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# Course Information

 Course Name Turkish Veri Bilimleri için Olasılık ve İstatistik English Probability and Statistics for Data Science Course Code YZV 231 Credit Lecture (hour/week) Recitation (hour/week) Laboratory (hour/week) Semester 4 4 - - - Course Language English Course Coordinator Gözde Ünal Abdullah Akgül Course Objectives Probability and Statistics. Course Description Probability and Statistics. Course Outcomes 1. Define laws and axioms of probability and be able to work with set theoretical rules of events and probabilities and concept of independence 2. Construct probabilities and conditional probabilities; use them in Bayes law to model simple real life problems 3. Know and utilize random variables (r.v.s), important standard models of probability density functions (pdfs) and cumulative density functions in both continuous and discrete space 4. Express multiple r.v.s with joint pdfs, relating to marginal pdfs and conditional pdfs, as well as to concepts of independence and correlatedness 5. Estimate means, variances, covariances, moments of random variables and random vectors 6. Calculate best predictors in minimum mean squared sense both for linear and nonlinear pre- dictors 7. Know the meaning and implications of limit theorems : Law of large numbers and Central limit theorem 8. Define and Utilize Basic Bayesian Statistical Inference Techniques and Classical Statistical Inference Techniques including Hypothesis Testing, Parameter Estimation, Linear Regression and Significance Testing. 9. Implement the above concepts in a programming environment (PYTHON) Pre-requisite(s) Python Programming Required Facilities Laptops / PCs Other Textbook Introduction to Probability. 2nd Ed. By D. P. Bertsekas and J.N. Tsitsiklis. Athena Scientific, 2008. Other References Intuitive Probability and Random Processes using MATLAB. By Kay, Steven, Springer 2012. (available in the University Library)