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  1. Swirl

    10 Jul 2020 | Software (On-site)

    Swirl: Learn R, in R

  2. Radiant Lite

    24 Jul 2019 | Software (On-site) | Contributor(s): Drew LaMar

    Stripped down version of Radiant on QUBES

  3. Radiant Lite

    24 Jul 2019 | Software (On-site) | Contributor(s): Drew LaMar

    Stripped down version of Radiant on QUBES

  4. Introduction to Genome Browser: What is a Gene?

    21 Feb 2018 | Teaching & Reference Material | Contributor(s): Joyce Stamm

    This resource has been updated - find the current version here: https://qubeshub.org/qubesresources/publications/850 Genes encode information that our cells use to carry out their functions. In particular,...

  5. Introduction to Genome Browser: What is a Gene?

    21 Feb 2018 | Teaching & Reference Material | Contributor(s): Joyce Stamm

    This resource has been updated - find the current version here: https://qubeshub.org/qubesresources/publications/850 Genes encode information that our cells use to carry out their functions. In particular,...

  6. Radiant

    16 Sep 2015 | Software (On-site)

    Business analytics using R and Shiny.

  7. Avida-ED Lab Book Summer 2017

    27 Jul 2017 | Teaching & Reference Material | Contributor(s): Wendy Johnson, Cory Kohn, Amy Lark, Louise Mead, Robert T Pennock, Jim Smith, Michael James Wiser

    The Summer 2017 version of the Avida-ED Lab Manual includes the addition of an exercise that covers genetic drift (Exercise 4: Exploring Population Change Without Selection) 

  8. Avida-ED Lab Book Summer 2017

    27 Jul 2017 | Teaching & Reference Material | Contributor(s): Wendy Johnson, Cory Kohn, Amy Lark, Louise Mead, Robert T Pennock, Jim Smith, Michael James Wiser

    The Summer 2017 version of the Avida-ED Lab Manual includes the addition of an exercise that covers genetic drift (Exercise 4: Exploring Population Change Without Selection) 

  9. Free Introduction to Python for Data Science Course at DataCamp

    06 Nov 2016 | Teaching & Reference Material | Contributor(s): Drew LaMar

    This resource has been updated - find the current version here: https://qubeshub.org/qubesresources/publications/915Python is a general-purpose programming language that is becoming more and more popular for doing data science. Companies worldwide are using Python to harvest insights from...

  10. Free Introduction to R Course at DataCamp

    06 Nov 2016 | Teaching & Reference Material | Contributor(s): Drew LaMar

    This resource has been updated - find the current version here: https://qubeshub.org/qubesresources/publications/914In this introduction to R, you will master the basics of this beautiful open source language, including factors, lists and data frames. With the knowledge gained in this...

  11. DataCamp

    06 Nov 2016 | Teaching & Reference Material | Contributor(s): Drew LaMar

    This resource has been updated - find the current version here: https://qubeshub.org/qubesresources/publications/913The Easiest Way to Learn Data Science OnlineMaster data analysis from the comfort of your browser, at your own pace, tailored to your needs and expertise. Whether...

  12. DataCamp

    06 Nov 2016 | Teaching & Reference Material | Contributor(s): Drew LaMar

    This resource has been updated - find the current version here: https://qubeshub.org/qubesresources/publications/913The Easiest Way to Learn Data Science OnlineMaster data analysis from the comfort of your browser, at your own pace, tailored to your needs and expertise. Whether...

  13. 50 years of Data Science

    23 Feb 2016 | Teaching & Reference Material | Contributor(s): David Donoho

    This resource has been updated - find the current version here: https://qubeshub.org/qubesresources/publications/912More than 50 years ago, John Tukey called for a reformation of academic statistics. In ‘The Future of Data Analysis’, he pointed to the existence of an as-yet...

  14. 50 years of Data Science

    23 Feb 2016 | Teaching & Reference Material | Contributor(s): David Donoho

    This resource has been updated - find the current version here: https://qubeshub.org/qubesresources/publications/912More than 50 years ago, John Tukey called for a reformation of academic statistics. In ‘The Future of Data Analysis’, he pointed to the existence of an as-yet...

  15. Modernizing Statistics Education via Biology Applications

    21 Oct 2015 | Teaching & Reference Material | Contributor(s): Olcay Akman

    This resource has been updated - find the current version here: https://qubeshub.org/publications/254In most traditional statistics courses, instructors use data from different fields in an effort to give the courses an interdisciplinary flavor. They generally fail because these attempts...

  16. HHMI Teacher Guide: Math and Statistics

    23 Sep 2015 | Teaching & Reference Material | Contributor(s): Paul Strode, Ann Brokaw

    This resource has been updated - find the current version here: https://qubeshub.org/publications/342Topics include measures of average (mean, median, and mode), variability (range and standard deviation), uncertainty (standard error and 95% confidence interval), Chi-square analysis, student...

  17. R Commander

    04 Sep 2015 | Software (On-site) | Contributor(s): Drew LaMar

    R provides a powerful and comprehensive system for analysing data and when used in conjunction with the R-commander (a graphical user interface, commonly known as Rcmdr) it also provides one that is easy and intuitive to use.

  18. BioRadiant

    19 May 2015 | Software (On-site) | Contributor(s): Drew LaMar

    Interactive statistics package using R and Shiny.

  19. Statistical Power for One-Sample t-Test

    18 May 2015 | Software (On-site) | Contributor(s): Drew LaMar

    Interactive visualization of statistical power for a one-sample t-test.

  20. A very basic tutorial for performing linear mixed effects analyses: Tutorial 2

    24 Apr 2015 | Teaching & Reference Material | Contributor(s): Bodo Winter

    This resource has been updated - find the current version here: https://qubeshub.org/qubesresources/publications/833This tutorial serves as a quick boot camp to jump-start your own analyses with linear mixed effects models. This text is different from other introductions by being decidedly...