BIG DATA & AI ARCHITECT
A double certification program in partnership with UNISI and Oracle that allows you to master the theoretical and practical aspects of AI and Big Data with practical cases on current issues.Apply Now
The AI and Big Data architect must master 2 environments at the heart of this GRADEO :
- The management of DATA, both structured with pre-definition of a fixed schema (SQL3, OQL), semi-structured with meta data (SparQL) and unstructured (NOSQL, New SQL) as well as the concepts of DATA WAREHOUSE and DATA LAKE.
- The analysis of DATA with computer methods (Data Mining and OLAP), statistics (Machine learning in supervised or unsupervised mode) or based on AI with the fundamental approach of Deep Learning (multi-layer neural networks)
Job role: this GRADEO prepares you to become a Big Data architect.
This GRADEO includes 3 university courses taught by teachers from ESTIA engineering school and 1 industrial course in partnership with Oracle University. An academic course is generally divided into 6/7 modules, each one being delivered over a period of one week and 6/7 consecutive weeks. Each module represents 5 hours of student work per week.
- Data and Codd Relational Model for structured databases
- SQL2 for Relational Databases
- Chris Date and Mike Stonebraker's Manifestations on the object relational model
- OQL for object databases
- SQL3 for hybrid (object-relational) databases
- Data and NO SQL paradigms for unstructured databases (Datalakes and polystores)
- Mathematical Tools
- Basics of Machine Learning
- Learning with Shallow Architectures
- Learning with Deep Architectures
- Computer Vision, Natural Language
- Software Packages for Machine Learning
- Strategic vision on big data economy around technical disruptions
- N.O. SQL and NEW SQL
- Category theory
- Graph query languages
- The map/reduce paradigm Hadoop presentation
- Advanced Hadoop development Apache Spark
- Program with Oracle SQL and PL/SQL
- Create procedures, functions, packages, and triggers using PL/SQL
- Describe the components and feature of Oracle Machine Learning (OML)
- Use OML features with Oracle Autonomous Database
- Identify Oracle Cloud Services that are compatible with OML
- Create projects, workspaces, SQL scripts, job schedules, templates, and notebooks in OML
- Describe OML use cases
Learners must master basic mathematics (linear algebra, graph theory) and basic computer science: SQL (potentially GRADEO on SQL programming) and PYTHON (undergraduate level).
Open to any computer scientist for continuous education (no academic prerequisites)
6-month accelerated program (or more if needed for the academic courses)
Providing double certification: an academic certificate from ESTIA corresponding to 6 ECTS & a professional certification from Oracle
Preparing for jobs
If you are an organization wishing to register a group of employees for this training, we invite you to contact us in order to propose a partnership including benefits.