FORMATIONS |
Fiche détaillée d'un cours
BIG DATA TOOLS - PART 2 | |||
2023-2024 | FrIESEG School of Management
(
IÉSEG
)
| ||
Code Cours : | 2324-IÉSEG-MBD1S2-MIS-MBDCE07UE | MANAGEMENT OF INFORMATION SYSTEMS |
Niveau | Année de formation | Période | Langue d'enseignement |
---|---|---|---|
MSc in Big Data Analytics for Business | 1 | S2 | FrEnglish |
Professeur(s) responsable(s) | S.HOORNAERT |
---|---|
Intervenant(s) | S.HOORNAERT |
- Ce cours apparaît dans les formations suivantes :
- IÉSEG > MSc in Big Data Analytics for Business > Semester 1 > 3,00 ECTS
Pré requis
- Participants should be familiar with the basic concepts of R (e.g., vectors, dataframes, functions, packages).
- Participants should be familiar with reading and writing SQL queries (e.g., select, group by, having).
- Participants should know the basic concepts of Business Analytics and Predictive Modeling.
Objectifs du cours
At the end of the course, the student should be able to:
- understand the available technologies in the Big Data universe and use the correct technology for a given Big Data problem
- know the technologies for reading and writing Big Data (e.g., MapReduce, Hadoop, HDFS, Parquet)
- know Spark, its architecture and its APIs
- use Spark as a tool for descriptive and predictive analytics using Spark SQL, MLlib, Streaming, and GraphX
- solve and present an end-to-end solution to a Big Data problem in an intercultural team
These competencies and/or skills contribute to the following learning objectives
- 1.B Successfully collaborate within a intercultural team
- 3.A Breakdown complex organizational problems using the appropriate methodology
- 4.B Compose constructive personal feedback and guidance
- 4.C. Convey powerful messages using contemporary presentation techniques
- 5.B Construct expert knowledge from cutting-edge information
- 7.A Demonstrate an expertise on key concepts, techniques and trends in their professional field
Contenu du cours
Every day, 2.5 quintillion bytes (=2.5*10^18 bytes) of data are created. Every minute, more than 4.2 million posts are liked and 300 hours of videos are uploaded. This generated (Big) data is characterized by its volume, variety, velocity, and veracity, and requires a specific approach for reading, writing, transforming, and modeling. This course introduces the problem of Big Data, the Big Data universe, reading and writing Big Data, and the skills to work with these data. It uses Spark as a core processing engine for running descriptive and predictive analyses on Big Data.
Modalités d'enseignement
Organisation du cours
Type | Nombre d'heures | Remarques | |
---|---|---|---|
Independent study | |||
Individual Project | 6,00 | ||
Group Project | 28,00 | ||
Estimated personal workload | 34,00 | ||
Face to face | |||
Interactive class | 16,00 | ||
lecture | 16,00 | ||
Charge de travail globale de l'étudiant | 100,00 |
Méthodes pédagogiques
- Case study
- Coaching
- Interactive class
- Presentation
- Project work
- Research
Évaluation
The assessment will consist of:
- a group work where students will solve a Big Data case study end-to-end with group feedback
- a set of individual assignments to support learning Spark with individual feedback
- a written exam to test the knowledge of Big Data, the Big Data universe, and Spark.
Type de Contrôle | Durée | Nombre | Pondération |
---|---|---|---|
Final Exam | |||
Written exam | 4,00 | 1 | 50,00 |
Others | |||
Individual Project | 6,00 | 1 | 15,00 |
Group Project | 28,00 | 0 | 35,00 |
TOTAL | 100,00 |
Bibliographie
- Chambers, Bill, and Matei Zaharia. Spark: the definitive guide: big data processing made simple. " O'Reilly Media, Inc.", 2018. -
Ressources internet
* Informations non contractuelles et pouvant être soumises à modification