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  Course Description
Course Name : Introductıon to R Programme

Course Code : BİS-541

Course Type : Optional

Level of Course : Second Cycle

Year of Study : 1

Course Semester : Fall and Spring (16 Weeks)

ECTS : 4

Name of Lecturer(s) : InstructorDr. YAŞAR SERTDEMİR

Learning Outcomes of the Course : Knows how to install R
Knows the meaning of certain sembols in R
knows the basic commands in R
knows to import data from external sources
knows to smilate random data
knows how to manipulate data using R
Knows how to write function in R
knows about the simulation logic
knows how to compare tests using the simulation technique

Mode of Delivery : Face-to-Face

Prerequisites and Co-Prerequisites : None

Recommended Optional Programme Components : none

Aim(s) of Course : Introduction to R programming

Course Contents : Installing R, certain symbols, their meanings and uses in R, Help files, and libraries, basic commands, storing the R working environment, arithmetic operations, the R environment, the creation of vector and matrix, descriptive statistics, classes, objects, read data from an external source, Loops(for/while ), printing data or output to an external source, changing working directory, random data generation, forming subsets of data, data manipulation, Graphics, Writing functions, the logic of simulation, simulation and test results will be studied

Language of Instruction : Turkish

Work Place : Classroom and Medical Informatics Laboratory


  Course Outline /Schedule (Weekly) Planned Learning Activities
Week Subject Student's Preliminary Work Learning Activities and Teaching Methods
1 Installing R
2 certain symbols, their meanings and uses in R Using R for introductory statistics Reading Presentation, question and answer, group discussion
3 Help files, and libraries, basic commands, storing the R working environment, Using R for introductory statistics Reading Presentation, question and answer, group discussion
4 arithmetic operations Using R for introductory statistics Reading Presentation, question and answer, group discussion, homework and reading
5 the R environment, the creation of vector and matrix Using R for introductory statistics Reading Homework and reading
6 descriptive statistics, classifying objects Using R for introductory statistics, A handbook of statistical analyses using R Reading Presentation, question and answer, group discussion, homework and reading
7 reading data from an external source and writing data or output to external files Using R for introductory statistics, A handbook of statistical analyses using R Reading Presentation, question and answer, group discussion, homework and reading
8 Mid- Term
9 changing the working directory, random data generation Using R for introductory statistics, A handbook of statistical analyses using R Reading Presentation, question and answer, group discussion, homework and reading
10 forming subsets of data, data manipulation, Using R for introductory statistics, A handbook of statistical analyses using R Reading Presentation, question and answer, group discussion, homework and reading
11 Graphics Using R for introductory statistics, A handbook of statistical analyses using R, The R BOOk Reading Presentation, question and answer, group discussion, homework and reading
12 Writing functions, the logic of simulation Using R for introductory statistics, A handbook of statistical analyses using R, The R BOOk Reading Presentation, question and answer and homework
13 Writing functions, the logic of simulation Using R for introductory statistics, A handbook of statistical analyses using R, The R BOOk Reading Presentation, question and answer and homework
14 Writing functions, the logic of simulation Using R for introductory statistics, A handbook of statistical analyses using R, The R BOOk Reading Presentation, question and answer and homework
15 comparison of statistical methods using simulation Using R for introductory statistics, A handbook of statistical analyses using R, The R BOOk Reading discussion
16/17 Final


  Required Course Resources
Resource Type Resource Name
Recommended Course Material(s)  B.S.Everitt, T.Hothorn. A handbook of statistical alalysis using R. 2005
 M.J. Crawley, The R BOOK 2007 WİLEY&SONS
 J. Verzani. Using R for Introductorey statistics
Required Course Material(s)


  Assessment Methods and Assessment Criteria
Semester/Year Assessments Number Contribution Percentage
    Mid-term Exams (Written, Oral, etc.) 1 40
    Homeworks/Projects/Others 10 60
Total 100
Rate of Semester/Year Assessments to Success 40
 
Final Assessments 100
Rate of Final Assessments to Success 60
Total 100

  Contribution of the Course to Key Learning Outcomes
# Key Learning Outcome Contribution*
1 Students design scientific research studies in order to give response to the problem arising from health and clinical sciences 0
2 Students provide consulting services by using effective communication skills; take part in research teamworks; defend the ethical rules. 0
3 Students collect data from research studies, analyze, and make inferences 0
4 Students design health survey, determine the sampling method and conduct the survey 0
5 Students knows the system of international classification of diseases, obtain and analyze hospital statistics. 0
6 Students select the appropriate statistical procedure for analysis , apply and make inferences. 0
7 Students use the necessary statistical packages for analysis, if necessary write and develop software. 5
8 Students select and use proper statistical procedure for diagnosis and in making inferences for the data in health and clinical medicine and provide consultance to clinicians in the field. 0
9 Students comprehends the fundamentals of statistical theory related to the field of health ( probability and bayesian biostatistics). 0
10 Students explain demographic terminologies and statistical methods in the field of health sciences. 0
11 Students understand and use medical terminology. 0
12 Students develop the ability of critical thinking, make a conclusion with a critical approach to the evidence 0
13 Students apply analytical procedure to frequently used survival data, multivariate procedure and regression techniques. 0
14 Students follow the latest development in medical informatics and employ frequently used tools and methods. 0
15 Students explain the fundamental terminologies in epidemiology, guide researchers conducting field survey and clinical studies, develop methodologies in determining disease risk factor and disease burden and advise for choosing proper diagnostic test. 0
* Contribution levels are between 0 (not) and 5 (maximum).

  Student Workload - ECTS
Works Number Time (Hour) Total Workload (Hour)
Course Related Works
    Class Time (Exam weeks are excluded) 16 2 32
    Out of Class Study (Preliminary Work, Practice) 14 2 28
Assesment Related Works
    Homeworks, Projects, Others 10 3 30
    Mid-term Exams (Written, Oral, etc.) 1 1 1
    Final Exam 1 1 1
Total Workload: 92
Total Workload / 25 (h): 3.68
ECTS Credit: 4