DEGREE PROGRAMS  Associate's Degree (Short Cycle)  Bachelor’s Degree (First Cycle)  Master’s Degree (Second Cycle)

Course Description Course Name : Basic data analysis methods in Biophysics Course Code : BFZ-510 Course Type : Compulsory Level of Course : Second Cycle Year of Study : 1 Course Semester : Fall (16 Weeks) ECTS : 7 Name of Lecturer(s) : Prof.Dr. İSMAİL GÜNAY Prof.Dr. İSMAİL GÜNAY Learning Outcomes of the Course : is aware of the importance of measurement differentiates between error and mistake differentiates between systematic and random errors presents experimental data in graphs does statistical analysis of experimental data reports the experimental results Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : to analyze the experimental data considering statistical and graphical methods and to interpret the results Course Contents : Measurement, error and error analysis, representation styles of experimental data as graph, representation of numerical results with significant figures, correlation coefficient and regression curve, curve fitting, basic statistics, student t-test, comparison of the means, ANOVA, homework1,2,3,4 Language of Instruction : Turkish Work Place : Classroom   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Measurement reads the related chapter beforehand Lecture,class discussion, problem solving, open textbook tests, take home tests 2 Error and error types reads the related chapter beforehand Lecture,class discussion, problem solving, open textbook tests, take home tests 3 Error analysis reads the related chapter beforehand Lecture,class discussion, problem solving, open textbook tests, take home tests 4 Representation of results with significant figures reads the related chapter beforehand Lecture,class discussion, problem solving, open textbook tests, take home tests 5 Representation of experimental data using graphs reads the related chapter beforehand Lecture,class discussion, problem solving, open textbook tests, take home tests 6 Correlation coefficient and regression curve reads the related chapter beforehand Lecture,class discussion, problem solving, open textbook tests, take home tests 7 curve fitting reads the related chapter beforehand Lecture,class discussion, problem solving, open textbook tests, take home tests 8 Basic statistics reads the related chapter beforehand Lecture,class discussion, problem solving, open textbook tests, take home tests 9 homework1 reads the related chapter beforehand lecture and exemplification, interactive discussion 10 Comparison of means and student t-test reads the related chapter beforehand Lecture,class discussion, problem solving, open textbook tests, take home tests 11 homework2 reads the related chapter beforehand lecture and exemplification, interactive discussion 12 Comparison of means and student t-test reads the related chapter beforehand Lecture,class discussion, problem solving, open textbook tests, take home tests 13 homework3 reads the related chapter beforehand lecture and exemplification, interactive discussion 14 ANOVA reads the related chapter beforehand lecture and exemplification, interactive discussion 15 homework4 studies books and does literature review student lectures, practises and discusses 16/17 Final exam oral and written exam, practice   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  Biostatistics-Kadir Sumbuloglu, 2000  Lecture notes  Problems and practices  Computer. statistics and Medicine - Murat Hayran, Oktay Ozdemir, Hekimler Yayin Birligi, 1996-Ankara  Basic and Clinical Biostatistics, Beth Dawson-Saunders, Robert G. Trapp, 1994 Prentice-Hall International Inc Required Course Material(s)   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 2 60     Homeworks/Projects/Others 4 40 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 do an experiment on their own 0 2 Students have analytic notion mechanism related to the field, they are able to have access to reliable knowledge, they are able to design and write a research project , they study depending on ethical values 0 3 Students are able to systematically impart the theoretical knowledge that is being investigated to the audience effectively and transfer it to public 2 4 Students are able to report the results of a research 5 5 Students are able to interpret the findings of a research 5 6 Students have scientific consideration related to the profession 1 7 Students do an experimental setup in the laboratory and study on it 0 8 Students plan an experimental research 4 9 Students get basic knowledge of the specialiity 3 10 Students prepare a project proposal by themselves 1 * 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) 14 4 56     Out of Class Study (Preliminary Work, Practice) 14 4 56 Assesment Related Works     Homeworks, Projects, Others 4 10 40     Mid-term Exams (Written, Oral, etc.) 2 5 10     Final Exam 1 5 5 Total Workload: 167 Total Workload / 25 (h): 6.68 ECTS Credit: 7