Applied MSc in Data Engineering for Artificial Intelligence
The Data Engineer expert is one of the most important job in the Big Data Industry
The Applied MSc in Data Engineering for Artificial Intelligence1 programme, with its two entries in autumn and spring, will provide you the essentials to explore the world of Big Data and to build data infrastructures. This programme is designed to open your career to these Big Data Engineering jobs all industries are looking for.
There is an important shortage of big data experts and companies in various sectors suffer from this. Data Engineering specialists are in high demand and enterprises know the real value of their roles. In France-Benelux, we have seen a big increase in the demand for skilled people in data engineering in both start-ups and world leading companies. For every data scientist there is a need for at least two experts in data engineering.
A data engineer role is a highly technical position and requires skills such as programming, mathematics and computer science.
1 Once your studies and professional experience are completed, your achievements will be assessed by our Graduation Committee. If successful, you will be able to obtain the degree “Expert en Sciences des Données” (Experts in Data Science). DSTI is proud of its Applied MSc in Data Analytics, Applied MSc in Data Engineering for AI and Applied MSc in Data Science & AI to have been fully accredited at Master’s level by the French Government via the RNCP mechanism. The RNCP “Répertoire National des Certifications Professionnelles” is a government recognition mechanism dedicated to scrutinising programmes’ suitability for the work market. A RNCP title rewards specific needs in terms of skills and knowledge transfer for immediate employability, which is the heart of DSTI philosophy.
Data engineering experts are rare and in greater demand than data scientists.
In Europe, we report a Mid-Level average salary between €59,000 and €83,000. The average salary for the Director of the Data engineering department varies between €100,000 and €174,000. Salaries depend on experience, skills, and location.
Average trainee remuneration in France: €1,300 per month
830-hours programme – 60 ECTS:
- 765 hours of classes including 75 hours of DSTI Warm Up
- 65 hours of support sessions
850-hour internship (5 to 6 months) – 30 ECTS
The Applied MSc in Data Engineering for AI programme programme is composed of all the following modules*, which are actual hours of class presence (personal work is expected on top of these):
Cloud-Computing – Amazon AWS
Preparation to AWS Certified Solutions Architect – Associate Certification
Object Oriented Programming , Software Architecture through UML and base of Java & Scala development
Software Engineering Part II
Fundamentals of algorithmics & data structures using object-oriented programming – Applications in C++ & Python
Software Engineering Part I
Object-Oriented Design, Design Patterns with Java programming
Cloud Computing – Microsoft Azure
Comparative overview of with Amazon AWS – Focus on Azure Services specific to data lakes and data pipelines
Python Machine Learning Labs
Data structures – Cleaning & preparation – Pandas – Matplotlib – Scikit-learn – OpenCV – Python & Flask – Keras – Numpy
Semantic Web technologies for Data Science developments
Representing and querying web-rich data (RDF, SPARQL) – Introducing Semantics in Data (RDFS, Ontologies) – Tracing and following data history (VOiD, DCAT, PROV-O)
Data Wrangling with SQL
Fundamentals of Relational Model & Databases - Relational Algebra – Advanced SQL queries - Stored procedures & triggers (T-SQL), Dynamic SQL – Applications with Microsoft SQL Server
Datawarehousing & ETL
Using Microsoft SQL Server: stand-alone and cluster deployments, design and implementation of a datawarehouse - Structing an Extract, Transform, Load process – Applications with Microsoft SQL Server
Data Pipeline Part I & II
XML dataflow, DTD & Schemas, XLS Transformation, JSON & Transformations – Cloud-based solutions with Glue in AWS & AWS Kinesis – Open-source solutions with Apache Kafka & Beam
Document Databases – NoSQL
– Part 2
Fundamentals of MongoDB Databases, Collection and Document – Advanced MongoDB queries, MongoDB aggregations, MongoDB data architecture – Applications with MongoDB and Robo3T
The Hadoop & Spark Ecosystem
HDFS – Scheduling & resources management – Workflow management & ETL – Dataflow management - Scalable Enterprise Serial Bus – Realtime processing with SPARK – Machine Learning – Data Exploration & Visualisation
IT Project Management – PMP-PMI and Agile Approaches
PMBOK (PMI) – Agile Approaches – Kanban – Quality Metrics
DevOps by Adaltas
The DevOps toolbox: Nagios, Consul, Docker, Ansible, GitHub – Continuous Integration with Jenkins & Kubernetes
Data Laws & Regulations – Philosophies, Geopolitics & Ethics
EU & USA approaches – GDPR – Safe Harbour & Successors – Common Law vs Code Law
System Security Design Patterns – Infrastructure security – Data at-rest and in-transit encryption – Code safety
Applied mathematics & data structures
- Mathematic fundamentals review, data structures for algorithmics on Python Modeling elements
(all pre-calculus level)
- Data Structures for algorithmics: a benchmark on Python & R
- Design structures for fitting well known libraries (glmnet, xgboost, sci-kit learn)
Introduction to networks
- Fundamentals of packet networking, routing networks layers, protocols, address spaces and associated service with TCP/IP
Introduction to IT systems
- Fundamentals of Computer Architecture & Operating Systems
Introduction to computer science
- Fundamentals of programming
- Introduction to OS architecture
- Introduction to DOS/Power shell command
- Introduction to bash command /scripting
- Use case on a Linux web server
Cambridge English classes
APPRENTICESHIP STUDENTS ONLY
English language fundamentals: vocabulary (general and specialised), grammar, conjugation,
and syntax, both oral and written comprehensions.
Evaluation: Linguaskill General Certification Exam assessing all four language skills - speaking, writing, reading and listening.
Applied Mathematics for Data Science
Calculus – Linear Algebra – Trigonometry & Complex Numbers
Big Data Processing with R
Import and manipulate very large datasets with R – Best data structures selection – Data Transformation – Visualisation – Exploring and modelling
Artificial Neural Networks*
Perceptron’s layers, weights, biases – Hyperparameter – Activation and cost functions – Review of optimization algorithms – Backpropagation – Learning mechanism – Classification & regression – Applications in Python using TensorFlow
Foundations of Statistical Analysis & Machine Learning Part I
Descriptive Statistics - Probability Theory – Applications using R
Recurrent Neural Networks, LSTM, Residual Networks, Computer Vision & NLP – Deep Learning on GPU – Application using Python & PyTorch
* Please note that course content and support technologies may vary when delivered according to job market needs and under the supervision of Data ScienceTech Institute Scientific Advisory Board.
** Provided you are not subject to any Sanction Programmes of the United States of America which would affect your rights to take these classes and/or examinations.
Types of evaluation
In order to obtain their degree, students must validate all the assessments required throughout the programme.
The evaluation procedures are as following:
Engineering Projects/Applied Projects
All students will be assigned engineering projects included in the majority of the modules. Students will conduct projects throughout the year until their classes finish and they go to their work placement . These Engineering Projects aim to apply all the knowledge and skills acquired in the different classes and to use DSTI professors as mentors and coaches throughout the year. Some of these projects may come from applied research work done by our Professors affiliated to a research lab.
Resources and tools available for students
Mandatory full validation
Field: Computer Science
Field: Computer Science
Field: Computer Science
IT MINIMUM REQUIREMENTS
DSTI students are required to have a Windows PC (Apple Macs are not an option) laptop with the following minimum specifications:
1.CPU: Intel Core i5 minimum (or AMD equivalent)
2.RAM: 8GB minimum, 16GB strongly recommended
3.Storage: 512GB minimum, 1TB strongly recommended. SSD is best, but expensive. A good and common alternative is a dual-drive system with 128GB/256GB SSD + 512 or 1TB magnetic. If magnetic only, it has to be a 7200rpm minimum, 5400rpm are too slow.
4.Graphic Card (GPU): an NVIDA GPU is a plus, but it’s not a mandatory requirement.
5.Operating System: any edition of Microsoft Windows, as DSTI will provide a Windows 10 Professional licence key once the classes are starting
6.Do not pay for MS Office 365, DSTI will also provide a licence once the classes are starting.
Without complying to these requirements, DSTI will not be able to provide its IT support.
Ability in English required
All classes are conducted exclusively in English, therefore a good level of proficiency in this language is required (equivalent to English level C1 on the European scale “CEFR”).
It is not necessary to include proof of language proficiency with your application. However, your level of English will be assessed at the admission interview to ensure that you are able to understand and follow the desired programmes.
DSTI Warm Up - Be ready!
The Applied MSc programmes include preparatory courses to get you up to speed on skills required to start leveraging the different topics. For the Applied MSc in Data Engineering for Artificial Intelligence, the DSTI Warm Up lasts 3 weeks (75 hours) and can be followed on-campus or online.
Regarding the apprenticeship study mode, all tuition fees are paid by the host company.
Unique fee for all rhythms and modes of study
- Full-time, apprenticeship, asynchronous
- Sophia-Antipolis and Paris campuses (on-campus), synchronous online (off-campus), asynchronous online (SPOC)
- Self-funded student fees are inclusive of VAT
- These fees are valid for corporate & funding bodies, plus 20% VAT (see detailed fees)