Technical & Scientific Programming

Justification
Graduates in Data Science and Artificial Intelligence must be able to design, develop, test and implement applications with high-level tools and languages oriented to technical and scientific computing.

The course on Technical and Scientific Programming enables students to work with data structures (vectors and matrices) using a high-level language that facilitates the understanding of the concepts, making the work with data transparent and simpler for the programmer. In addition, specific packages and libraries allow the graphical representation and visualization of data, as well as the development of interactive web applications for data visualization.

This course contributes to the development of the following macro-competence of the degree:

CE-TI-02: Design and implement simple data processing and artificial intelligence algorithms using tools and platforms for prototyping and development of software oriented to this area, understanding the characteristics that distinguish them from others of a generic nature.

Subject competencies
CE1 - Design and implement simple data processing algorithms in a high-level programming language. 

CE2 - Design and implement modular programs that operate on simple data structures in a high-level programming language.

CE3 - Develop simple interactive web applications for data visualisation in a high-level programming language.

CE4 - Integrate simple algorithms with data structures operating on a high-level language oriented to technical and scientific calculation.

Course content
LESSON 1: Introduction

Structure of a computer; operating systems and databases. Phases of a computer problem; algorithm; instruction; program; programming languages; phases of programming; debugging; testing. Scripting languages: compiled vs. interpreted languages. Application languages vs. scripting.

LESSON 2: Introduction to programming in R and RStudio

Environment and syntax. Variables, constants and data types. Assignments; expressions and operators; precedence rules. Basic input/output. Sequential, selective and repetitive control structures. Simple data structures.

LESSON 3: Modular programming

Procedures and functions. Types of parameters. Environments; scope of variables. Packages.

LESSON 4: Computing in R and RStudio

Loading, manipulation, indexing and visualization of data. Implementation of simple data processing and artificial intelligence algorithms.

LESSON 5: Creating web applications with R and RStudio

Introduction to the Shiny package.

EVALUATION SYSTEM

CONTINUOUS EVALUATION ACTIVITIES (65% of the grade)

Deliverable 1 (15%)
Partial laboratory test (20%)
Deliverable 2 (30%)
OVERALL ASSESSMENT ACTIVITIES (35% of the grade)

Final exam (35%)
Below is a table with the evaluation instruments by competences, indicating whether they are part of the Continuous Evaluation (C) or Final Evaluation (F).

     CE1    CE2    CE3    CE4    TOTAL
Deliverable 1 (C)    15%                   15%
Partial laboratory test (C)    5%    15%              20%
Deliverable 2 (C)         5%    25%         30%
Final exam (F)              10%    25%    35%
TOTAL    25%    20%    35%    25%    100%
To successfully complete the course, in addition to obtaining an overall grade ≥5, it is necessary to obtain at least a 4 out of 10 in any of the exams (partial or final).

If the student has not satisfactorily passed the continuous evaluation, 75% of this grade will be recoverable in all competences by means of tests and activities similar to those carried out during the course and will take place on the day indicated for the final exam.

The same evaluation scheme will be maintained for the extraordinary exam.