Big Data & Artificial Intelligence
Ref. 186G002
With this GRADEO, master data and explore machine learning and deep learning, thanks to 3 theory courses and a certification course delivered by ESTIA and Oracle.
- Duration: 17 weeks
- Effort: 85 hours
- Pace: ~5 hours/week
- Languages: English and french
What you will learn
At the end of this course, you will be able to:
- Master big data architecture with expected SQL extensions to master NoSQL and NewSQL systems.
- Understand the expected functionalities of the next GQL (Graph Query Language) standard.
- Master the fundamentals of machine learning and deep learning.
Description
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, NewSQL) as well as the concepts of datawarehouse and datalake.
- The analysis of data with computer methods (data mining and OLAP), statistics (machine learning in supervised or unsupervised mode) or based on artificial intelligence with the fundamental approach of deep learning (multi-layer neural networks).
Format
This GRADEO is made up of 4 courses:
- 3 ESTIA academic courses: SQL Relational & Object Programming, Distributed Big Data Management and Artificial Intelligence (Machine Learning & Deep Learning).
- 1 Oracle professional course: Machine Learning on Oracle Cloud
The Oracle Professional Course is to be taken on the Oracle platform.
To ensure the efficient implementation of this GRADEO, you will be asked to fill in a form after your registration to enable Datum Academy, technical partner of FUN and ESTIA, to provide you with an activation key to access your Oracle course. Once you have the activation key, all you need to do is create your Oracle account to take the course!
The ESTIA academic courses lasts an estimated 17 weeks, with one module per week.
Learning the Oracle professional course takes an estimated 5 weeks. It should be noted that learners of this program will be issued with an activation key giving them access to the course and exam for a period of 6 months.
Prerequisites
Learners should have a good grasp of basic mathematics (linear algebra, graph theory) and computer science: SQL (potentially GRADEO on SQL programming) and Python (bachelor 2-3 years level).
Assessment and certification
The learner will take an exam at the end of each course. After passing these exams, they will receive a GRADEO certificate issued by ESTIA (corresponding to 6 ECTS), as well as the Oracle certification "Oracle Machine Learning Using Autonomous Database 2021 Specialist (1Z0-1096-21)".
The ESTIA certificate of success will be delivered by email to the learner. The Oracle professional certificate can be downloaded directly to the Oracle platform upon successful completion of the corresponding exam.
Course plan
- Introduction
- Data paradigms
- Codd's relational data model
- Conclusion
- Exercise : Codd's algebra
- Exercise : Codd's relational data model
- Self-assessment quiz
- Introduction
- Introduction to Codd & Date method for relational schema design
- Datawarehouse
- Database storage & access
- Exercice : Theorem of Casey & Delobel
- Exercise : Jim Gray's cube
- Exercise : Dynamic hashing & B=Tree
- Introduction
- Schema definition with SQL2
- DB manipulation with SQL
- Exercise : Codd's algebra & SQL2
- DB control with SQL
- Conclusion
- Exercise : Car rental agency
- Exercise : SQL2
- Self-assessment quiz
- Introduction
- Object paradigm & databases
- Object-oriented data model
- Object relational data model
- D language & Thesaurus
- Conclusion
- Second DB manifesto & illusta/postgres by Mike Stonebraker
- Object-middleware approach & universal access with DCOM
- Covid relational schema
- Exercise : Date's schema
- Self-assessment quiz
- Introduciton
- OQL
- Thesaurus
- Exercise : Covid relational schema
- Exercise : ODMG schema
- Introduction
- Major object features within SQL3
- Critics of the double paradigm
- Exercise : SQL3 & ODMG
- Conclusion
- Conclusion
- Exercise : SQL3 schema
- Self-assessment quiz
- Introduction
- Data
- NFC & mobiquitous systems
- Big Data
- Computer science evolution
- Data future
- Conclusion
- Seminars
- Neuralnets
- Blockchain
- Big bridge
- Self-assessment quiz
- Introduction
- Hadoop ecosystem for the 1st "V" of Big Data
- Document
- Graph-oriented
- New SQL
- Towards Big SQL & interactive real-time analytics
- Self-assessment quiz
- Introduction
- Data lake : polystores vs multi-model data stores
- Mathematical bridge over SQL, NoSQL & NewSQL
- Exercise : Matrix multiplication in SQL
- Associative arrays concept
- Examples
- Category concepts
- Functor
- Exercise : Category theory
- Cartesian products & matrix
- Conclusion
- Seminar graph theory, property graphs & data paradigms
- GQL introduction
- Where is the target data model ?
- Graph queries
- SQL extensions for property
- Graph analytics
- Best practices
- Self-assessment quiz
- Introduction
- Cluster computing
- Map/Reduce paradigm presentation
- Word count - logs analysis
- Social network - common friends
- Breadth first search
- Breadth first search - execution
- Conclusion
- HADOOP general presentation
- HADOOP HDFS presentation
- HADOOP HDFS usage
- HADOOP YARN
- Self-assessment quiz
- Introduction
- HADOOP presentation
- DRIVER class
- MAPPER class
- REDUCER class
- HDFS API
- Custom configuration properties, counters
- Examples
- Development environment
- Execution
- Conclusion
- Practical exercises setup
- Virtual machine
- Practical exercises
- Self-assessment quiz
- Introduction
- INPUTFORMAT classes
- Custom INPUTFORMAT classes
- OUTPUTFORMAT classes
- Custom writable types
- APACHE SPARK presentation
- APACHE SPARK architecture
- APACHE SPARK API - CORE - Base
- APACHE SPARK API - CORE - Transformation
- APACHE SPARK API - CORE - Actions
- APACHE SPARK API - CORE - Broadcast variables & accumulators
- APACHE SPARK API - CORE - Examples
- Conclusion
- Self-assessment quiz
- Vectors
- Matrices
- Linear equations
- Matrices & linear maps
- Matrix factorization
- Free optimization
- Self-assessment quiz
- Big picture
- Biological & artificial learning
- Learning environment
- Supervised learning
- Unsupervised learning
- Learning protocols
- Bayesian decision
- Self-assessment quiz
- Introduction to linear prediction
- Normal equations
- Ridge regression
- Linear threshold machines
- Gradient-based learning
- Loss & risk functions
- Perceptron Rosenblatt's algorithm
- Gaining linear separability in the feature space
- Self-assessment quiz
- Introduction
- Introduction to neural networks
- Neural networks
- Learning with neural networks
- Details of backpropagation
- Learning from data
- Deep architectures
- Learning in deep architectures
- Selecting the optimal architecture
- Self-assessment quiz
- Computer vision, natural language
- Computer vision, image classification & neural networks
- Convolution & images
- Convolutional neural networks
- Image classification with convolutional neural networks
- Natural language modeling & neural networks
- Recurrent neural networks
- Language modeling with recurrent neural networks
- Long-short term memories
- Self-assessment quiz
- Software packages for Machine Learning
- Machine Learning software : libraries & main tools
- Exercise : Websites
- Introduction to Tensorflow
- Exercise : Tensorflow installation
- Tensors, operations, graphs
- Exercise : Linear combination
- Executing operations
- Exercise : Matrix-by-matrix product
- Variables & INPUT data
- Exercise : Interactive process
- Setting up a classification problem
- Exercise : IRIS Data
- Implementing a neural network
- Exercise : Mean squared error
- Training a neural network
- Exercise : Learning rate
- Introduction
- Practice
- Retrieving data using the SQL SELECT statement
- Practice
- Restricting & sorting data
- Practice
- Using single-row functions to customize output
- Number functions
- Practice
- Using conversion functions & conditional expressions
- General functions
- Practice
- Reporting aggregated data using the group functions
- Practice
- Displaying data from multiple tables using joins
- Practice
- Using subqueries to solve queries
- Practice
- Using SET operators
- Practice
- Managing tables using DML statements in Oracle
- State of data
- Practice
- Introduction to date definition language in Oracle
- FOREIGN KEY constraint
- Practice
- Introduction to data dictionnary views
- Practice
- Creating sequences, synonyms, & indexes
- Sequence information
- Practice
- Creating views
- Practice
- Introduction
- Practice
- Introduction to PL/SQL
- Practice
- Declaring PL/SQL variables
- The %TYPE attribute
- Practice
- Writing executable statements
- Practice
- Using SQL statements within a PL/SQL block
- Practice
- Writing control structures
- Basic loop : example
- Practice
- Working with composite data types
- Associative arrays
- Practice
- Using explicit cursors
- Cursor FOR loops
- Practice
- Handling exceptions
- Practice
- Introducing stored procedures & functions
- Practice
- Creating procedures
- Practice
- Creating functions
- Practice
- Creating packages
- Practice
- Creating triggers
- Practice
- Introduction to OML
- Oracle Machine Learning features
- Introduction to Machine Learning for Python
- Creating workspaces & projects for use with OML noteboooks & AutoML UI
- Users in workspaces in Oracle Machine Learning
- Creating SQL scripts in Oracle Machine Learning
- Restrictions on SQL commands
- An introduction to Oracle AutoML
- AutoML with OML4Py
- OML AutoML user interface (UI)
- Notebooks
- Forms in notebooks
- Versioning in notebooks
- Templates
- Creating notebook from templates
- Working with jobs
- Administering
- Connection groups
- Introduction to Machine Learning for Python
- Why Machine Learning & usecases
- Machine Learning workflow & types of ML algorithms
- Introduction to Oracle Machine Learning for Python & features
- OML4Py features
- Oracle Machine Learning for Python advantages
- OML notebooks
- Python libraries in OML4Py
- Practice
- OML4Py transparency layer
- Combine data
- Clean & split data
- Data exploration
- Practice
- Working with Machine Learning models
- Common in database algorithm features
- Working with Machine Learning models
- Create a model proxy object from an existing model, export & import a model
- Practice
- Data store for Python objects
- Save objects & load saved objects from a data store
- Get information from a data store
- Get information & delete data store object
- Manage access to stored objects
- Practice
- OML4Py automated Machine Learning
- Machine Learning workflow automated by AutoMl
- Algorithm selection
- Feature selection
- Model tuning
- Model selection
- Practice
- Introduction to embedded Python execution
- Run a Python function
- Run a Python function on the specified data
- Run a Python function on datagrouped by column values
- Introduction to script repository overview
- Load & drop script from repository
- Introduction to REST API
- Practice
- Working with cx_Oracle
- Oracle architecture
- Readwrite table methods
- Work on your final project
Course team
Thomas Frisendal
Categories
Graphic data architect, visual data modeler, online trainer and national member of the ISO SQL/GQL committee - Teaches only the English course runs
Marco Gori
Categories
Professor of Computer Science, University of Siena, Department of Information Engineering and Mathematics.
Stefano Melacci
Categories
Assistant Professor of Computer Science, University of Siena, Department of Information Engineering and Mathematics.
Organizations
In partnership with
Courses included in this GRADEO
SQL Relational & Object Programming
Distributed Big Data Management
Artificial Intelligence (Machine-Learning & Deep Learning)
Machine Learning on Oracle Cloud
License
License for the course content
Attribution-NonCommercial-NoDerivatives
You are free to:
- Share — copy and redistribute the material in any medium or format
Under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- NonCommercial — You may not use the material for commercial purposes.
- NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.
License for the content created by course participants
All rights reserved
"All rights reserved" is a copyright formality indicating that the copyright holder reserves, or holds for its own use, all the rights provided by copyright law.