ECS 017: Data, Logic, & Computing

Subject
ECS 017
Title
Data, Logic, & Computing
Status
Active
Units
4.0
Effective Term
2021 Fall Quarter
Learning Activities
Lecture: 3 hours
Discussion: 1 hour
Description
Display, processing, and representation of information and data on a computer. Understanding and analyzing the digital representations of numbers, images, and sounds. Basic computer operations and their logic. Introduction to discrete mathematics in computer science, including propositional logic, proofs by induction, recursions, and counting. Introduction to algorithms. Uses of computers and their influence on society. Not open for credit to students who have completed course ECS 020 or MAT108. GE: SE, QL.
Prerequisites
MAT 016A { can be concurrent } or MAT 017A { can be concurrent } or MAT 021A { can be concurrent }
Credit Limitation
Not open for credit to students who have completed course ECS 020 or MAT108.
Enrollment Restrictions
Pass one open to Data Science majors only.

Summary of Course Content:

  1. Introduction and Course Overview
  2. Going Digital: Representing different types of information in a computer
    1. Representations of numbers: from integers and real numbers to binary formats
    2. Analog vs digital signal. Sampling and discretization
    3. Representing music, pictures, and movies in a computer
    4. Computer hardware: processors, interfaces, storage
    5. Basic principles of computer operations
  3. The logic behind computing
    1. Propositional and first order logic
    2. Basic set theory
    3. Methods of proof. Induction and recursion. Inductive definitions.
    4. Combinatorics and counting. Permutations, combinations. The Pigeonhole principle.
  4. Introduction to Algorithms by examples
    1. Program design
    2. Algorithms: correctness and complexity
    3. Basic algorithms: sorting and searching
  5. Ethical issues: computer and society
    1. Data collection and privacy
    2. Data forgery and deep fakes

Illustrative Reading:

  • S. S. Epp, Discrete Mathematics with Applications, 5th edition, Cengage Learning, 2019.
  • K. Rosen, Discrete Mathematics and Its Applications, 7th edition, McGraw-Hill, 2011.
  • M. Lipson, Schaum’s Outline of Discrete Mathematics, revised 3rd edition, 2009.
  • Selected review papers and technical papers and class notes will be used.

Potential Course Overlap:
The course overlaps with ECS 20 (Discrete Mathematics for Computer Science) but this course does not include asymptotic notation, combinatorics, discrete probability, graphs, or trees. The course overlaps with MAT 108 (Introduction to Abstract Math) but this course does not include topics like cardinal numbers. ECS 17 includes topics on ethical issues related to computer uses which neither ECS 20 nor MAT 108 cover. ECS 17 is a fundamental, preparatory course for Data Science majors.

Course Category

Lower Division