Vision, Mission, Goals and Objectives

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HOLY ANGEL UNIVERSITY College of Engineering & Architecture

Department of Computer Engineering

University Vision, Mission, Goals and Objectives:

Mission Statement (VMG)

We, the academic community of Holy Angel University, declare ourselves to be a Catholic University. We dedicate ourselves to our core purpose, which is to provide accessible quality education that transforms students into persons of conscience, competence, and compassion. We commit ourselves to our vision of the University as a role-model catalyst for countryside development and one of the most influential, best managed Catholic universities in the Asia-Pacific region. We will be guided by our core values of Christ-centeredness, integrity, excellence, community, and societal responsibility. All these we shall do for the greater glory of God. LAUS DEO SEMPER!

College Vision, Goals and Objectives:

Vision

A center of excellence in engineering and architecture education imbued with Catholic mission and identity serving as a role-model catalyst for countryside development

Mission

To provide accessible quality engineering and architecture education leading to the development of conscientious, competent and

compassionate professionals who continually contribute to the advancement of technology, preserve the environment, and improve life for countryside development.

Goals

The College of Engineering and Architecture is known for its curricular programs and services, research undertakings, and community involvement that are geared to produce competitive graduates:

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- who are equipped with high impact educational practices for global employability and technopreneurial opportunities;

- whose performance in national licensure examinations and certifications is consistently above national passing rates and that falls within the 75^th to 90^th percentile ranks; and,

- who qualify for international licensure examinations, certifications, and professional recognitions;

Objectives

In its pursuit for academic excellence and to become an authentic instrument for countryside development, the College of Engineering and Architecture aims to achieve the following objectives:

1. To provide students with fundamental knowledge and skills in the technical and social disciplines so that they may develop a sound perspective for competent engineering and architecture practice;

2. To inculcate in the students the values and discipline necessary in developing them into socially responsible and globally competitive professionals;

3. To instill in the students a sense of social commitment through involvement in meaningful community projects and services;

4. To promote the development of a sustainable environment and the improvement of the quality of life by designing technology solutions beneficial to a dynamic world;

5. To adopt a faculty development program that is responsive to the continuing development and engagement of faculty in research, technopreneurship, community service and professional development activities both in the local and international context;

6. To implement a facility development program that promotes a continuing acquisition of state of the art facilities that are at par with leading engineering and architecture schools in the Asia Pacific region; and,

7. To sustain a strong partnership and linkage with institutions, industries, and professional organizations in both national and international levels.

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Relationship of the Program Educational Objectives to the Vision-Mission of the University and the College of Engineering & Architecture:

Computer Engineering Program Educational Outcomes (PEOs):

Within a few years after graduation, our graduates of the Computer Engineering program are expected to have:

Vision-Mission

Christ-

Centeredness Integrity Excellence Community Societal

Responsibility

1. Practiced their profession     

2. Shown a commitment to life-long learning     

3. Manifested faithful stewardship     

Relationship of the Computer Engineering Program Outcomes to the Program Educational Objectives:

Computer Engineering Student Outcomes (SOs):

At the time of graduation, BS Computer Engineering program graduates should be able to:

PEOs

1 2 3

a) Apply knowledge of mathematics, physical sciences, and engineering sciences to the practice of Computer

Engineering.   

b) Design and conduct experiments, as well as to analyze and interpret data   

c) Design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability, in accordance with standards

  

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d) Function on multidisciplinary teams   

e) Identify, formulate and solve engineering problems   

f) Have an understanding of professional and ethical responsibility   

g) Demonstrate and master the ability to listen, comprehend, speak, write and convey ideas clearly and effectively, in

person and through electronic media to all audiences.   

h) Have broad education necessary to understand the impact of engineering solutions in a global, economic,

environmental, and societal context   

i) Recognition of the need for, and an ability to engage in life-long learning and to keep current of the development

in the field   

j) Have knowledge of contemporary issues   

k) Use the techniques, skills, and modern engineering tools necessary for engineering practice.    l) Have knowledge and understanding of engineering and management principles as a member and leader in a

team, to manage projects and in multidisciplinary environments.   

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COURSE SYLLABUS

Course Title: DATA STRUCTURE AND ALGORITHM ANALYSIS LECTURE Course Code: DATASTRUCT

Course Credit: 3 Units Year Level: 3^rd Year

Pre-requisites:

Co-requisite:

COMFUN

DATASTRUCTL

Course Calendar:

2^nd Semester Course Description:

The course includes linear data structures such as arrays, stacks, queues, linked-lists; nonlinear data structures such as generalized lists, trees, and graphs; operations on these using algorithms such as insertions, deletions, and traversals.

Course Outcomes (COs):

After completing this course, the students should be able to:

Relationship to the Program Outcomes:

a b c d e f g h i j k l

1) Recognize the different linear and nonlinear data structures

E 2) Graphically represent any data structure

D 3) Have a clear understanding of the algorithms for creating, accessing,

and destroying structural information E E

4) Determine the complexity of common algorithms

E E

5) Apply programming techniques like searching and sorting in solving

problems D D D

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COURSE ORGANIZATION Time

Frame

Hours Course

Outcomes Course Outline

Teaching & Learning

Activities Assessment Tools

Resources Week

1

3 CO1

CO2

OVERVIEW OF DATA STRUCTURES AND ALGORITHMS

 Some Uses for Data Structures and Algorithms

 Overview of Data Structures

 Overview of Algorithms

 Some Initial Definitions

 A Quick Introduction to Object-Oriented Programming

 New C++ Features

 Software Engineering Library Activity:

 Arrays

 Lecture

 Multimedia instruction

 Small group discussion on real-life applications of Data Structures and Algorithms

 Class discussion

 Questioning

 Small group activities on OOP and UML design

 Library work: Arrays

Seatwork Classroom assignment Recitation

Direct observation Board Work Group work

A[1], A[4], B[1], B[2], B[3], B[4], B[5]

Week 2

3 CO2

CO4

Arrays

 The Array Workshop Applet

 An Array Example

 Dividing a Program Into Classes

 Class Interfaces

 Small group activities on Arrays

 Questioning

A[2], A[3], A[4], B[1], B[2], B[3], B[4], B[5]

Week 3

3 CO2

CO3 CO4

Ordered Arrays

 The Ordered Array Workshop Applet

 Logarithms

 Storing Objects

 Big O Notation

 Small group activities on Ordered Arrays

 Questionings

 Library work: Sorting

Direct observation

A[2], A[3], A[4], B[1], B[2], B[3], B[4], B[5]

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 Why Not Use Arrays for Everything?

Library Activity:

 Sorting

Board Work Group work Quiz

Week 4

3 CO3

CO4 CO5

The Bubble Sort

 Sorting

 Inventing Your Own Sorting Algorithm

 Bubble-Sorting the Baseball Players

 The bubbleSort Workshop Applet

 Implementing C++ Code for a Bubble Sort

 Invariants

 Efficiency of the Bubble Sort Library Activity

 The bubbleSort Workshop Applet

 Lecture

 Small group activities on Bubble Sort

 Questioning

A[2], A[3], A[4], B[1], B[3], B[5]

Week 5

3 CO3

CO4 CO5

The Insertion Sort

 Insertion Sort on the Baseball Players

 The insertSort Workshop Applet

 Implementing the Insertion Sort in C++

 Efficiency of the Insertion Sort

 Sorting Objects

 Another Feature of Sorting Algorithms: Stability

 Comparing the Simple Sorts Library Activity

 Stacks

 Small group activities on Insertion Sort

 Questioning

 Library work: Stacks

Direct observation Board Work Group work Quiz

A[2], A[3], A[4], B[1], B[3], B[5]

Week 6

3 CO1

CO2 CO3 CO4

Stacks

 A Different Way to Think About Data Structure

 Understanding Stacks

 Implementing a Stack in C++

 Lecture

 Small group activities on Queues

A[2], A[3], A[4], B[1], B[3], B[5]

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CO5  Reversing a Word

 Delimiter Matching

 Efficiency of Stacks

Queues and Priority Queues

 Queues

 Priority Queues

 Questioning

 Small group discussion on real-life applications of Stacks

Written examination

PRELIM EXAMINATION Week

7

3 CO1

CO2 CO3 CO4 CO5

Linked Lists

 Understanding Links

 The LinkList Workshop Applet

 Implementing a Simple Linked List

 Finding and Removing Specified Links

 The Efficiency of Linked Lists

 Lecture

 Small group activities on Linked Lists

 Questioning

A[2], A[3], A[4], B[1], B[3], B[5]

Week 8

3 CO1

CO2 CO3 CO4 CO5

Abstract Data Types

 A Stack Implemented By a Linked List

 Double-Ended Lists

 Implementing a Queue Using a Linked List

 Data Types and Abstraction

 Using ADTs as a Design Tool

 Abstract is a Relative Term

 Small group activities on Abstract Data Types

 Questioning

A[2], A[3], A[4], B[1]

Week 9

3 CO1

CO2 CO3 CO4 CO5

Specialized Lists

 Sorted Lists

 List Insertion Sort

 Doubly Linked Lists Recursion

 Demonstrating Recursion with Triangular Numbers

 Small group activities on Specialized Lists

 Small group activities in Recursion

 Questioning

A[2], A[3], A[4], B[1], B[3], B[5]

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 Characteristics of Recursive Functions

 Demonstrating Recursion with Anagrams

 Demonstrating Recursion in a Binary Search

 Recursion Versus Stacks

Quiz

Week 10

3 CO1

CO2 CO3 CO4 CO5

Applied Recursion

 The Towers of Hanoi

 Mergesort Quicksort

 Partitioning

 Basic Quicksort

 Lecture

 Small group activities

 In Applied Recursion and Quicksort

 Questioning

A[2], A[3], A[4], B[1], B[3], B[5]

Week 11

3 CO3

CO4 CO5

Improving Quicksort

 Problems with Inversely Sorted Data

 Handling Small Partitions

 Efficiency of Quicksort Library Activity:

 Binary Trees

 Small group activities in Improving Quicksort

 Questioning

 Library work: Binary Trees

A[2], A[4], B[1], B[3], B[5]

Week 12

3 CO1

CO2 CO3 CO4 CO5

Binary Trees

 Why Use Binary Trees?

 What is a Tree?

 Basic Binary Tree Operations

 Finding a Node

 Inserting a Node

 Deleting a Node

 Lecture

 Small group discussion on Binary Trees

 Questioning

Written examination

A[2], A[3], A[4], B[1], B[5]

MIDTERM EXAMINATION

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Week 13

3 CO1

CO2 CO3 CO4 CO5

Traversing Binary Trees

 Traversing the Tree

 Finding Maximum and Minimum Values

 The Efficiency of Binary Trees

 Duplicate Keys

 Implementing a Binary Search Tree in C++

 Small group discussion on Traversing Binary Trees

 Questioning

A[2], A[3], A[4], B[1], B[5]

Week 14

3 CO1

CO2 CO3 CO4 CO5

Red-Black Trees

 Our Approach to the Discussion

 Balanced and Unbalanced Trees

 Using the RBTree Workshop Applet

 Simple Insertions

 Rotations Library Activity

 The RBTree Workshop Applet

 Small group activities on Red-Black Trees

 Questioning

 Library work:

A[2], A[3], B[1], B[5]

Week 15

3 CO1

CO2 CO3 CO4 CO5

Red-Black Tree Insertions

 Inserting a New Node

 Deletion

 Efficiency of Red-Black Trees

 Implementing the Insertion Process

 Other Balanced Trees 2-3-4 Trees

 Introduction to 2-3-4 Trees

 The Tree234 Workshop Applet

 Small group activities on Red-Black Trees Insertions

 Questioning

A[2], A[3], B[1], B[5]

Week 16

3 CO1

CO2

Implementing 2-3-4 Trees

 Implementing a 2-3-4 Tree in C++  Small group activities on Implementing 2-3-4

Seatwork Classroom

A[2], A[3], B[1], B[5]

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CO3 CO4 CO5

 2-3-4 Trees and Red-Black Trees

 Efficiency of 2-3-4 Trees Hash Tables

 Introduction to Hash Tables

 Linear Probing

 C++ Code for a Linear Probe Hash Table

Trees

 Small group discussion on Hash Tables and Linear Probing

 Questioning

assignment Recitation

Week 17

3 CO1

CO2 CO3 CO4 CO5

Quadratic Probing

 Quadratic Probing

 Double Hashing

 Efficiency of Open Addressing Separate Chaining

 The HashChain Workshop Applet

 C++ Code for Separate Chaining

 Efficiency of Separate Chaining

 Open Addressing Versus Separate Chaining

 Hash Functions Library Activity

 Graph Algorithms

 Lecture

 Questioning

 Library work: Graph Algorithms

A[2], A[3], A[4], B[1], B[3], B[5]

Week 18

3 CO1

CO2 CO3 CO4 CO5

GRAPH ALGORITHMS

 Graphs

 Data Structures for Graphs

 Graph Traversals

 Directed Graphs

 Shortest Path

 Minimum Spanning Trees

 Weighted Graphs

 Lecture

 Small group discussion on Graph Algorithms real life applications

 Small group activities on Graph Algorithms

 Jeopardy game

Recitation

Direct observation Board work

Group work Final Project Project presentation

Written examination

A[2], A[3], A[4], B[1], B[5]

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FINAL EXAMINATION

Course References:

A. Basic Readings

1) Antonakos, J.L. (2011). Data structure and software engineering: challenges and improvements. Apple Academic Press, Inc.

2) Goodrich, M.T. (2011). Data Structures & Algorithms in C++. John, Wiley, Danvers, MA 3) Main, M. (2011). Data structures & other objects using C++. Pearson

4) Malik, D.S. (2015). C++ programming: program design including data structures. Cengage Learning B. Online References

1) Dale, N. (2011). C++ Plus Data Structures Fifth Edition. Jones & Barlett Learning, LLC. Retrieved from

https://books.google.com.ph/books?id=bdzzlLOJb1YC&printsec=frontcover&dq=C%2B%2B&hl=en&sa=X&ved=0ahUKEwi0nsWx- qLOAhVGpZQKHe7PDwo4KBDoAQhQMAk#v=onepage&q=C%2B%2B&f=false

2) Davis, S.R. (2014). Beginning Programming with C++ For Dummies 2^nd Edition. John Wiley & Sons, Inc. Retrieved from

https://books.google.com.ph/books?id=WXrDBAAAQBAJ&printsec=frontcover&dq=Beginning+Programming+with+C%2B%2B+For+

Dummies&hl=en&sa=X&ved=0ahUKEwi9suDl-qLOAhWCW5QKHWEVA18Q6AEIGjAA#v=onepage&q=

Beginning%20Programming%20with%20C%2B%2B%20For%20Dummies&f=false

3) Lambert, K. (2013). Fundamentals of Python: Data Structures. Cengage Learning. Retrieved from

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http://site.ebrary.com/lib/haulib/detail.action?docID=10791253&p00=data+algorithms+structures

4) Ravichandran, D. (2011). Programming with C++ Third Edition. Tata McGraw Hill Education Private Limited. Retrieved from

https://books.google.com.ph/books?id=Zw0jqouq61gC&printsec=frontcover&dq=C%2B%2B&hl=en&sa=X&ved=0ahUKEwiNusDU- aLOAhXIppQKHffYALY4HhDoAQgkMAI#v=onepage&q=C%2B%2B&f=false

5) Stephens, R. (2013). Essential Algorithms: A Practical Approach to Computer Algorithms. Wiley. Retrieved from http://site.ebrary.com/lib/haulib/detail.action?docID=10740157&p00=data+algorithms+structures

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Course Requirements 1) 3 Major Exams (Prelims, Midterms, and Finals) 2) 6 Quizzes

3) Assignments &Seatworks

Grading System CAMPUS++ COLLEGE ONLINE GRADING SYSTEM

Legend: (All Items in Percent)

CSA Class Standing Average for All Performance Items (Cumulative) P Prelim Examination Score

M Midterm Examination Score F Final Examination Score MEA Major Exam Average PCA Prelim Computed Average MCA Midterm Computed Average FCA Final Computed Average

Computation of Prelim Computed Average (PCA) CSA =

MEA = P

PCA = (60%)(CSA) + (40%)(MEA)

Computation of Midterm Computed Average (MCA) CSA =

MEA =

MCA = (60%)(CSA) + (40%)(MEA)

Computation of Final Computed Average (FCA)

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CSA =

MEA =

FCA = (60%)(CSA) + (40%)(MEA) Passing Percent Average: 50

Transmutation Table

Range of Computed Averages Range of Transmuted Values Grade General Classification 94.0000 – 100.0000 97 – 100 1.00 Outstanding

88.0000 – 93.9999 94 – 96 1.25 Excellent 82.0000 – 87.9999 91 – 93 1.50 Superior 76.0000 – 81.9999 88 – 90 1.75 Very Good 70.0000 – 75.9999 85 – 87 2.00 Good 64.0000 – 69.9999 82 – 84 2.25 Satisfactory 58.0000 – 63.9999 79 – 81 2.50 Fairly Satisfactory 52.0000 – 57.9999 76 – 78 2.75 Fair

50.0000 – 51.9999 75 3.00 Passed

Below Passing Average 5.00 Failed

6.00 Failure due to absences 8.00 Unauthorized or unreported withdrawal

Note: A student's Computed Average is a consolidation of Class Standing Percent Average and Major Exam Percent Average.

Course Policies Maximum Allowable Absences: 10 (held 3 times a week); 7 (held 2 times a week)

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Date Revised: Date Effectivity: Prepared By: Checked By: Approved By:

May 30, 2016 June, 2016 Engr. Gerard C. Cortez CpE Faculty

Engr. Gerard C. Cortez Chairperson, CpE Department

Dr. Doris Bacamante

Dean, College of Engineering and Architecture