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Big-Scale Analytics

  • Enseignant(s):   M.Vlachos  
  • Titre en français: Analyse de la balance Web
  • Cours donné en: anglais
  • Crédits ECTS: 6 crédits
  • Horaire: Semestre de printemps 2019-2020, 4.0h. de cours (moyenne hebdomadaire)
  •  séances
  • site web du cours site web du cours
  • Formation concernée: Maîtrise universitaire ès Sciences en systèmes d'information

 

Objectifs

Many of the world's biggest discoveries and decisions in science, technology, business, medicine, politics, and society as a whole, are now being made on the basis of analyzing large data sets. This course provides a practical introduction to big data: data analysis techniques including databases, data mining, machine learning, text analysis and data visualization. During the class we will mostly be using Python as the programming language. We will also learn how to use cloud services, elasticsearch, deploy services on the cloud, etc.

Contenus

This class represents a continuation of the “Data Mining and Machine Learning” course of the last quarter. We will continue our exploration of how to use algorithms and tools for analyzing data and extracting insights.

We will explore Big Data frameworks, SQL, text analytics, noSQL, visualization, search, clustering, association rules and other topics related to Big Data Analytics.

Each weekly 4h block will consist of 2h lecture and 2h lab session. In the lab sessions you get hands-on exercises in Python and you will also be exposed to cloud-based services.

Références

The material for this course will consist of slides given by the instructor and articles from various sources that you will have to read and discuss.

Books (recommended reading but not required)

  • Mining Massive Datasets (by J. Leskovec, A. Rajaraman, J. Ullman)
  • Data Mining (by Charu Agrawal)

Pré-requis

  • The fall MScIS “Data Mining and Machine Learning” course.
  • Knowledge of Python.
  • Knowledge of calculus and statistics.

Evaluation

1ère tentative

Examen:
Sans examen (cf. modalités)  
Evaluation:
  • in-class quizzes
  • personal assignments
  • group project(s)
  • class participation

Rattrapage

Examen:
Ecrit 2h heures
Documentation:
Non autorisée
Calculatrice:
Non autorisée
Evaluation:

Written exam.



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