Désactiver la navigation animée
Désactiver la navigation animée

  • Module Overview

    Big Data Management and Analysis

    Welcome!

    Get ready to explore the science behind handling, analyzing, and extracting value from massive datasets. You'll gain practical skills and theoretical foundations for real-world impact in business, research, and technology!

    Course Chapters & Highlights

    1. Introduction to Big Data

      Big Data and its 5V model (Volume, Velocity, Variety, Veracity, Value).
      How Big Data drives smarter decision-making and business intelligence.
      Cloud computing solutions and everyday examples of big data in action.

    2. Data Centers & Cloud Computing

      Behind the scenes: physical and software infrastructure.
      Cloud service models (IaaS, PaaS, SaaS).
      How data gets distributed: sharding and consistent hashing.
      Powerful distributed processing: MapReduce, MVCC, Vector-clock.
      Discovering the Hadoop ecosystem.

    3. Storage & Engineering for Big Data

      Why traditional databases struggle with big data.
      HDFS architecture for scalable storage.
      NoSQL types and principles (Key-Value, Column, Document, Graph).
      Examples: MongoDB, Cassandra, ElasticSearch.
      Green IT: efficient storage & querying.
      Safety first: data security, integrity, and privacy.
      Modern tools: Spark, Flink.
      Priority research & innovation topics.

    4. Big Data Analytics

      Challenges in analyzing huge, complex data.
      Machine learning basics for big data.
      Analytics for text, the web, and streaming data.

    5. Managing Projects & Implementation

      Planning and delivering successful big data projects
      Hybrid architectures and integration techniques
      Team roles, collaboration, and project flow
      Technical, legal, and ethical questions
      Ethics: privacy, transparency, responsibility

    ✔️ Learning Outcomes

    • Recognize and explain the main concepts and models behind big data
    • Design, implement, and manage distributed data systems
    • Work with NoSQL and big data architectures for real applications
    • Apply analytics and machine learning to massive data sets
    • Deliver, secure, and ethically manage big data projects
    • Stay ahead of trends and new developments in big data

    ⏳ Workload and Assessment

    Weekly Workload
    Lectures: 1 × 1.5 hours/week
    Practicals: 1 × 1.5 hours/week

    Assessment Structure
    Final Exam: 60% (written, 90 minutes)
    Continuous Assessment: 40%, includes attendance, participation, conduct, performance in practicals, online quizzes & assignments (see Moodle)

    📑 Attendance Policy & FAQ

    Do I have to come to every class?
    We strongly encourage attendance at all lectures for the best experience! Practicals are mandatory.

    What happens if I miss class?
    Missing three sessions without a valid reason means exclusion from the course. Five absences, even justified, also mean exclusion, no exceptions.

    What counts as a "justified" absence?
    Only official documents (medical certificate, official letter, etc.) stamped by the department administration count. Personal excuses without paperwork don’t count.

    I need to miss a class, what should I do?
    Act quickly! You have 3 days to notify the department administration, submit your documentation, and have it stamped. Then, bring the stamped document to your instructor at your next class.
    Missing the 3-day window makes your absence unjustified.