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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">JDS</journal-id>
      <journal-title-group>
        <journal-title>Journal of Data Science</journal-title>
      </journal-title-group>
      <issn pub-type="epub">1680-743X</issn>
      <issn pub-type="ppub">1680-743X</issn>
      <publisher>
        <publisher-name>SOSRUC</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="publisher-id">150</article-id>
	  <article-id pub-id-type="doi">10.6339/JDS.201707_15(3).0001</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>MatLab vs. Python vs. R</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Ozgur</surname>
            <given-names>Ceyhun</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_000"/>
        </contrib>
        <aff id="j_JDS_aff_000">Valparaiso University</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Colliau</surname>
            <given-names>Taylor</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_001"/>
        </contrib>
        <aff id="j_JDS_aff_001">Valparaiso University</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Rogers</surname>
            <given-names>Grace</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_002"/>
        </contrib>
        <aff id="j_JDS_aff_002">Valparaiso University</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Hughes</surname>
            <given-names>Zachariah</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_003"/>
        </contrib>
        <aff id="j_JDS_aff_003">Valparaiso University</aff>
      </contrib-group>
      <volume>15</volume>
      <issue>3</issue>
      <fpage>355</fpage>
      <lpage>372</lpage>
      <permissions>
        <ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/>
      </permissions>
      <abstract>
        <p>Matlab, Python and R have all been used successfully in teaching college students fundamentals of mathematics &amp; statistics. In today’s data driven environment, the study of data through big data analytics is very powerful, especially for the purpose of decision making and using data statistically in this data rich environment. MatLab can be used to teach introductory mathematics such as calculus and statistics. Both Python and R can be used to make decisions involving big data. On the one hand, Python is perfect for teaching introductory statistics in a data rich environment. On the other hand, while R is a little more involved, there are many customizable programs that can make somewhat involved decisions in the context of prepackaged, preprogrammed statistical analysis.</p>
      </abstract>
      <kwd-group>
        <label>Keywords</label>
        <kwd>MatLab</kwd>
        <kwd>Python</kwd>
        <kwd>R</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
