Format
Lecture: 3 hours
Discussion: 1 hour
Catalog Description:
Univariate and multivariate distributions. Markov models. Sampling, estimation and model building. Regression analysis. Applications to data mining, networks, disk systems, security, software engineering and bioinformatics.
Prerequisites: Course 40; course 50 or Engineering Electrical and Computer 70; Mathematics 21C; Mathematics 22A or Mathematics 67
Credit restrictions, cross listings: None
Summary of course contents
The course includes moderately extensive programming, platformindependent, using the opensource programming language R or the MATLAB package.
Goals: Students will: (1) understand basic notions of discrete and continuous probability; (2) understand the philosophy behind basic methods of statistical inference, and the role that sampling distributions play in those methods; (3) be able to perform correct and meaningful statistical analyses of simple to moderate complexity; and (4) have a firstlevel understanding of Monte Carlo simulation.
Illustrative reading
Possible choices include
Computer Usage:
Moderately extensive programming, platformindependent, using the opensource programming language R or the MATLAB package.
ABET Category Content:
Engineering Science: 3 units
Engineering Design: 1 unit
GE3
Science & Engineering
Quantitative Literacy
Overlap: There is some topical overlap with MAT 135A and STA 131ABC, as well as with applicationspecific probability/statistics courses such as ECI 114, EEC 161, ECN 140. This course differs greatly in its collection of topics, its usage of computers, and especially in its computer science applications.
Instructors: I. Davidson, D. Ghosal, and N. Matloff
History: 2012.10.24 (N. Matloff): changed abbreviated title, catalog description, course contents. Initial course description prepared by N. Matloff (December 2007).
Outcomes
1 
X 
an ability to apply knowledge of mathematics, science, computing, and engineering 
2 
X 
an ability to design and conduct experiments, as well as to analyze and interpret data 
3 

an ability to design, implement, and evaluate a system, process, component, or program to meet desired needs, within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability 
4 

an ability to function on multidisciplinary teams 
5 

an ability to identify, formulate, and solve computer science and enginequisites: Course 40; course 50 or Eering problems and define the computing requirements appropriate to their solutions 
6 
X 
an understanding of professional, ethical, legal, security and social issues and responsibilities 
7 

an ability to communicate effectively with a range of audiences 
8 

the broad education necessary to understand the impact of computer science and engineering solutions in a global and societal context 
9 

a recognition of the need for, and an ability to engage in lifelong learning 
10 
X 
knowledge of contemporary issues 
11 

an ability to use current techniques, skills, and tools necessary for computing and engineering practice 
12 
X 
an ability to apply mathematical foundations, algorithmic principles, and computer science and engineering theory in the modeling and design of computerbased systems in a way that demonstrates comprehension of the tradeoffs involved in design choices 
13 

an ability to apply design and development principles in the construction of software systems or computer systems of varying complexity 