Big Data computing technologies, BSC

Module supervisors

Table of Contents

This web page contains all the resources (courses and practical work) for the Big Data Computing Technology module, taught as part of the Bachelor of Science in Data Science for Responsible Business during semester 4.

All practical work and project reports will be submitted according to the instructions set by the teacher.


General information

Time spreading: 53 hours, from Jan. 6th to Apr. 22th, 2025

Prerequisites Description
Python Programming Knowledge of programming with Python and object-oriented programming
SQL Experience Proficiency in databases and SQL queries
Use of Terminal Familiarity with command line usage (Windows PowerShell or Terminal)

Organization of the module

The module is divided into three chapters:

  • Chapter 1. NoSql with MongoDB (8h)
    • Lecturer: Elöd Egyed-Zsigmond (INSA Lyon)
    • Assessment: Lab report (20% of the final grade).
  • Chapter 2. Big Data Technologies (25h)
    • Lecturers: Lamia Derrode & Stéphane Derrode (Centrale Lyon)
    • Assessment: Lab report (20% of the final grade) + LOD project (40% of the final grade: 20% for the report and 20% for the defense).
  • Chapter 3. Cloud computing and AWS certifications (20h)
    • Lecturer: Yann Fornier (guest speaker)
    • Assessment: AWS certifications

The 1h mid-term exam (scheduled at week #8) accounts for 20% of the final grade. The exam will cover the first two chapters.

Course schedule

Week 2025 Chapter #1 Chapter #2 Chapter #3
#2 4h
#3 4h
#4 4h
#5 4h
#6 4h 2h
#7 2h
#8 1h midterm exam
#9 2h 4h
#11 2h 2h
#12 4h
#13 2h restitution
#14 2h
#15 4h
#16 2h
#17 4h
Total 8h 25h 20h

Chapter 1. NoSql with MongoDB

  • Lecturer Elöd Egyed-Zsigmond (INSA Lyon).

  • Time allocation 8h.

  • Type of teaching in-person.

  • Assessment

    • Lab report: 20% of the final grade.

  • Course material here

Chapter 2. Big Data Technologies

  • Lecturers Lamia Derrode & Stéphane Derrode (Centrale Lyon).

  • Time allocation 25h, including 1h for mid-term exam and 2h for project restitution.

  • Type of teaching in-person.

  • Organisation

    • Part 1. Linked Open Data (LOD) technology (6h) and project (5h).
    • Part 2. Hadoop framework, including HDFS and MrJob python library (8h).
    • Part 3. Spark framework, using pyspsark python library (4h).

  • Assessment

    • Lab report (Part 2): 20% of the final grade.
    • LOD project (Part 1): 40% of the final grade: 20% for the report and 20% for the project defense.
    • Mid-term exam (Chapter 1 and Chapter 2) will account for 20% of the final grade.

  • Detailled content here

Chapter 3. Cloud computing and AWS certifications

  • Lecturer Yann Fornier (guest lecturer).

  • Learning objectives To assist in obtaining AWS certifications.

  • Time allocations 20h.

  • Type of teaching in-person and remote.

  • Assessment: AWS Cloud Practitioner & AWS AI Practitioner certifications.