Data Engineer

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Data

Applied MSc in Data Engineering for Artificial Intelligence

The Data Engineer expert is one of the most important job in the Big Data Industry

This 6 months of classes and 6-month internship Applied MSc1 programme, with its two entries in Autumn and Spring, 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.

Data engineering experts are rare and in greater demand than data scientists.

Data is the energy that powers the digital transformation. The developers consume it in their applications. Data Analysts search, query and share it. Data Scientists feed their algorithms with it. Data Engineers are responsible for setting up the value chain that includes the collection, cleaning, enrichment and provision of data. Some of the Data Engineers’ missions are to manage scalability, ensure data security and integrity, be fault-tolerant, manipulate batch or streaming data, validate schemas, publish APIs, select formats, models and databases appropriated to their exhibitions. From this work, flow the trust and success of those who consume and exploit the data.
And while the Harvard Business Review may have declared: Data Scientist: The Sexiest Job of the 21st Century, it is the data engineering team that allows them to shine.

The salaries

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

Study modes

On-Campus

Choose to study either on the campus of Nice Sophia-Antipolis or Paris. You will follow 6 months of courses (around 5hrs per day), followed by 6 months of internship.

Off-Campus - full-time

Designed for those who want to study full time. Study online for a period of 6 months, followed by 6 months of internship.

SPOC – at your own pace

Designed for working professionals who wish to acquire new skills. Study online and at your own pace, for a flexible period up to 36 months.

Objectives

  • IT & Big Data architectures

    Learn how to understand the analysis, design, implementation & monitoring of IT & Big Data architectures

  • DevOps

    Discover the DevOps world and set up continuous integration architecture.

  • Machine and Deep Learning

    Leverage the most prevalent programming languages and their libraries for applied machine and deep learning

  • Hadoop or SPARK

    Learn how to architect and deploy highly distributed data and computation clusters such as Hadoop or SPARK

Programme Structure

700-hour programme – 60 ECTS
850-hour internship (6 months) – The internship is equivalent to 30 ECTS

This Applied MSc programme programme is composed of all the following modules*, which are actual hours of class presence (personal work is expected on top of these):

  • Distributed & Performance IT
    10 ECTS
  • Data management
    8 ECTS
  • Operational Methodologies
    4-6 ECTS
  • Data Science
    4-8 ECTS

Cloud-Computing – Amazon AWS
3 ECTS

(50 hrs)

Preparation to AWS Certified Solutions Architect – Associate Certification

Java & Scala programming
1 ECTS

(25 hrs)

Java for Map Reduce in Hadoop & Scala for SPARK

Software Engineering Part II
1 ECTS

(25 hrs)

Fundamentals of algorithmics & data structures using object-oriented programming – Applications in C++ & Python

Software Engineering Part I
1 ECTS

(25 hrs)

Review of programming and memory management fundamentals, introduction to Microsoft .NET environment – Applications in C & C#

Cloud Computing – Microsoft Azure
2 ECTS

(25 hrs)

Comparative overview of with Amazon AWS – Focus on Azure Services specific to data lakes and data pipelines

Python Machine Learning Labs
1 ECTS

(25 hrs)

Data structures – Cleaning & preparation – Pandas – Matplotlib – Scikit-learn – OpenCV – Python & Flask – Keras – Numpy

Semantic Web technologies for Data Science developments
1 ECTS

(25 hrs)

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
1 ECTS

(25 hrs)

Fundamentals of Relational Model & Databases - Relational Algebra – Advanced SQL queries - Stored procedures & triggers (T-SQL), Dynamic SQL – Applications with Microsoft SQL Server

Datawarehousing & ETL
1 ECTS

(25 hrs)

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
2 ECTS

(50 hrs)

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
2 ECTS

(10 hrs)

Fundamentals of MongoDB Databases, Collection and Document – Advanced MongoDB queries, MongoDB aggregations, MongoDB data architecture – Applications with MongoDB and Robo3T

The Hadoop & Spark Ecosystem
2 ECTS

(50 hrs)

HDFS – Scheduling & resources management – Workflow management & ETL – Dataflow management - Scalable Enterprise Serial Bus – Realtime processing with SPARK – Machine Learning – Data Exploration & Visualisation

Graph Databases – NoSQL
– Part 1
1 ECTS

(25 hrs)

Preparation to Neo4j Certification – Modelling a graph-based problem, implementation with the Neo4j database

IT Project Management – PMP-PMI and Agile Approaches
1 ECTS

(25 hrs)

PMBOK (PMI) – Agile Approaches – Kanban – Quality Metrics

CRM Data Management*
2 ECTS

(25 hrs)

Preparation of certification "Microsoft Power Platform Functional Consultant (PL-200)"

DevOps & Continuous Integration
3 ECTS

(50 hrs)

The DevOps toolbox: Nagios, Consul, Docker, Ansible, GitHub – Continuous Integration with Jenkins & Kubernetes

Data Laws & Regulations – Philosophies, Geopolitics & Ethics
1 ECTS

(25 hrs)

EU & USA approaches – GDPR – Safe Harbour & Successors – Common Law vs Code Law

Cybersecurity
2 ECTS

(25 hrs)

System Security Design Patterns – Infrastructure security – Data at-rest and in-transit encryption – Code safety

* The student chooses between “CRM Data Management” or “Artificial Neural Networks”.

Applied Mathematics for Data Science
1 ECTS

(25 hrs)

Calculus – Linear Algebra – Trigonometry & Complex Numbers

Big Data Processing with R
2 ECTS

(25 hrs)

Import and manipulate very large datasets with R – Best data structures selection – Data Transformation – Visualisation – Exploring and modelling

Artificial Neural Networks*
2 ECTS

(25 hrs)

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
2 ECTS

(25 hrs)

Probabilities and distribution – Descriptive Statistics – Visualisation – Applications using R

Deep Learning
2 ECTS

(25 hrs)

Recurrent Neural Networks, LSTM, Residual Networks, Computer Vision & NLP – Deep Learning on GPU – Application using Python & PyTorch

* The student chooses between “CRM Data Management” or “Artificial Neural Networks”.

* 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.

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.

Careers Perspectives

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.

Tuition Fees

Early Bird Discount

Waiver of 10% of the total tuition fee: Following the admission to the full-time study mode, candidates must pay the matriculation deposit before June 15th 2021 to qualify for the waiver.

Apply Now
  • French Students
  • International Students
15,000

On-Campus

Choose to study on the campus of Sophia-Antipolis or Paris.
  • Full-time
  • Nice Sophia-Antipolis & Paris Campus
13,500

Off-Campus France

Study From home or anywhere
  • Full-time
  • Live classes & synchronous
15,000

SPOC

Study from home or anywhere at your own pace
  • At your own pace & asynchronous
  • Personalised learning path
15,000

On-Campus

Choose to study on the campus of Sophia-Antipolis or Paris.
  • Full-time
  • Nice Sophia-Antipolis & Paris Campus
11,250

Off-Campus International

Study From Home or anywhere
  • Full-time
  • Live classes & synchronous
15,000

SPOC

Study from Home or anywhere at your own pace
  • At your own pace & asynchronous
  • Personalised learning path