COURSE DESCRIPTIONwebdata/planuri/licenta/en/CS1101.pdf · D. Lucanu, M. Craus. Proiectarea algoritmilor. Polirom, 2008. 9. Course content synchronization with the expectations of

COURSE DESCRIPTION

1. Program Information1.1 University "Alexandru Ioan Cuza" University of Iasi1.2 Faculty Computer Science1.3 Department Computer Science1.4 Study Domain Computer Science1.5 Study Cycle Bachelor1.6 Study Program / Qualification Computer Science

2. Course Information2.1 Course Name Data Structures

2.2 Course TeacherAssociate Prof. Madalina Ionita, Ph.D.Associate Prof. Cristian Gatu, Ph.D.

2.3 Seminary TeacherAssociate Prof. Madalina Ionita, Ph.D.Associate Prof. Cristian Gatu, Ph.D.

2.4 Study Year I 2.5 Semester 1 2.6 Evaluation E 2.7 Course Status* OB* OB – Compulsory / OP – Optional

3. Total estimated hours (hours per semester and didactic activities)3.1 Hours per week 4 in which: 3.2 course 2 3.3 seminary/laboratory 23.4 Hours in curriculum 56 in which: 3.5 course 28 3.6 seminary/laboratory 28Time Distribution hoursManual study, Course support, Bibliography, and others 14Supplementary Documentation in library, in electronic forums, and on the field 14Seminaries/laboratories preparation, homeworks, reports, portfolios and essays 28Tutoring -Evaluation 4Other activities (consultations per student) -

3.7 Total hours individual study 563.8 Total hours per semester 1163.9 Credits 5

4. Preconditions (if necessary)4.1 Of Curriculum -4.2 Of Skills -

5. Conditions (if necessary)

5.1 For Course Operation -

5.2 For Seminary/Laboratory Operation

Presence to seminar activities is mandatory

6. Specific Skills Acquired

Pro

fes

sio

nal

Ski

lls

C1. The usage of an algorithmic language.C2. Desing algorithms for solving simple and avarage complexity problems.C3. Knowledge of main data structures and their specific operations.C4. Evaluation of the worst case time-complexity of a problem.

Tra

ns

vers

al

Ski

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CT1. The ability of designing algorithms for solving problems from other disciplines/fields. CT2. The ability of using mathematical tools for the algorithm analysis.

7. Course Objectives (from the grid of specific skills acquired)

7.1

Ge

ne

ral

Ob

jec

tive

s Knowledge of the main data structures and their employment techniques.

7.2

Sp

ec

ific

Ob

jec

tiv

es

O1. The usage of an algorithmic language.O2. The knowledge of the main functions to measure the algorithm efficiency.O3. Determine the complexity order of an algorithm.O4. The knowledge of of the main data structures and their employment techniques.

8. General Description

8.1 Course Teaching MethodsObservations(hours and bibliographic references)

1. Algorithms. Algorithmic language Exposition 2

2. Algorithm efficiency analysis I Exposition 2

3. Algorithm efficiency analysis II Exposition 2

4. Recursive algorithms efficiency analysis Exposition 2

5. Lists. Stacks. Queues Exposition 2

6. Trees Exposition 2

7. Priority queues. Max-heap Exposition 2

8. Partial evaluation Written test 2

9. Graphs. Digraphs Exposition 2

10. Sorting Exposition 2

11. Searching problem Exposition 2

12. Search trees I Exposition 2

13. Search trees II Exposition 2

14. Hash tables Exposition 2

Bibliography

Main references:T.H. Cormen, C.E. Leiserson, R.L. Rivest. Introduction to Algorithms. MIT Press, 1990.D. Lucanu, M. Craus. Proiectarea algoritmilor. Polirom, 2008.

Supplementary references: S. Skiena. The Algorithm Design Manual. Springer, 2008. R. Sedgewick, K. Wayne. Algorithms. 4th ed., Addison-Wesley, 2011.

8.2 Seminary / Laboratory Teaching methodsObservations(hours and bibliographic references)

1. Algorithmic language

Review of the course topics. The students have to solve a set of exercises - individual work. Interactive discussions using the blackboard.

2

2. Arrays and structures Same as above 2

3. Algorithm efficiency analysis Same as above 2

4. Recursive algorithms Same as above 2

5. Recursive algorithms efficiency analysis Same as above 2

6. Lists. Stacks. Queues Same as above 2

7. Lists. Stacks. Queues Same as above 2

8. Partial evaluationDiscussion on the exercices ofthe written test

2

9. Binary trees Same as above 2

10. Graphs. Digraphs Same as above 2

11. Graphs. Digraphs Same as above 2

12. Sorting Same as above 2

13. Searching problem. Binary search trees Same as above 2

14. Hash tables Same as above 2

BibliographyT.H. Cormen, C.E. Leiserson, R.L. Rivest. Introduction to Algorithms. MIT Press, 1990.D. Lucanu, M. Craus. Proiectarea algoritmilor. Polirom, 2008.

9. Course content synchronization with the expectations of the community representatives, professional associations and employers from the program domain

The course contents has been designed according to the requirements of the IT companies.

10. Evaluation

Activity Type 10.1 Evaluation criteria 10.2 Evaluation methods

10.3 The weightof each evaluation form (%)

10.4 Course - the correct understanding and usage of an algorithmic language;- the ability of identifying the complexity class of an algorithm; - knowledge of of the main data structures and their employment techniques;

Written tests 50%

- the quality of stating the answers.

10.5 Seminary/ Laboratory

- the ability of describing an algorithm in an algorithmic language; - the ability to evaluate the worst-case execution time;- the usage of the appropriate data structures;The quality of the algorithm description.

- Presence to seminar activities;- Written tests;- Active involvement in theseminar activity - bonus points.

50%

10.6 Minimal performance standardsThe following criteria must be met simoultaneously: - 50% from the overall maximum points; - 40% from the maximum of the written tests; - 60% from the maximum of the seminar activity.

The fullfilment of these conditions is equivalent to the ability of designing and implementing simple and average complexity algorithms using standard data structures.

Date Course Teacher Seminary/Laboratory Teacher30.09.2017

Department Date of Approval Director of the Department