{"id":20,"date":"2023-01-06T00:28:17","date_gmt":"2023-01-06T00:28:17","guid":{"rendered":"https:\/\/huangshizhaopin.com\/academics\/graduate-programs\/phd-data-science\/?page_id=20"},"modified":"2025-03-13T18:55:57","modified_gmt":"2025-03-13T18:55:57","slug":"curriculum","status":"publish","type":"page","link":"https:\/\/huangshizhaopin.com\/academics\/graduate-programs\/phd-data-science\/curriculum\/","title":{"rendered":"Curriculum"},"content":{"rendered":"<hr \/>\n<p>The Ph.D. in Data Science curriculum is 70 credits total, with 54 credits of required courses along with 16 credits of dissertation research. Students will register for 4 distinct research course sections, during which students will complete their doctoral dissertation with a principal adviser and faculty committee members. The goal of the program is to provide Ph.D. students the research training needed to advance the field of data science and to prepare them for rewarding careers in academia and industry. <span>Fully online option is also available.<\/span><\/p>\n<p>To be awarded the Doctoral Program in Data Science, students must complete the following within 10 years of first enrolling:<\/p>\n<ul>\n<li>Complete 54 credit hours of coursework plus 16 credits of dissertation research, while maintaining a cumulative grade point average of 3.0 (out of 4.0) each semester.<\/li>\n<li>Pass a Ph.D. qualifying exam after their fourth trimester.<\/li>\n<li>Pass a Ph.D. proposal defense (eligible only after passing Ph.D. qualifying exam).<\/li>\n<li>Pass a final defense of the dissertation after the completion of coursework and research.<\/li>\n<li>Complete all the steps for approval of their Ph.D. dissertation.<\/li>\n<\/ul>\n<p>Saint Peter\u2019s M.S. program alumni are eligible to transfer in up to a maximum of 18 credits towards the program (for outsiders up to 12 credits).<\/p>\n<p><strong>Business\/Management Courses \u2013 6 Credits (3 credits each)<\/strong><\/p>\n<ul>\n<li>DS \u2013 650 Data Law and Ethics and Business Intelligence<\/li>\n<li>DS \u2013 800 Forecasting Methods for Business Decisions<\/li>\n<\/ul>\n<p><strong>Technology \/ Data Science Courses \u2013 36 Credits (3 credits each)<\/strong><\/p>\n<ul>\n<li>DS \u2013 510 Introduction to Data Science<\/li>\n<li>DS \u2013 520 Data Analysis &amp; Decision Model<\/li>\n<li>DS \u2013 542 Introduction to Python<\/li>\n<li>DS \u2013 600 Data Mining<\/li>\n<li>DS \u2013 630 Machine Learning<\/li>\n<li>DS \u2013 665 Advanced Machine Learning<\/li>\n<li><span>DS \u2013 631 Deep Learning Algorithms<\/span><\/li>\n<li><span>DS \u2013 801 Advanced Data Structures &amp; Algorithms<\/span><\/li>\n<li><span>DS \u2013 802 Natural Language Processing<\/span><\/li>\n<li><span>DS \u2013 803 Optimization and Computational Linear Algebra<\/span><\/li>\n<li><span>DS \u2013 804 Advanced Optimization<\/span><\/li>\n<li><span>DS \u2013 805 Research Seminar in Forecasting or<\/span><\/li>\n<li><span>DS \u2013 806 Research Seminar in Unstructured Data Analysis<\/span><\/li>\n<\/ul>\n<p><strong>Practicum Courses \u2013 12 Credits (3 credits each)<\/strong><\/p>\n<ul>\n<li>DS \u2013 702\u00a0<span>Practicum in Data Science <\/span><\/li>\n<li>DS \u2013 703 <span>Practicum in Statistics<\/span><\/li>\n<li>DS \u2013 770 Topics in Data Science<\/li>\n<li>MS \u2013 523 Behavioral Research Methods and Design<\/li>\n<\/ul>\n<p><b>Course Requirements \u2013 4 Credits (2 credits each)<\/b><\/p>\n<ul>\n<li>DS-860 \u2013 Ph.D. Qualifying Exam<\/li>\n<li>DS-870 \u2013 Ph.D. Dissertation Proposal<\/li>\n<\/ul>\n<p><strong>Dissertation Research \u2013 12 Credits (3 credits each)<\/strong><\/p>\n<ul>\n<li>DS \u2013 871 Dissertation Seminar I<\/li>\n<li>DS \u2013 872 Dissertation Seminar II\u00a0\u00a0\u00a0\u00a0\u00a0 \u2018<\/li>\n<li>DS \u2013 873 Dissertation Seminar III<\/li>\n<li>DS \u2013 874 Dissertation Seminar IV<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><strong>Pass a Ph.D. Qualifying Examination<\/strong><\/p>\n<p>The Ph.D. qualifying exam is designed to determine whether the candidate displays the requisite data science\u00a0knowledge in the basic areas of data science and machine learning. The comprehensive exam consists of material from the following courses.<\/p>\n<ol>\n<li>DS-510 \u2013 Introduction to Data Science<\/li>\n<li>DS-520 \u2013 Data Analysis &amp; Decision Model<\/li>\n<li>DS-542 \u2013 Python in Data Science<\/li>\n<li>DS-600 &#8211; Data Mining<\/li>\n<\/ol>\n<p>To qualify for the Ph.D. qualifying examination, students must receive an B or above as their final grade for each of the courses above. Students are expected to complete this requirement by the end of their fourth trimester. Students who do not pass the Ph.D. qualifying examination requirement will be placed on academic probation and must complete the requirement by the end of their sixth trimester.<\/p>\n<p><strong>Pass a Ph.D. Proposal Defense<\/strong><\/p>\n<p>Scheduled individually for each student, the proposal defense explores research in an area of interest that might lead to an eventual dissertation topic. \u00a0Students should approach faculty about scheduling an exam at a convenient time before the end of their fifth trimester.<\/p>\n<p><strong>Ph.D. Dissertation Defense <\/strong><\/p>\n<p>The defense itself of the dissertation will involve questions and comments from the selected committee based on a thorough scrutiny of the dissertation.\u00a0Candidates should be prepared to make a case for the importance of their research, for its place in current scholarship, and for the possible future of the project, with an eye both to job placement and opportunities for peer-reviewed publication beyond the dissertation stage. All successfully defended dissertations will be published in\u00a0<a id="f0c4bd0f85" href=\"http:\/\/library.huangshizhaopin.com\/login?url=http:\/\/search.proquest.com\/pqdtglobal\">ProQuest national repository<\/a>\u00a0and the\u00a0<a id="f0c4bd0f85" href=\"https:\/\/blacklight.huangshizhaopin.com\/docshome\">Saint Peter&#8217;s University Document\u00a0Repository<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Ph.D. in Data Science curriculum is 70 credits total, with 54 credits of required courses along with 16 credits of dissertation research. Students will register for 4 distinct research course sections, during which students will complete their doctoral dissertation with a principal adviser and faculty committee members. The goal of the program is to [&hellip;]<\/p>\n","protected":false},"author":102,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"template-department-home.php","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-20","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/huangshizhaopin.com\/academics\/graduate-programs\/phd-data-science\/wp-json\/wp\/v2\/pages\/20","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/huangshizhaopin.com\/academics\/graduate-programs\/phd-data-science\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/huangshizhaopin.com\/academics\/graduate-programs\/phd-data-science\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/huangshizhaopin.com\/academics\/graduate-programs\/phd-data-science\/wp-json\/wp\/v2\/users\/102"}],"replies":[{"embeddable":true,"href":"https:\/\/huangshizhaopin.com\/academics\/graduate-programs\/phd-data-science\/wp-json\/wp\/v2\/comments?post=20"}],"version-history":[{"count":7,"href":"https:\/\/huangshizhaopin.com\/academics\/graduate-programs\/phd-data-science\/wp-json\/wp\/v2\/pages\/20\/revisions"}],"predecessor-version":[{"id":104,"href":"https:\/\/huangshizhaopin.com\/academics\/graduate-programs\/phd-data-science\/wp-json\/wp\/v2\/pages\/20\/revisions\/104"}],"wp:attachment":[{"href":"https:\/\/huangshizhaopin.com\/academics\/graduate-programs\/phd-data-science\/wp-json\/wp\/v2\/media?parent=20"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}