S196.1 Syllabus - 1617b.pdf - COURSE SYLLABUS STAT 196.1...

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COURSE SYLLABUS STAT 196.1 2ND SEM AY 2016-2017 1 Instructor: Michael Van Supranes (Instructor IV) Office: F.R. 19, 4 th Flr., UP School of Statistics Bldg. Email Address: [email protected] Consultation hours: TTh 8:30 AM 11:30 AM WF 1:00 PM 4:00 PM Classes: Stat 125 WFR WF 8:30 AM 10:00 AM Stat 125 WFU WF 10:00 AM 11:30 AM Stat 131 THWX TTh 1:00 PM 3:00 PM Stat 196.1 THY TTh 4:00 PM 5:30 PM UVLe Page: USAPang Stat 196.1 Password: magaaralako COURSE NUMBER: STATISTICS 196.1 COURSE TITLE: ADVANCED STATISTICAL COMPUTING COURSE DESCRIPTION: Contemporary themes in computational statistics; Applications of computing skills; Examples of computing intensive methods in statistics; Advanced data management; SQL programming, Relational Database Methodology; Efficient Programming, Macro Programming. Prereq: Stat 136. 3 units. CLASS “PHILOSOPHY”: This class is highly social but VERY rigorous. Expect a highly engaging and challenging learning environment. Your learning experience is a shared responsibility. Your teacher encourages you to learn collaboratively but prohibits you to cheat, plagiarize or copy. Be self-motivated and persevering . Don’t wait until the deadline is near. Instead, initiate the learning engagement find/make review materials, ask your peers, or arrange consultations. This course will also require you to gather “research inspirations” from other courses. You will need your k nowledge on modeling, inference and probability (practically everything you’ve learned in the BS program) in building a research topic . This class will utilize blended instruction (online and face-to-face instruction) for research consultations. In addition, our UVLe page serves as an online support. It will not serve its purpose without you engaging with it. Utilize it for your own benefit. There will be forums, activities and supplementary materials that may help you gain our learning goals. COURSE GOALS: By the end of the course, students should be able to: Create, Manage and Transform data sets appropriately using SAS Design efficient SAS programs Understand computing-intensive statistical methods Synthesize macro programs for statistical analyses and/or computing-intensive methods