Difference between revisions of "SMHS Usage"

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This [[SMHS|Scientific Methods for Health Sciences EBook]] provides three types of instructional and learning materials:
 
This [[SMHS|Scientific Methods for Health Sciences EBook]] provides three types of instructional and learning materials:
−
* Datasets and natural driving motivational problems.
+
* Datasets and natural driving motivational problems
−
* Mathematical techniques and modeling methodologies.
+
* Mathematical techniques and modeling methodologies
−
* Applications with interactive Graphical interfaces for statistical computing, data exploration and IT-blended instruction.
+
* Applications with interactive graphical interfaces for statistical computing, data exploration and IT-blended instruction
−
* All learning materials and instructional resources of this EBook are freely accessible via the Internet.
 
−
* This EBook provides a multi-language support. At the bottom of any chapter/section/page one, there may obtain a machine translation of the page content into a different language.
 
  
−
This EBook is not intended to be an ''one-book-fits-all-curricula'' textbook. Most instructors that use the Scientific Methods for Health Sciences Ebook may customize some of the content, develop additional applications, discuss course-specific data and expand the materials as appropriate to their curricula.
+
Furthermore, all learning materials and instructional resources of this EBook are freely accessible via the Internet. This publication also includes multi-language support; at the bottom of any chapter, section or page, the reader may obtain a machine translation of all page content into a different language.
  
−
The novelty of the Scientific Methods for Health Sciences EBook is derived from the fact that in the context of healths science training, it integrates mathematical foundations, statistical concepts, interactive experiments, statistical computing resources, simulations, and tools for data analysis and visualization.
+
This EBook is not intended to be an ''one-book-fits-all-curricula'' textbook. Instructors that use the Scientific Methods for Health Sciences Ebook may customize some of the content, develop additional applications, discuss course-specific data, or even expand the materials as appropriate to their curricula.
  
−
===Software help===
+
The novelty of the Scientific Methods for Health Sciences EBook is derived from the fact that in the context of health sciences training, it integrates mathematical foundations, statistical concepts, interactive experiments, statistical computing resources, simulations, and tools for data analysis and visualization.
 +
 
 +
===SOCR Help===
 
The SMHS Ebook uses [[SOCR|SOCR Tools]] and [http://www.r-project.org/ R] for all examples, computational demonstrations, data analytics and visualization. The following links provide useful tutorials and help with both software environments:
 
The SMHS Ebook uses [[SOCR|SOCR Tools]] and [http://www.r-project.org/ R] for all examples, computational demonstrations, data analytics and visualization. The following links provide useful tutorials and help with both software environments:
 
* [[SOCR]]
 
* [[SOCR]]
−
** [[SOCR_Help_Pages| SOCR Help]]
+
* [[SOCR_Help_Pages| SOCR Help]]
−
** [[SOCR_About_Pages| SOCR About]]
+
* [[SOCR_About_Pages| SOCR About]]
−
** [[SOCR_Activities| SOCR Activities]]
+
* [[SOCR_Activities| SOCR Activities]]
−
** [[SOCR_Videos| SOCR Videos]]
+
* [[SOCR_Videos| SOCR Videos]]
 +
 
 +
===[http://www.r-project.org/ R Statistical Computing Software]===
 +
* [[SMHS_Usage_Rfundamentals|Some R fundamentals]]
 +
* The Software Carpentry Foundation provides useful [http://swcarpentry.github.io/r-novice-inflammation Programming with R] and [http://swcarpentry.github.io/r-novice-gapminder R for Reproducible Scientific Analysis] materials.
 +
* [http://cran.r-project.org/doc/contrib/Verzani-SimpleR.pdf A very gentle stats intro using R Book (Verzani)]
 +
* [http://www.statmethods.net/index.html Quick-R examples]
 +
* [http://www.r-tutor.com/r-introduction R-tutor Introduction]
 +
* [http://cran.r-project.org/doc/manuals/r-release/R-intro.html R project Intro]
 +
* [http://www.ats.ucla.edu/stat/r/ UCLA ITS/IDRE R Resources]
 +
 
 +
===Why Use R===
 +
 
 +
There are marked differences between different types of computational environments for data wrangling, preprocessing, analytics, visualization and interpretation.  The table below provides some rough comparisons between some of the most popular data computational platforms (''higher scores'' represent better performance within the specific category, but the scales are not normalized between categories).
 +
 
 +
More information about [http://www.socr.umich.edu/people/dinov/2017/Spring/DSPA_HS650/notes/01_Foundation.html#1_why_use_r the rationale for using R is provided on the DSPA resources].
  
−
* [http://www.r-project.org/ R]
+
<center>
−
** [http://cran.r-project.org/doc/contrib/Verzani-SimpleR.pdf A very gentle stats intro using R Book (Verzani)]
+
{| class="wikitable" style="text-align:center; width:45%" border="1"
−
** [http://www.statmethods.net/index.html Quick-R examples]
+
|-
−
** [http://www.r-tutor.com/r-introduction R-tutor Introduction]
+
! Language || OpenSource || Speed || ComputeTime || LibraryExtent || EaseOfEntry || Costs || Interoperability
−
** [http://cran.r-project.org/doc/manuals/r-release/R-intro.html R project Intro]
+
|-
−
** [http://www.ats.ucla.edu/stat/r/ UCLA ITS/IDRE R Resources]
+
| Python || Yes || 16 || 62 || 80 || 85 || 10 || 90
−
> apropos("nova")  # within R keyword search/help
+
|-
 +
| Julia || Yes || 2941 || 0.34 || 100 || 30 || 10 || 90
 +
|-
 +
| R || Yes || 1 || 745 || 100 || 80 || 15 || 90
 +
|-
 +
| IDL || No || 67 || 14.77 || 50 || 88 || 100 || 20
 +
|-
 +
| Matlab || No || 147 || 6.8 || 75 || 95 || 100 || 20
 +
|-
 +
| Scala || Yes || 1428 || 0.7 || 50 || 30 || 20 || 40
 +
|-
 +
| C || Yes || 1818 || 0.55 || 100 || 30 || 10 || 99
 +
|-
 +
| Fortran || Yes || 1315 || 0.76 || 95 || 25 || 15 || 95
 +
|}
 +
</center>
  
 +
* [https://modelingguru.nasa.gov/docs/DOC-2625 NASA Comparison of Python, Julia, R, Matlab and IDL]
 +
* [http://sebastianraschka.com/Articles/2014_matrix_cheatsheet.html Sebastian Raschka, Numeric matrix manipulation - The cheat sheet for MATLAB, Python Nympy, R and Julia, June 2014.]
  
  

Latest revision as of 10:32, 21 July 2017

Scientific Methods for Health Sciences - Learning and Instructional Usage

This Scientific Methods for Health Sciences EBook provides three types of instructional and learning materials:

  • Datasets and natural driving motivational problems
  • Mathematical techniques and modeling methodologies
  • Applications with interactive graphical interfaces for statistical computing, data exploration and IT-blended instruction

Furthermore, all learning materials and instructional resources of this EBook are freely accessible via the Internet. This publication also includes multi-language support; at the bottom of any chapter, section or page, the reader may obtain a machine translation of all page content into a different language.

This EBook is not intended to be an one-book-fits-all-curricula textbook. Instructors that use the Scientific Methods for Health Sciences Ebook may customize some of the content, develop additional applications, discuss course-specific data, or even expand the materials as appropriate to their curricula.

The novelty of the Scientific Methods for Health Sciences EBook is derived from the fact that in the context of health sciences training, it integrates mathematical foundations, statistical concepts, interactive experiments, statistical computing resources, simulations, and tools for data analysis and visualization.

SOCR Help

The SMHS Ebook uses SOCR Tools and R for all examples, computational demonstrations, data analytics and visualization. The following links provide useful tutorials and help with both software environments:

R Statistical Computing Software

Why Use R

There are marked differences between different types of computational environments for data wrangling, preprocessing, analytics, visualization and interpretation. The table below provides some rough comparisons between some of the most popular data computational platforms (higher scores represent better performance within the specific category, but the scales are not normalized between categories).

More information about the rationale for using R is provided on the DSPA resources.

Language OpenSource Speed ComputeTime LibraryExtent EaseOfEntry Costs Interoperability
Python Yes 16 62 80 85 10 90
Julia Yes 2941 0.34 100 30 10 90
R Yes 1 745 100 80 15 90
IDL No 67 14.77 50 88 100 20
Matlab No 147 6.8 75 95 100 20
Scala Yes 1428 0.7 50 30 20 40
C Yes 1818 0.55 100 30 10 99
Fortran Yes 1315 0.76 95 25 15 95


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