Master of Science in Data Science La Salle Campus Barcelona URL

Master of Science in Data Science

Become an expert in analysing, structuring, filtering, visualizing and valuing the production of generated data

Computing infrastructures

Description
The course will address the evolution experienced by computing infrastructures over the last 25 years. In order to understand the relevant role played by infrastructures in the emergence and consolidation of data science, our journey will analyze the paradigm shift that has occurred. We have moved from processing reasonable amounts of data using personal computers to having huge volumes of information in the cloud. Edge computing, virtual machines, containers, orchestrators, serverless technologies, cloud platforms, and many other concepts are what every data scientist must have in his or her backpack to be able to tackle any kind of project in the data world.
Type Subject
Primer - Obligatoria
Semester
First
Course
1
Credits
5.00
Previous Knowledge

Recommended: basic knowledge of computer science and programming, although no advanced knowledge of programming is required, as the contents are very standard and with a high level of language.

Objectives

The goals will focus on:
• Provide knowledge of the most commonly used tools and platforms currently used in data projects.
• Know and control virtualization systems, container orchestrators and cloud platform services.
• Introduce the use of Python as a basic tool in the day-to-day work of the data scientist.

Contents

Chapter 1: Computing in the 20th century
• Classical Paradigm
• Computational revolution

Chapter 2: Welcome to the 21st century
• Virtualization, Hypervisors
• Software Defined Concepts
• IaaS, PaaS, SaaS

Chapter 3: Containers, trending topic
• History and evolution
• Docker, Images, Registry, Containers, Deployment
• Orchestrators: Swarm, Kubernetes, EKS...

Chapter 4: Cloud Services & Serverless Technologies
• AWS, GCP, DigitalOcean, Linode...
• Service discovery
• AWS Lambda as a Serverless model

Chapter 5: Introduction to Python 3
• Practical approach of language usage
• Using basic data structures

Note: topics may be adjusted and/or modified at the master's coordination discretion.

Methodology

The methodology used combines master classes, student participation and the development of a project. For the student, this will involve both individual and group works, as well as conceptual exercises, written exercises, and oral presentations.

Evaluation

Continuous assessment

Evaluation Criteria

Continuous assessment
This subject will be assessed on a continuous basis via oral presentations, assignments and/or exercises and class participation. 60% of the evaluation will depend on: (i) class participation (attendance is recommended); (ii) the completion of an individual work on the design and implementation of a data project. The remaining 40% will depend on the presentations of the collective exercises that will be detailed at the beginning of the classes

Extraordinary call
The exam and/or works of extraordinary call will be determined from the coordination of the subject.

Copies regulations
The subject is governed by the general regulations of copies of La Salle Campus BCN:
<https://www.salleurl.edu/en/copies-regulation>

The training activities will be considered to have the following category:
• Exercises: moderately significant
• Project: highly significant
• Final Evaluation: highly significant

Basic Bibliography

• Class/Lecture notes
• Documentation and papers uploaded to Intranet (eStudy)
• Link to technical videos

Additional Material

All class material (presentations, exercises, articles, documents, etc.) will be shared in the subject folder of the La Salle Intranet: eStudy.