Courses of the first year of the Data science curriculum

Entry year (M1)

Semester 1

ACO - Object-oriented analysis and design (code S7INACOU, 5 ects) 

The ACO course details the object-based development techniques and tools currently used in the software industry. The course is composed of four parts: intermediate level knowledge of the UML language, presentation of analysis and design approaches through model engineering, study of the good practices of building software architecture with objects and an introduction to the problems of quality and testing. 

At the end of this lecture, a student will be able to design a simple object-oriented applications from specifications, by implementing an object model engineering analysis and design approach, using UML as a pivot language, building a robust object-oriented architecture that is prepared for the future evolution of the application, and guaranteeing the functioning of the application through the implementation of a set of unit and integration tests. 

BDD - Advanced databases (code, S7IGBDNU, 4 ects) 

OLAP and NoSQL. 

RO - Operations research (code S7IMGROU, 5 ects) 

Operations Research (OR) is a discipline that attempts to apply mathematical methods and the capabilities of modern computers to solve difficult optimization problems arising in various  domains.  This course focuses on Linear programming (LP), which is the best developed and most used branches of OR. In  broad terms, it can be defined as a mathematical representation aimed at programming or planning the best possible utilization of limited resources. When this representation  uses linear functions exclusively, we have a LP model.

The course gives the basics of the simplex method – the main technique for solving the general LP problem, and also introduces the main classes of its application in networks (shortest/longest path in a graph, maximum flow – minimum cut problem,  minimum cost-flow problem).

The course is practical oriented – its main goal is to teach students to find convenient mathematical formulation for real life constraints, to implement these constraints with an appropriate language (PULP in our case), and to call the most suitable solver towards its solution.

Students are evaluated through homeworks, written exam and practical exercise (a project implemented in PULP and using an optimization solver).

BDA - Basics of data analysis (code S7INBDAU, 6 ects) 

This course gives the basics of data analysis in the Python ecosystem. It covers all major steps of data analysis from reading data to visualizing it, going through data transformations and processing, combining and joining data, and extracting useful information from data. It covers the major kinds of data : tables, multi-dimensional arrays with NumPy, tabular data and timeseries with pandas. 

Students are evaluated through homeworks and written exams. 

IEB - Innovation & Entrepreneurship (basics) (code S7ININEU, 5 ects) 

This course is an integrative course on the basics of entrepreneurship and innovation management. The course focuses on the in-depth understanding of the concepts and vocabulary in the areas of innovation processes, strategy and technology-based entrepreneurship, marketing and markets, organization and project management, new product and process development, entrepreneurial finance and human resource development. 

After completion of this module, the students should be able to demonstrate ability to:   

  • Have  an understanding of the general process and roles involved in  developing an idea and starting up a new technology-based company 
  • Systematically explore customers and markets   
  • Have  an understanding and the ability to systematically explore business organization and projects 
  • Have  an understanding and the ability to systematically explore basic product and process development 
  • Have  an understanding and the ability to systematically explore basic entrepreneurial finance   
  • Have  an understanding and ability to link the financial value of a venture with the societal values and the sustainability issues that are raised   
  • Have  an understanding and the ability to systematically explore the  important elements in managing companies and developing its human  resources 

The course is assessed through a final exam. Oral evaluation in the second session. 

BDL1 - Business Development Lab 1 (code S7IDBDLU, 5 ects)

The BDL course follows the main phases of a business development. (a) idea recognition – (b) business modelling – (c) Costumer and technical development – (d) Business Plan presentation (costumer and investor oriented). 

After completion of this module, the students should be able to demonstrate ability to: 

  • Apply the learned knowledge for development of a new product or business concept. 
  • Recognize diverse obstacles in transforming an idea or technology into a business project. 
  • Invent or find solutions to address and solve the project main challenges (customer problem, functionality, business model, development…). 
  • Work in teams and to reflect upon team processes. 
  • Drive  his project according to the dimensions of (1)  customer problem/solution discovery (including in relation to the product technical development) and (2) market discovery (related to  strategic thinking) in ways that are relevant for the situation. 
  • Use  analytical business skills to recognize, assess and/or develop  business opportunities in relation to all dimensions covered in his  project: market, customers, competition, environment and human,  material and technical resources. 
  • Relate  the value proposed in his project to all relevant stakeholders  including producers, customers, shareholders, communities,  ecological systems and policies as appropriate.   

The course is assessed through continual assessment. Oral evaluation in the second session.

 


Semester 2

WS - Semantic Web technologies (code S8IGWSEU, 5 ects) 

This course offers an introduction to the Semantic Web technologies, also called Web of data or knowledge graphs. It makes up a new layer of the Web, on top of the Web of documents and social networks, whose adoption by companies and web search engines is growing rapidly. The course covers the motivations and history of the Semantic Web, its key languages (RDF, RDFS, OWL, SPARQL), and the main existing resources (data, vocabularies, tools). 

The learning objective is that students become able to model an application domain in RDF(S), either from textual documents or from structured data (spreadsheets, databases), and to express SPARQL queries in order to retrieve data. They should know various key resources and tools: the DBpedia knowledge base, the Protégé ontology editor, the YASGUI query editor, and the Sparklis query builder. 

Students are evaluated through homeworks, a small project, and a final exam. 

SBD - Database security (code S8IGSBDU, 5 ects)

"Personal data is the new oil of the internet and the new currency of the digital world.'' claimed Meglena Kouneva, European Commissioner for Consumer Protection in March 2009. The value of personal data for industry, science and society in general is widely recognized today. However, the potentially identifying and sensitive nature of personal data is often a major obstacle to their collection, processing, and sharing. The goal of privacy-preserving techniques in data-centric systems is to enable a wide diversity of usages of big personal data while still providing strong privacy guarantees. The Database Security module is a hands-on introduction to database security with a strong focus on the protection of personal data. The module is based on three technical pilars : access control, database encryption, and privacy-preserving data publishing. It presents the key concepts, lets students manipulate them on real-life systems/implementations, and overviews hard issues still open today.  
 

Acquited competencies:  

  • Access control : understand the main access control models. Specify an access control policy and instanciate it in an RDBMS (e.g., Postgres, Oracle) according to the principles of the DAC and RBAC models.
  • Encrypted databases : Understand the main security models (trusted VS untrusted server) and resulting architectures (e.g., key management, secure hardware). Perform server-side encryption on an RDBMS (e.g., Postgres, Oracle) and understand the key limitations. Understand the main challenges of client-side encryption. 
  • Privacy-preserving data publishing : Understand the main privacy models and their pros/cons (e.g., k-anonymity, l-diversity, differential privacy). Implement a differentially private perturbation algorithm and validate it. Understand the main challenges of usign and designing a differentially private solution.  

Students are evaluated through homeworks and a written exam.

MPC - Machine learning 1 (code S8IGMPCU, 5 ects) 

This course is an introduction to machine learning for the purpose of the prediction of a numerical variable. In the first part, we focus on time series prediction. Different techniques are explained, such as Holt Winters, auto-regressive models. In the second part, we focus on regression techniques to predict a variable using other ones. Linear and non-linear regression are explained, as well as variable selection techniques. 

Students are evaluated through 2 personal works and a written exam. 

TWA - Technological watch (code S8ITWAU, 5 ects) 

In this course, students choose a topic, and perform a technological watch about it. At the beginning, they learn what a technological watch is, how to search for information, and how to properly cite their sources. Then, they have two months to find and read relevant documents, analyze them, and produce a synthesis through comparisons, pros and cons discussions or SWOT analyses. The analysis must cover not only the technological aspects but also the market and innovation aspects. 

Students are evaluated through a final report of 20 pages, and a defense in front of a teacher committee. 

KNI  - Knowledge and intangible assets management (code S8IDKNIU, 5 ects)

This course unit presents a deeper understanding of business management fundamentals specifically targeted at the context of knowledge economy. It focuses on the management of intangible assets within and between organizations, as well as the environment in which they develop.  

After completion of this module, the students should be able to demonstrate ability to: 

  • Apply analytical social, economic and business research concepts and methods to recognize, assess and/or forecast business opportunities in their cases studies and/or projects.    
  • Identify appropriate strategies for risk reduction potentially in relation to all dimensions covered in the student’s study and/or project:  market, customers, competition, environment and human, material and  technical resources. 
  • Leverage  the valuation process as an ongoing collective and distributed  practice where environmental, economic, societal and political  dimensions are embedded. The students should be able to relate value  to all relevant stakeholders including producers, customers,  shareholders, communities, ecological systems, etc. 
  • Focus and ground their study and/or project in the information gathered through journalistic articles, scientific papers, corporate documentation, public or self-made survey and interviews, etc. 

The course is assessed through continual assessment. Oral evaluation in the second session. 

BDL2 - Business Development Lab 2 (code S8IDBDLU, 5 ects) 

The BDL course follows the main phases of a business development. (a) idea recognition – (b) business modelling – (c) Costumer and technical development – (d) Business Plan presentation (costumer and investor oriented). 

After completion of this module, the students should be able to demonstrate ability to: 

  • Apply the learned knowledge for development of a new product or business concept. 
  • Recognize diverse obstacles in transforming an idea or technology into a business project. 
  • Invent or find solutions to address and solve the project main challenges (customer problem, functionality, business model, development…). 
  • Work in teams and to reflect upon team processes. 
  • Drive  his project according to the dimensions of (1) customer  problem/solution discovery (including in relation to the product  technical development) and (2) market discovery (related to strategic thinking) in ways that are relevant for the situation. 
  • Use analytical business skills to recognize, assess and/or develop  business opportunities in relation to all dimensions covered in his  project: market, customers, competition, environment and human,  material and technical resources. 
  • Relate the value proposed in his project to all relevant stakeholders  including producers, customers, shareholders, communities, ecological systems and policies as appropriate.  

The course is assessed through continual assessment. Oral evaluation in the second session.