CC-MEI-2017 2017-08-11T12:19:53+00:00

Cloud Computing & Analytics

Master Ingeniería Informática – FIB/UPC – 2017

Créditos ECTS: 3.0
Horas teóricas de dedicación estudiante: 75 (3.0 créditos * 25 horas/crédito)
Clases presenciales: 6’5 semanas x 4 horas semana (desde 7/04/2017 al 31/05/2017)
Entrega proyecto final: semana 12-16/06/2017 (tentativo)
Idioma de impartición de las clases curso 2017: Español
Idioma documentación curso 2017: Inglés

Contenido asignatura tentativo: quedan pendientes definir algunos detalles de contenido.

Breve descripción assignatura

Services converge and pass from the physical world to the digital world, making them accessible from any electronic device. Cloud Computing is what makes it possible for digital technology to penetrate every corner of our economy and society. Cloud computing is a service model for large-scale distributed computing. It is based on a converged infrastructure and a set of common services over which applications can be deployed and run over the network. Not only does this allow users to be connected to this new digital world through their mobile devices, it will soon also allow the connection of any object or device. This will cause a deluge of digital information requiring a big data analytics with magnitudes never seen before.

This course will start with a brief review of Cloud Computing and Big Data technologies which will shape our near future, as well as attempt to visualize in which direction this technology will take us.

In this course students will gain a practical view of the latest in Cloud technology in order to know how to start to implement a prototype that can help to develop a business idea created by the student. The students will begin creating a basic toolbox to get started in the Cloud. This will prepare them to practice with APIs, the doors in the Cloud. All these things together will allow the students to mine the deluge of data coming from the Cloud, or use new advanced analytics services provided nowadays by the Cloud. Finally, we will look under the hood of these advanced analytics services in the Cloud, either in terms of software or hardware, in order to understand how their high performance requirements can be provided.

The goal is to help students become part of this profound transformation that is causing the Advanced Analytics that the Cloud is already providing as a service, and also, to encourage their desire to want to delve further into this exciting world of technology and become actively involved.

Contenido asignatura

  • Cloud Basics
  • Current required layers in a Big Data Software Stack
  • APIs: The doors in the Cloud
  • New Software requirements for Advanced Analytics
  • New Hardware requirements for Advanced Analytics
  • Elevator Pitch presentations Comprehensive presentation of one Cloud provider (by all students)
  • Course Project
  • Lab sessions
    • Lab session 1: “Basic Toolbox to Getting Started in the Cloud”
    • Lab session 2: “Doors in the Cloud” – Case study mining the social web
    • Lab session 3: “Analysing the Cloud” – Sentimental Analysis of Tweets
    • Lab session 4: “Advanced Analytics as a Cloud Services”. Case Study: Sentimental Analysis of images
    • Lab session 5: “Under the hood of Advanced Analytic Services in the Cloud” – Case study TensorFlow (and optionally with GPUs)

 

 

Metodología docente

During the course there will be four types of activities:

a) Activities focused on the acquisition of theoretical knowledge. The theoretical activities include participatory lecture classes, which explain the basic contents of the course.

b) Activities focused on the acquisition of knowledge through experimentation by “learn by doing” approach in lab sessions guided by hands-on (and final report). Prior to the lab sessions the student will prepare a related piece of

c) A short oral presentation by the student of one of the topics in the course (indicated by the teacher). This activity requires the preparation of a short slides

d)Course project that will be based on technologies considered in this course

 

Método de evaluación

The evaluation of this course will take into account different items:

  • Attendance (minimum 80% required) & participation in class will account for 25% of the grade.
  • Homework and presentations will account for 25% of the grade.
  • Course Project will account of 15% of the grade
  • Lab sessions (+ Lab reports) will account for 35% of the grade

Conocimientos previos

Python is the programming choice language of choice for the labs sessions of this course. It is assumed that the student has a basic knowledge of Python prior to starting classes.

Any student can learn the required background by following two hands-on that provide a fast-paced introduction to the basic characteristics of Python and git (will be available here few weeks before to start the course).

Warning. Be sure to acquire the required background prior to starting CC-MEI labs.

Profesor 

Jordi Torres i Viñals
Telf: +34 93 401 7223
Email : torres@ac.upc.edu
Web: http://www.JordiTorres.Barcelona
Github: http://github.com/jorditorresBCN
Twitter: @JordiTorresBCN
Office: UPC Campus Nord, Modul C6. Room 217. Jordi Girona 1-3, 08034 – Barcelona