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Viewing: ST 114 : Statistical Programming

Last approved: Mon, 13 Jun 2016 19:53:38 GMT

Last edit: Mon, 13 Jun 2016 19:53:32 GMT

Catalog Pages referencing this course
Change Type
Major
ST (Statistics)
114
032342
Dual-Level Course
Cross-listed Course
No
Statistical Programming
Statistical Programming
College of Sciences
Statistics (17ST)
Term Offering
Fall Only
Offered Every Year
Fall 2016
Previously taught as Special Topics?
No
 
Course Delivery
Face-to-Face (On Campus)

Grading Method
Graded with S/U option
3
16
Contact Hours
(Per Week)
Component TypeContact Hours
Lecture3
Problem Session1
Course Attribute(s)


If your course includes any of the following competencies, check all that apply.
University Competencies

Course Is Repeatable for Credit
No
 
 
Eric Chi
Assistant Professor

Open when course_delivery = campus OR course_delivery = blended OR course_delivery = flip
Enrollment ComponentPer SemesterPer SectionMultiple Sections?Comments
Lecture4040NoAll incoming FR ST majors will be batch enrolled
Problem Session4040NoAll incoming FR ST majors will be batch enrolled
Open when course_delivery = distance OR course_delivery = online OR course_delivery = remote
Restriction: Statistics majors only
Is the course required or an elective for a Curriculum?
Yes
SIS Program CodeProgram TitleRequired or Elective?
17STBSStatisticsElective
This is an introductory course in computer programming for statisticians using Python. Emphasis is on designing algorithms, problem solving, and forming good coding practices: methodical development of programs from specifications; documentation and style; appropriate use of control structures such as loops, of data types such as arrays; modular program organization; version control. Students will become acquainted with core statistical computational problems through examples and coding assignments, including computation of histograms, boxplots, quantiles, and least squares regression.

As computation becomes a more critical skill for statisticians we must introduce a wider variety of programming skills, and introduce those skills earlier in our curriculum. This course will primarily target first-semester Freshmen Statistics majors, and provide them with both an overview of programming principles and a set of skills specific for carrying out statistical calculations. The standard introductory CSC courses (112, 116) do not have the statistical content this course will contain, and because of demand from the COE it is virtually impossible for our students to take those courses as Freshmen. Discussion with the DUP for CSC, Dr. Dennis Bahler, documents that CSC is supportive of this course.


No

Is this a GEP Course?
No
GEP Categories

Humanities Open when gep_category = HUM
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Global Knowledge Open when gep_category = GLOBAL
Each course in the Global Knowledge category of the General Education Program will provide instruction and guidance that help students to achieve objective #1 plus at least one of objectives 2, 3, and 4:
 
 

 
 

 
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Requisites and Scheduling
 
a. If seats are restricted, describe the restrictions being applied.
 

 
b. Is this restriction listed in the course catalog description for the course?
 

 
List all course pre-requisites, co-requisites, and restrictive statements (ex: Jr standing; Chemistry majors only). If none, state none.
 

 
List any discipline specific background or skills that a student is expected to have prior to taking this course. If none, state none. (ex: ability to analyze historical text; prepare a lesson plan)
 

Additional Information
Complete the following 3 questions or attach a syllabus that includes this information. If a 400-level or dual level course, a syllabus is required.
 
Title and author of any required text or publications.
 

 
Major topics to be covered and required readings including laboratory and studio topics.
 

 
List any required field trips, out of class activities, and/or guest speakers.
 

College(s)Contact NameStatement Summary
College of EngineeringDennis BahlerDr Bahler's response to this course proposal was "This sounds like a great idea."
This course will require no additional resources. The instructor will teach the class as part of his regular teaching load, a TA will come from our existing pool, and we will use our recently installed computing equipment for the virtual computing lab. The department has a small number of laptops for loan to students who do not own their own for use in the course.

Upon completion of this course students will be able to:


- write basic software using modern software best practices


- develop programs from the specification stage through to completion


- effectively use modern flow control and data structures


- efficiently code a variety of statistical calculations, including algorithms on matrices and vectors


Student Learning Outcomes

Students will:



  • apply classic problem-solving techniques to create simple computational programs, namely breaking large problems into smaller ones and reasoning through alternative cases.

  • evaluate arithmetic expressions using order operations, promotion from integer to floating-point types, and integer division; demonstrate effective strategies for working with finite precision arithmetic.

  • write code containing effective flow control– selecting one course of action among several alternatives based on more than one predicate.

  • correct syntax errors, and distinguish runtime errors from errors in logic.

  • find and correct logical programming errors via debugging printout and systematic searching.

  • implement an object-oriented design.

  • demonstrate the use of basic recursion.

  • implement the basic elements of version control.

  • write effective program documentation.


Evaluation MethodWeighting/Points for EachDetails
Homework60%Weekly programming assignments.
Project40%Two major programming projects.
TopicTime Devoted to Each TopicActivity
Introduction to programming in Python1 week
Designing and Using Functions1 week
Program Logic and Organization1 week
Data Storage with Lists1 week
Flow control1 week
Testing and Debugging1 week
More on Data Storage1 week
Algorithm Design1 week
Searching and Sorting2 weeks
Objected Oriented Programming2 weeks
Programming with Matrices and Vectors2 weeks
Interfacing with Databases1 week
We anticipate that all of our incoming Freshmen ST majors will take this course, along with roughly 50% of our transfer students (about half of our majors are internal transfers). Transfer students who have completed CSC 112 or 116 will not be required to take it.
svhoward (Wed, 20 Apr 2016 17:21:20 GMT): svhoward: Lecture contact hours updated to 3, with instructor permission.
Key: 9796