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Course Description Course Name : Computational Statistics Course Code : İSB351 Course Type : Compulsory Level of Course : First Cycle Year of Study : 3 Course Semester : Fall (16 Weeks) ECTS : 5 Name of Lecturer(s) : Assoc.Prof.Dr. ALİ İHSANGENÇ Learning Outcomes of the Course : Learns the basics of a statistical package, for instance R. Plots univariate data and does their analysis. Plots bivariate data, does regression and correlation analyses. Learns the properties of some well known distributions via computer and solves related problems. Perfoms computer simulations. Finds confidence intervals and tests statistical hypotheses via computer. Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : This course aims to teach the basics of a statistical software and how to use it for solving a practical problem. Course Contents : Data types, univariate data and graphs, bivariate data and graphs, regression, distributions, central limit theorem, simulation, confidence interval, hypothesis tests Language of Instruction : Turkish Work Place : Faculty of Arts and Sciences Annex classrooms   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Data types, basics of R program Source reading Lecture, discussions, applications 2 Univariate data and graphs Source reading, preparing projects on a computer Lecture, discussions, applications 3 Univariate data and graphs Source reading, preparing projects on a computer Lecture, discussions, applications 4 Univariate data and graphs Source reading, preparing projects on a computer Lecture, discussions, applications 5 Bivariate data and graphs Source reading, preparing projects on a computer Lecture, discussions, applications 6 Bivariate data and graphs Source reading, preparing projects on a computer Lecture, discussions, applications 7 Bivariate data and graphs Source reading, preparing projects on a computer Lecture, discussions, applications 8 Mid-term exam Review the topics discussed in the lecture notes and sources Written exam 9 Distributions and central limit theorem Source reading, preparing projects on a computer Lecture, discussions, applications 10 Simulation Source reading, preparing projects on a computer Lecture, discussions, applications 11 Confidence intervals Source reading, preparing projects on a computer Lecture, discussions, applications 12 Confidence intervals Source reading, preparing projects on a computer Lecture, discussions, applications 13 Hypothesis tests Source reading, preparing projects on a computer Lecture, discussions, applications 14 Hypothesis tests Source reading, preparing projects on a computer Lecture, discussions, applications 15 Hypothesis tests Source reading, preparing projects on a computer Lecture, discussions, applications 16/17 Final exam Review the topics discussed in the lecture notes and sources Written exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  Using R for Introductory Statistics, J. Verzani, Chapman and Hall/ CRC, Boca Raton, 2005. Required Course Material(s)  Introductory Statistics with R, P. Dalgaard, Springer, New York, 2008.  A Beginner´s Guide to R, A. F. Zuur, E. N. Ieno, E. H.W.G. Meesters, Springer, New York, 2009.   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 100     Homeworks/Projects/Others 10 0 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 Utilize computer systems and softwares 5 2 Apply the statistical analyze methods 5 3 Make statistical inference(estimation, hypothesis tests etc.) 5 4 Generate solutions for the problems in other disciplines by using statistical techniques 4 5 Discover the visual, database and web programming techniques and posses the ability of writing programme 0 6 Construct a model and analyze it by using statistical packages 5 7 Distinguish the difference between the statistical methods 4 8 Be aware of the interaction between the disciplines related to statistics 3 9 Make oral and visual presentation for the results of statistical methods 5 10 Have capability on effective and productive work in a group and individually 4 11 Develop scientific and ethical values in the fields of statistics-and scientific data collection 0 12 Explain the essence fundamentals and concepts in the field of Probability, Statistics and Mathematics 3 13 Emphasize the importance of Statistics in life 5 14 Define basic principles and concepts in the field of Law and Economics 0 15 Produce numeric and statistical solutions in order to overcome the problems 5 16 Construct the model, solve and interpret the results by using mathematical and statistical tehniques for the problems that include random events 5 17 Use proper methods and techniques to gather and/or to arrange the data 3 18 Professional development in accordance with their interests and abilities, as well as the scientific, cultural, artistic and social fields, constantly improve themselves by identifying training needs 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) 14 2 28     Out of Class Study (Preliminary Work, Practice) 14 2 28 Assesment Related Works     Homeworks, Projects, Others 10 1 10     Mid-term Exams (Written, Oral, etc.) 1 28 28     Final Exam 1 30 30 Total Workload: 124 Total Workload / 25 (h): 4.96 ECTS Credit: 5