Algorithms, Data Structures and Databases

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Credits
6
Types
Compulsory
Requirements
This subject has not requirements, but it has got previous capacities
Department
CS;ESSI
Web
https://learnsql.fib.upc.es/moodle/
This is a fundamental course that covers basic concepts on algorithms, data structures and databases. Even if the objectives of the course are common, this course spans two different tracks: one for students who have a major in Computer Science and another track for the rest.

The methodology is different for both tracks. For the former, it is a project-oriented course focusing on dataOps / MLOps, while for the latter it covers basic material on algorithms, data structures and databases in a guided, yet autonomous learning manner that will allow them to develop complex data systems

Teachers

Person in charge

  • Anna Queralt Calafat ( )
  • Maria Josefina Sierra Santibañez ( )

Others

  • Oscar Romero Moral ( )

Weekly hours

Theory
1
Problems
0
Laboratory
3
Guided learning
0
Autonomous learning
7.11

Competences

Transversal Competences

Information literacy

  • CT4 - Capacity for managing the acquisition, the structuring, analysis and visualization of data and information in the field of specialisation, and for critically assessing the results of this management.

Third language

  • CT5 - Achieving a level of spoken and written proficiency in a foreign language, preferably English, that meets the needs of the profession and the labour market.

Basic

  • CB6 - Ability to apply the acquired knowledge and capacity for solving problems in new or unknown environments within broader (or multidisciplinary) contexts related to their area of study.
  • CB9 - Possession of the learning skills that enable the students to continue studying in a way that will be mainly self-directed or autonomous.

Generic Technical Competences

Generic

  • CG1 - Identify and apply the most appropriate data management methods and processes to manage the data life cycle, considering both structured and unstructured data

Technical Competences

Especifics

  • CE1 - Develop efficient algorithms based on the knowledge and understanding of the computational complexity theory and considering the main data structures within the scope of data science
  • CE2 - Apply the fundamentals of data management and processing to a data science problem

Objectives

  1. To analyse the cost of iterative and recursive algorithms
    Related competences: CT4, CT5, CE1,
  2. To review some simple data structures: stacks, queues, lists, and trees
    Related competences: CT4, CT5, CE1,
  3. To know, explain, design, analyse, compare and implement the main data structures and algorithms that can be used to implement priority queues
    Related competences: CT4, CT5, CE1,
  4. To know, explain, design, analyse, compare and implement the main data structures and algorithms that can be used to implement dictionaries
    Related competences: CT4, CT5, CE1,
  5. To know, explain, design, analyse, compare and implement the main data structures and algorithms that can be used to represent graphs and solve classic graph problems such as traversals, topological ordering and shortest paths
    Related competences: CT4, CT5, CE1,
  6. To know, understand, explain, analyse and compare some algorithm design techniques: greedy, divide and conquer, and dynamic programming
    Related competences: CT4, CT5, CE1,
  7. To be aware of the limits of computation: to understand the definitions of the P and NP classes, the concept of Polynomial-Time reduction, the notion of NP-Completeness, and to know some classic NP-complete problems
    Related competences: CT4, CT5, CE1,
  8. Describe what is a database and a database management system
    Related competences: CT4, CT5, CE2,
  9. Effectively use the standard Structured Query Language (SQL) to query relational databases
    Related competences: CT4, CT5, CE2,
  10. Explain the relational data model, including its data structures, the relational algebra and integrity constraints
    Related competences: CT4, CT5, CE2,
  11. Given a set of informational requirements, model the logic schema of a relational database
    Related competences: CT4, CT5, CE2,
  12. Identify the main objectives of a database management system query optimizer
    Related competences: CT4, CT5, CE2,
  13. Apply data structures, algorithms and database queries to solve a problem in a realistic situation
    Related competences: CT5, CG1, CE1, CE2, CB6, CB9,

Contents

  1. Basics of Analysis of Algorithms
    Worst case, best case and average case cost analysis. Asymptotic order of growth notations: Big-O, Omega and Theta. Analysis of the correctness and cost of iterative and recursive algorithms.
  2. Simple Data Structures: Review
    Stacks, queues, lists and trees.
  3. Priority Queues
    Operations of priority queues. Implementations with heaps. Heapsort.
  4. Dictionaries
    Operations of dictionaries. Basic implementations: tables and lists. Advanced implementations: hash tables, binary search trees, and AVL trees.
  5. Graphs
    Representations: adjacency matrices, adjacency lists and implicit representations. Depth-first search (DFS). Breadth-first search (BFS). Topological sort. Algorithms for shortest paths. Algorithm for minimum spanning trees.
  6. Algorithm design techniques
    Greedy, divide and conquer, and dynamic programming.
  7. Introduction to NP and Computational Intractability
    Basic introduction to P and NP classes, Polynomial-Time reduction, and NP-completeness. Examples of classic NP-complete problems.
  8. Introduction to databases and database management systems
    Main concepts on databases and database management systems. Relational database management systems.
  9. SQL: Data-definition language and data-manipulation language
    Introduction to the SQL language.
  10. The relational model
    Data structures and integrity constraints.
  11. The relational algebra
    The relational algebra operators and how to build data pipes with them. Notion of semantic and syntactic optimization.
  12. Logical design of relational databases
    Design the logical schema of a database.
  13. Notions of physical design and physical database optimization
    Notions of query optimizer, access plan and cost model.
  14. Data Science-related advanced topics
    Students with a major in Computer Science will investigate on advanced topics specific for data science projects. For example, data
    quality, entity resolution, data integration, etc.

Activities

Activity Evaluation act


Basic concepts on Algorithms, Data Structures, and Databases

Motivation and main concepts on algorithmics, data structures, and database management systems.
Objectives: 1 2 8
Contents:
Theory
7.5h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Partial Exam

For students with a minor in Computer Science, this exam evaluates their knowledge on fundamental concepts of algorithms, data structures and databases.
Objectives: 3 9 4 6 7 1 2 5 8 10 11 12
Week: 8
Type: theory exam
Theory
3h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
6h

Computer Science for Data Science

Students in the minor track will put in practice all the contents of the course by solving exercises related to algorithms, data structures, and databases. Students in the major track will undertake a project spanning all main phases of a data science. As result, they are asked to develop a quality realistic end-to-end system architecture for a data science project.
Objectives: 3 9 4 6 7 5 10 11 12 13
Contents:
Theory
0h
Problems
0h
Laboratory
40.5h
Guided learning
0h
Autonomous learning
84h

Final Exam

For students with a minor in Computer Science, this exam evaluates their knowledge on fundamental concepts of algorithms, data structures and databases
Objectives: 3 9 4 6 7 1 2 5 8 10 11 12
Week: 14
Type: theory exam
Theory
3h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
6h

Teaching methodology

The students are divided in two tracks: one for students with a minor in computer science (track 1) and another one for students with a major in computer science (track 2).

Students in track 1 will study fundamental concepts in algorithms, data structures and databases. First, additional material to read, study and understand is provided. Lectures focus on the main concepts and those that require some additional explanation to guarantee a proper understanding. Students will have a large bank of self-assessing exercises to practice their understanding on their own. During the course, they will have to solve some mandatory exercises to guarantee a smooth learning process. Additionally, in the face-to-face lectures, the lecturer will solve doubts, go through representative exercises to guarantee a solid understanding and discuss exercises (to be solved during the lecture) with the students.

Students in track 2 will carry out the DS-EtE project and, for them, this course is a project course. There, students must create an end-to-end system architecture to ingest, store, process, learn models and deploy such system for a realistic project with realistic data. The students must develop good practices developing such architecture (what nowadays is known as DataOps / MLOps).

This course has a strong self-learning component and shares the same objectives in both tracks. The lecturers will supervise the students progress during the semester to guarantee a proper progress.

Evaluation methodology

Let DBM = Databases midterm exam grade,
DBF = Databases final exam grade,
DBEx = Database exercises to be solved during the course,
ADSM = Algorithms and Data Structures midterm exam grade,
ADSF = Algorithms and Data Structures final exam grade and
ADSEx = Algorithms and Data Structures exercises to be solved during the course,
DS-EtE = DS-EtE project grade

Then,

1) If the student followed track 1 (see methodology) the mark is calculated as follows:
BD = MAX (0.2*DBEx + 0.8*DBM, DBF),
ADS = MAX (0.2*ADSEx + 0.4*ADSM + 0.4*ADSF, ADSF).

ADSDB (final course mark) = 0.5*BD + 0.5*ADS

2) If the student followed track 2 (see methodology) the mark is calculated as follows:

ADSDB (final course mark) = DS-EtE

Bibliography

Basic:

Complementary:

Web links

Previous capacities

This course assumes basic competences in algorithms, data structures and databases. The course is structured to cope with different backgrounds and learning needs but basic knowledge on Computer Science principles is assumed: notions of computer architecture, basic programming constructs and data structures.