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ENGAGE Shared Modules - 2022-2023

Students from ENGAGE.EU partner universities can take modules offered by Toulouse Capitole

 University Toulouse Capitole will offer two ENGAGE.EU Modules as of fall semester 2022. They will consist of three courses during the fall semester 2022 and the spring semester 2023, bearing 15 ECTS each. UT1 offers a module on Smart Cities and Data in modern Era.

The Shared Modules catalogue course from our partners are available here.
Registrations are open from 1st June to 1st July 2022.

Application process

Who can apply?

The online course offer is available to students studying at the following partner universities:
  • University Toulouse Capitole
  • LUISS University
  • NHH Norwegian School of Economics
  • Tilburg University
  • University of Mannheim
  • University of National and World Economy
  • WU Vienna

Application period - Until when can I apply

From June 1st to July 1st 2022.

How can I apply?

Registrations are open from June 1st to July 1st. The selection will be based on an evaluation of your overall academic performance (transcript of records) and the statement of motivation. You will find the form to register at the end of the course catalog.

Transcript of records

At the end of your virtual exchange, you will receive an official transcipt of records from Toulouse 1 Capitole University. For credit transfer options, please check with your home institution.

Smart Cities

Application period : June 1st - July 1st
Course period: Year 2022-2023
ECTS: 15
Module Coordinator : Grégory Kalflèche

 

 
Description:
The module has been designed for 1st and 2nd year Master students.
The Smart Cities module is a multidisciplinary group of courses whose aim is to perceive the future of cities through a scientific, legal, economic and societal approach.
A "smart city" is a set of interdependent urban evolutions aiming at making the city more resource efficient and more pleasant to live in thanks to new technologies. Firstly, it is an evolution of public services and an opening of their data (energy consumption, transportation, cameras...) which allows an optimization of these services by their interconnection (smart grid). Secondly, there is a desire to develop resource optimization through "big data" processing of this data, combined with energy saving measures, sustainable construction, autonomous buildings and nature in the city.
The module will end with a “challenge week” in Toulouse with a practical case proposed by external partners like Toulouse Municipality.

Smart cities and law : between urban planning law, public services and data

ECTS: 6
Semester: Fall
Language of instruction
: English
Mode of delivery
: Blended : Prerecorded videos and Interactions in class / debate.
Prerequisite
: Some basis on law
Learning outcomes
: The objective is to explain firstly what is a smart city, then to understand the implication of law and the necessity to know the rules which could be applicated.
Assessment
: Oral exam.
Description
: After having understood what is a smart city, the student will learn the implication of law in this process. It is necessary to know several rules which could be applicated to the smart cities, like rules about public services, urban planning law, and legislation about data.

Data Ethic

ECTS: 5
Semester: Fall
Language of instruction
: English
Mode of delivery
: Blended : Asynchronous videos + online discussion and practical work
Prerequisite
: None
Description
: This course is divided into 2 parts of 6 hours each at Master 1 and Master 2 level.
In the first part, the pedagogical objective is to make students aware of the societal stakes of data and its economic implication In the 2nd one, the objective is complementary by deepening the reality of digital colonisation, which includes surveillance capitalism and the emerging ethical issues of AI
Part 1 : Data, a social issue (6 hours)
Introductory chapter
Chapter 1: Nature of data
- The scope of digital data
- The economic specificity of data
Chapter 2: Economic characteristics of digital platforms and the challenges of data
- Digital platforms: a recent phenomenon driven by data
- The challenges of regulating platforms and their corollary: data
Part 2: Data, Gafam and digital colonisation (6h)
Chapter 1: The levers of surveillance capitalism: the case of GAFAM
Chapter 2: Data ethics: The case of Artificial Intelligence (AI)

Challenge week with Toulouse Municipality

ECTS: 4
Semester:  November 21-25
Language of instruction
: English
Mode of delivery
: Face to face – One week in Toulouse
Prerequisite
: None
Learning outcomes
: The aim is to work on a practical case provided by Toulouse Municipality or another professional partner.
Assessment
: Participation during the week. Quality of the final presentation
Description
: The content will depend on the case provided by the professional partner. The aim is to put into practice the lessons learned. 3 steps are planned during the week : Ideation process, Prototype and storystelling (pitching the information) There will be a contact-person available throughout the week for students. The challenge-owner will also make data available for students and offer the opportunity to interview employees, clients, costumers and other stakeholders for the benefit of the ideation process. Training session about innovative methodology like Design thinking will be provided.

Data in modern era

Application period : June 1st - July 1st
Course period: Year 2022-2023
ECTS: 15
Module Coordinator : Marion Larouer
Description:
The module has been designed for 1st and 2nd year Master students.
Faced with the digitalization of society, the module aims to provide different points of view regarding the role of data.
Data are at the same time
  • a tool for IT
  • an object to be used in conformity with fundamental rights and
  • an object to be protected.
Computer science, communication and law are all essential disciplines to understand the stakes of data in our data society.

NB : 4 courses are offered in this module but the sum of the 3 selected courses must total 15.
Students must therefore choose between the following 2 courses: Statistical Softwares for Data scientists Data Bases

Data protection regulation

ECTS: 4
Semester: Spring
Language of instruction
: English
Mode of delivery
: Blended : Prerecorded videos and Interactions in class / debate.
Prerequisites
: Union european Law, basic legal skills
Learning outcomes
: Students are able to : Understand the reasons of Data protection Understand the GDPR and its main provisions.
Assessment
: Oral exam.
Description
: General Data Protection Regulation (GDPR) The main principles of personnal data protection (concept of privacy ; Internal and european texts) Rights and obligations under the legal framework applicable in France : The scope of the legal framework ; The rights of the data subject ; Obligations The actors in charge of the application of the law: the control authorities (CNIL) : Présentation, main mission.

Data Ethic

ECTS: 5
Semester: Fall
Language of instruction
: English
Mode of delivery
: Blended : Asynchronous videos + online discussion and practical work
Prerequisites
: None
Description
: This course is divided into 2 parts of 6 hours each at Master 1 and Master 2 level.
In the first part, the pedagogical objective is to make students aware of the societal stakes of data and its economic implication In the 2nd one, the objective is complementary by deepening the reality of digital colonisation, which includes surveillance capitalism and the emerging ethical issues of AI
Part 1 : Data, a social issue (6 hours)
Introductory chapter
Chapter 1: Nature of data
- The scope of digital data
- The economic specificity of data
Chapter 2: Economic characteristics of digital platforms and the challenges of data
- Digital platforms: a recent phenomenon driven by data
- The challenges of regulating platforms and their corollary: data
Part 2: Data, Gafam and digital colonisation (6h)
Chapter 1: The levers of surveillance capitalism: the case of GAFAM
Chapter 2: Data ethics: The case of Artificial Intelligence (AI)

Statistical Softwares for data scientits (option1)

ECTS: 6
Semester: Fall

Language of instruction
: English
Mode of delivery
: Online
Learning Activities
:Practical tutorial: 36 hours for R and Python (3h every week). 5 first tutorials are dedicated to practical introduction to R, programming in R and data importation, manipulation and graphical visualisation. The 6th tutorial is dedicated to the evaluation related to the R part. The next 5 tutorials are dedicated to practical introduction to thePython language for programming, data manipulation and web scrapping. The last tutorial (the 12th one) is dedicated to the Python evaluation. Personal computer allowed.
Prerequisites
: Basis of descriptive statistics.
Required/recommended reading, learning resources or tools
:There is no compulsory textbooks. Below are some references we would recommend:
-An Introduction to R http://cran.r-project.org/doc/manuals/R-intro.html
-R pour les débutants - Emmanuel Paradis (https://cran.r-project.org/doc/contrib/Paradis-rdebuts_fr.pdf)
-Python in a nutshell Alex Martelli, O'Reilly Media, 2017.
Learning outcomes:
The objective is to give students in economics bases in three reference softwares and programming language necessary for data scientists: R, Python and SAS. Through practical sessions, students should be able to manipulate datasets (import, clean, compute indicators, and visualise them).
Assessment
: R and Python: Midterm evaluation (50%) and final evaluation (50%). Each evaluation will be a practical evaluation on computer.
Description
: The objective is to give students in economics bases in three reference softwares and programming language necessary for data scientists: R, Python and SAS. Through practical sessions, students should be able to manipulate datasets (import, clean, compute indicators, and visualise them).

Data Bases (option2)

ECTS: 6
Semester: Spring
Language of instruction
: English
Mode of delivery
: Blended : Asynchronous videos + online discussion and practical work
Learning Activities
:Courses will use videos and sessions will be dedicated to discussions and practical work. Some sessions may be distance learning using virtual classrooms. Frequent multiple choice question tests will be available in order to ease course knowledge retention. Objects is only available on the university computers and there is no student licence. Students will have a project that will require, among other things, using SAP Business Objects.
Prerequisite
: Knowledge of how to use a computer and managing computer files. Knowledge in using a spreadsheet tool may help (such as Open Office/Libre Office Calc or Microsoft Excel).
Required/recommended reading, learning resources or tools
:Kimball, Ralph; Margy Ross (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (3rd ed.). Wiley. Note that the French version of this book that dates from 2007 is not recommended as its content is outdated (it corresponds to the 1st edition of the book). Laptops may be used in class with Microsoft Office Access (2010 or later). .
Learning outcomes
: Skills developed will be: expressing data requirements in terms of data query language (using the database query language SQL); expressing analysis requirements in terms of multidimensional database schemas; and designing relevant data presentation reports or dashboards.
Assessment
: Several positioning MCQ (multiple choice question) tests will be provided on the Moodle platform. Their usage and progression in the answers which is the goal of these positioning tests, will be monitored and taken into account in the two following grades:
- A project done in pairs (40% of the final grade) and in two parts will be handed in during the semester.
- A final exam (60% of the final grade) that may be done on-line.
Description
: The objective of this course is to earn a basic knowledge on Decision Support Systems (data analysis) using database systems. The course presents an overview of the possible computer software architectures (interconnection of different software and data sources) that can be used for data analysis, focussing on querying data sources and designing multidimensional databases to be used with typical On-Line Analytical Processing tools (called OLAP tools or Business Intelligence tools). Methods taught will concern understanding analysis requirements by elaborating a multidimensional database and relevant data presentations.
 

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