better ideas," said Dr Austwick.
Jenny Darrow

the Data Liberation Front - 0 views

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    The Data Liberation Front is an engineering team at Google whose singular goal is to make it easier for users to move their data in and out of Google products.  We do this because we believe that you should be able to export any data that you create in (or import into) a product.  We help and consult other engineering teams within Google on how to "liberate" their products. 
Jenny Darrow

Journalism in the Age of Data on Vimeo - 2 views

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    Journalists are coping with the rising information flood by borrowing data visualization techniques from computer scientists, researchers and artists. Some newsrooms are already beginning to retool their staffs and systems to prepare for a future in which data becomes a medium. But how do we communicate with data, how can traditional narratives be fused with sophisticated, interactive information displays?
Jenny Darrow

Student Data Principles - 0 views

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    10 Foundational Principles for Using and Safeguarding Students' Personal Information High-quality education data are essential for improving students' achievement in school and preparing them for success in life. When effectively used, these data can empower educators, students, and families with the information they need to make decisions to help all learners succeed. Everyone who uses student information has a
Judy Brophy

InstantAtlas | data visualization tools for data professionals - 0 views

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    "InstantAtlas™ enables information analysts and research professionals to create highly-interactive web solutions that combine statistics and map data to improve data visualization, enhance communication, and engage people in more informed decision making."
Judy Brophy

Scraping for Journalism: A Guide for Collecting Data - ProPublica - 1 views

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    If you are a complete novice and have no short-term plan to learn how to code, it may still be worth your time to find out about what it takes to gather data by scraping web sites -- so you know what you're asking for if you end up hiring someone to do the technical work for you. How to get data from a PDF, for example
Judy Brophy

Crowdsourcing contingent salary data | Inside Higher Ed - 0 views

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    It started with an announcement in February that a University of Georgia instructor would start a crowdsourcing project to find out more about working conditions and salaries of adjuncts. Last month, a graduate student at the University of North Carolina at Greensboro announced that she was attempting something similar for graduate student employees. Both projects are attempts at gathering information -- on the salaries of adjuncts and graduate students -- where rigorously researched data is difficult to come by. Read more: http://www.insidehighered.com/news/2012/04/02/crowdsourcing-contingent-salary-data#ixzz1quha2YCW  Inside Higher Ed 
Judy Brophy

UNDP Open Data | Data | United Nations Development Programme - 0 views

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    In an effort to expand access to large data sets and information about their work, the United Nations Development Programme (UNDP) has created this website to provide access to such materials. 
Jenny Darrow

Campus Focus - 0 views

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    From an LMS provider's standpoint, the more open and flexible the LMS, the more it can be integrated with other programs for robust analysis of student activity and interaction.  According to Lou Pugliese, president of online learning solutions provider  Moodlerooms, that kind of integration is needed. Technologies exist to measure student data and interactions on a large scale, Pugliese says: The focus now is how to effectively collect data and conduct reporting on-demand within the LMS. "Over the past ten years, the LMS has managed to record the most basic of student interactions and activity, but we've barely scratched the surface in enabling universities to analyze data on an institutional level," says Pugliese. "However, new developments in analytical technologies will provide educators with the ability to measure interactions within the ever-popular collaborative tools present in today's LMS environments. Moving beyond simple traffic reporting to more comprehensive online behaviour analysis will be critical to make more effective intervention decisions."  
Jenny Darrow

Code of practice for learning analytics | Jisc - 0 views

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    Article outlining policy recommendation for institutions using student data
Jenny Darrow

https://chronicle-assets.s3.amazonaws.com/5/items/biz/pdf/ChronFocus_Analyticsv5_i.pdf - 0 views

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    As Big Data Comes to College, Officials Wrestle to Set New Ethical Norms Plus many other articles that address data
Jenny Darrow

Evidence Framework for Innovation and Excellence in Education » Blog Archive ... - 0 views

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    The Office of Educational Technology at the U.S. Department of Education asked SRI to talk to industry experts and convene a panel of researchers to understand the state of the art, the state of the practice, and the emerging field of learning analytics and educational data mining.
Matthew Ragan

GIS Explorer - 0 views

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    ArcGIS Explorer is a free virtual globe that is much more than just a fun and easy way to explore the world in 2D and 3D. You can use it to add your own data to your maps and combine it with free data from ESRI. You can also customize your maps by adding photos, reports, videos, and other information.
Jenny Darrow

Home - Information Visualization - 0 views

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    This course provides a thorough introduction to the emerging field of Information Visualization. The goal of Information Visualization is to use human perceptual capabilities to gain insights into large and abstract data sets that are difficult to extract using standard query languages. Specific abstract data sets that will be studied are: symbolic, tabular, networked, hierarchical, or textual information. The course objectives are:  *  Provide a sound foundation in human visual perception and how it relates to creating effective information visualizations.  *  Understand the key design principles for creating information visualizations.  *  Study the major existing techniques and systems in information visualization.  *  Evaluate information visualizations tools.  *  Design new, innovative visualizations.
Matthew Ragan

Data Scraping Wikipedia with Google Spreadsheets « OUseful.Info, the blog… - 0 views

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    Prompted in part by a presentation I have to give tomorrow as an OU eLearning community session (I hope some folks turn up - the 90 minute session on Mashing Up the PLE - RSS edition is the only reason I'm going in…), and in part by Scott Leslie's compelling programme for a similar duration Mashing Up your own PLE session (scene scetting here: Hunting the Wily "PLE"), I started having a tinker with using Google spreadsheets as for data table screenscraping.
Matthew Ragan

175+ Data and Information Visualization Examples and Resources - 0 views

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    Since taking a class that discussed Edward Tufte's work, I've been fascinated by turning information into visual data. His site contains many examples that you could easily spend hours on the site. I have. Plus, I spent several days browsing sites with articles, resources, and examples of infovis (information visualization) in action
Judy Brophy

Student guide to global development data on the web | Global development | guardian.co.uk - 0 views

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    Looking for figures? Here's a beginner's guide to our Global development datastore and the best sources for development data on the web
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