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) 
Prerequisite(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) 