Computer Science
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# CSC 217 - Syllabus

The City College of New York • Grove School of Engineering • Computer Science Department • Course Syllabus

 Course number CSc 21700 Course name Probability and Statistics for Computer Science Credits & hours 3 cr., 3 hr. Course coordinator Prof. Leonid Gurvits

Textbook, title, author, and year

• Introduction to Probability and Statistics for Engineers and Scientists, Sheldon M. Ross, Third Edition. Academic Press
• Other supplemental materials: course related materials may be posted to course website

Specific course information

• Overview of applicable discrete and stochastic foundations: combinatorics, probability, and Monte Carlo methods. Descriptive statistics for data analysis. Random variables, mathematical expectation. Study of the constant density and random number generator, normal, exponential, as well as Bernoulli, Binomial and Poisson distributions. Limit theorems and Sample statistics. Foundations of discrete event simulation, computational examples.
• Prereq.: Math 20100 with minimum C grade, CSc 10300, CSc 10400
• Required course

Specific goals for the course and Relationship to student outcomes

 1 2 3 4 5 6 a. the student acquires knowledge of descriptive statistics and the ability to describe real, everyday data by using the concept of sample mean and variance, correlation coefficient R b. the student acquires knowledge of probability concepts and the ability to apply probability theory to gain insight into real problems and situations and to applications like simulation R c. the student acquires knowledge of random variables, expectation and their use in applications R d. the student acquires knowledge of the basic concepts in computer simulation, central limit theorem and the distribution of sample statistics I I e. the student learns to develop team interaction and horizontal networking, which is critical to improve learning curve, through programming project illustrating basic statistical techniques, with written and oral presentations R I
 I - introductory-level; R - reinforced-level; P - program-level

Brief list of topics to be covered

 Seq. Topics 1 Introduction to Statistics 2 Descriptive Statistics 3 Elements of Probability 4 Random Variables and Expectation 5 Special Random Variables 6 Distribution of Sample Statistics 7 Computer Experiments, Simulation