Checking date: 22/05/2022

Course: 2022/2023

Data structures and algorithms
Study: Bachelor in Data Science and Engineering (350)


Department assigned to the subject: Computer Science and Engineering Department

Type: Basic Core
ECTS Credits: 6.0 ECTS


Branch of knowledge: Engineering and Architecture

Requirements (Subjects that are assumed to be known)
- Programming (Course: 1 / Semester: 1) - Calculus (Course: 1 / Semester: 1)
Skills and learning outcomes
Description of contents: programme
1. Abstract Data Type 2. Linear Abstract Data Types: stacks, queues, linked lists. 3. Complexity of Algorithms. 4. Recursive Algorithms. 5. Trees 6. Graphs. 7. Divide and Conquer.
Learning activities and methodology
THEORETICAL-PRACTICAL CLASSES. [44 hours with 100% classroom instruction, 1.67 ECTS] Knowledge and concepts students must acquire. Student receive course notes and will have basic reference texts to facilitate following the classes and carrying out follow up work. Students partake in exercises to resolve practical problems and participate in workshops and evaluation tests, all geared towards acquiring the necessary capabilities. TUTORING SESSIONS. [4 hours of tutoring with 100% on-site attendance, 0.15 ECTS] Individualized attendance (individual tutoring) or in-group (group tutoring) for students with a teacher. STUDENT INDIVIDUAL WORK OR GROUP WORK [98 hours with 0 % on-site, 3.72 ECTS] WORKSHOPS AND LABORATORY SESSIONS [8 hours with 100% on site, 0.3 ECTS] FINAL EXAM. [4 hours with 100% on site, 0.15 ECTS] Global assessment of knowledge, skills and capacities acquired throughout the course. METHODOLOGIES THEORY CLASS. Classroom presentations by the teacher with IT and audiovisual support in which the subject's main concepts are developed, while providing material and bibliography to complement student learning. PRACTICAL CLASS. Resolution of practical cases and problem, posed by the teacher, and carried out individually or in a group. TUTORING SESSIONS. Individualized attendance (individual tutoring sessions) or in-group (group tutoring sessions) for students with a teacher as tutor. LABORATORY PRACTICAL SESSIONS. Applied/experimental learning/teaching in workshops and laboratories under the tutor's supervision.
Assessment System
  • % end-of-term-examination 50
  • % of continuous assessment (assigments, laboratory, practicals...) 50
Calendar of Continuous assessment
Basic Bibliography
  • Karumanchi, N. Data Structure and Algorithmic Thinking with Python: Data Structure and Algorithmic Puzzles. . CareerMonk Publications. 2015
  • Michael T. Goodrich and Roberto Tamassia. Data Structures and Algorithms in Python, . John Wiley & Sons. 2013
Recursos electrónicosElectronic Resources *
Additional Bibliography
  • Isabel Segura Bedmar, Harith AlJumaily, Julian Moreno Schneider, Juan Perea & Nathan D. Ryan. Algorithms and Data Structures. OCW-UC3M: 2011
(*) Access to some electronic resources may be restricted to members of the university community and require validation through Campus Global. If you try to connect from outside of the University you will need to set up a VPN

The course syllabus may change due academic events or other reasons.