The laws of nature are expressed in the form of differential equations. Scientists and engineers must know how to model the world in terms of differential equations, and how to solve these equations and interpret the solutions. This course is deliberately much more focused on differential equations. It aims to give the student not only a mastery of some mathematical tools necessary for certain chemistry-physics modules in their curriculum (vibrations and waves, thermodynamics, chemical kinetics, ...), but also a kind of general knowledge and an open-mindedness regarding the interaction between different sciences, and above all, from my point of view, to strengthen their logical reasoning for sound scientific reasoning.
Welcome to Introduction to Probability and Descriptive Statistics!, a foundational course designed for first-year undergraduate students in Mathematics and Computer Science Applied to the Human and Social Sciences (MIASHS).
This course introduces the fundamental concepts of descriptive statistics and probability, providing the essential tools required to analyze data and model uncertainty. Students will learn how to organize, summarize, visualize, and interpret data using tables, graphs, and key statistical measures such as the mean, median, mode, variance, and standard deviation.
The course also covers the foundations of probability theory, including counting techniques, sample spaces, events, probability rules, conditional probability, Bayes' theorem, and independent events. In addition, students will study the Binomial, Poisson, and Normal distributions, together with their expected values and variances, and will explore basic applications of exploratory data analysis (EDA) using introductory Python tools.
By the end of this course, students will be able to analyze and interpret data, apply probability models to real-world problems, and develop the quantitative reasoning and problem-solving skills required for further studies in mathematics, computer science, engineering, data science, and related fields.
We are delighted to welcome you to this course and wish you a successful and rewarding learning experience.
Algebra II is the branch of mathematics that is interested in vector spaces and linear transformations, general formalization of theories of systems of linear equations. Linear algebra is a universal language used to describe many phenomena in mechanics, electronics, and economics,...
The main chapters of the course include:
- Notions of vector spaces
- Linear applications
- Matrices and determinants
- System of linear equations
Network Engineering is a core discipline in computer science focused on the design, implementation, management, and optimization of communication networks. It relies on networking protocols, architectures, and technologies to ensure secure, reliable, and efficient data transmission. Modern technologies such as IPv6, SDN, and advanced routing mechanisms improve network scalability, flexibility, and performance.
This course introduces the fundamental concepts and tools required for enterprise network design and deployment. It covers network architectures, services, IP technologies, routing protocols, and Quality of Service (QoS), while combining theoretical and practical aspects. The course enables students to design secure and scalable network infrastructures, analyze network requirements, and optimize performance in real-world environments.
The course aims to:
- Understand networking fundamentals and modern technologies.
- Design, configure, and manage network infrastructures.
- Implement routing, switching, and network services.
- Troubleshoot and evaluate network performance.
- Apply security, scalability, and professional networking practices.
يقدم مقرر رياضيات 01 لطلبة السنة الأولى ليسانس مدخلًا إلى أساسيات التحليل الرياضي، من خلال دراسة المتتاليات العددية والمشتقات وتطبيقاتهما. يهدف المقرر إلى إكساب الطلبة المعارف والمهارات اللازمة لتحليل المتتاليات والدوال، وحساب النهايات والمشتقات، وتنمية التفكير المنطقي وحل المشكلات الرياضية، بما يُشكل قاعدة أساسية لدراسة مقررات الرياضيات المتقدمة.
The Mathematics 2 course offered at the University Abdelhamid Mehri Constantine 2 is designed for first-year students in Economics, Commercial Sciences, and Management Sciences. Building upon the mathematical foundations acquired in Mathematics 1, this course introduces essential concepts in differential equations and linear algebra. It covers first and second-order differential equations and their solution methods, matrices and the fundamental operations on them, determinants and the computation of inverse matrices, and techniques for solving systems of linear equations. By combining theoretical concepts with practical problem-solving approaches, the course develops students' analytical reasoning and mathematical modeling skills, providing a solid foundation for advanced studies in economics, management, finance, and other quantitative disciplines. 
The course "Big Data" aims to introduce, understand, analyze, design, and implement NoSQL database solutions in the context of Big Data environments.
This course is intended for second-year Master's students in the ILSI speciality.
Generally, the course "Big Data" aims to:
- Define Big Data List its 5 V's
- State the reasons why traditional relational databases fail to handle Big Data challenges
- Recall what the term "NoSQL" refers to List the historical context in which the term NoSQL emerged.
- Name the main characteristics and features of NoSQL databases
- Identify the data properties specific to NoSQL systems
- Identify the 4 main types of NoSQL databases
- State the 3 properties of the CAP theorem
- Recall the properties of the BASE theorem
- Name the properties of the PACELC theorem
- Explain the trade-offs of the CAP, BASE, and PACELC theorems
- Define horizontal scalability and vertical scalability
- Identify the main data distribution models used in NoSQL systems
- Compare NoSQL databases and traditional RDBMS
- Explain the internal working principles of each NoSQL database category
- Interpret architectural differences, data models, and processing paradigms in the context of Big Data environments.
- Break down and evaluate frameworks and database models based on technical criteria
- Make and defend informed technical decisions about nosql database and Big Data framework choices
- Build, query, and process data using NoSQL systems and distributed computing frameworks
This activity aims to analyze the course content, learning objectives, resources, learning activities, and assessment methods. It focuses on identifying strengths, weaknesses, and possible improvements in the learning process.
This course provides a general introduction to Information Systems and their role in modern organizations.
It explains how information is represented, captured, controlled, and used to support operations, decision-making, and organizational performance.
The course also introduces key types of information systems, basic modeling techniques, IS development approaches, security and ethical issues, systems integration, and emerging technologies such as cloud computing, IoT, artificial intelligence, and quantum computing.
Learning Outcomes:
At the end of this course, students will be able to:
- Define the fundamental concepts, components, types, and technologies of Information Systems.
- Explain how Information Systems support organizational processes, decision-making, and performance.
- Apply basic data representation and modeling techniques, such as encoding, flowcharts, and ER diagrams.
- Analyze the structure, roles, and interactions of Information Systems within organizations.
- Design a basic Information System model integrating data, processes, and decision-support elements.
- Evaluate Information System solutions in terms of effectiveness, strategic value, ethics, and security.
Ce cours introduit les principes fondamentaux de l’Interaction Homme–Machine (IHM), discipline essentielle à la conception de systèmes interactifs efficaces et ergonomiques.
L’objectif est de permettre aux étudiants de concevoir, analyser et évaluer des interfaces utilisateur en tenant compte des contraintes humaines, cognitives et techniques. Le cours aborde les méthodes de conception centrée utilisateur, les modèles d’architecture des interfaces, ainsi que les critères ergonomiques.
Une partie pratique (TP) permettra aux étudiants de mettre en œuvre les concepts étudiés à travers la conception d’interfaces interactives.
This course introduces the fundamental concepts of Complex Systems and Artificial Intelligence, with emphasis on modeling, adaptation, emergence, self-organization, and collective intelligence. Students explore complex adaptive systems, Genetic Algorithms, and Swarm Intelligence techniques such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO).
The course combines theoretical concepts with practical applications in optimization and AI. Students learn how evolutionary and swarm-based methods can be used to solve complex problems, analyze convergence, perform feature selection, and improve machine-learning model performance. Practical sessions include maze pathfinding using Genetic Algorithms and feature-selection tasks using swarm-based optimization methods.
The course aims to develop both conceptual understanding and practical skills for applying nature-inspired computational methods to real-world optimization and artificial intelligence problems.
Description du cours
Le cours d'Anatomie en première année Licence STAPS permet aux étudiants d'acquérir les connaissances fondamentales sur la structure et l'organisation du corps humain. Il présente les principaux systèmes anatomiques ainsi que leur rôle dans le mouvement et l'activité physique, constituant ainsi une base essentielle pour les études en sciences du sport et l'analyse de la performance motrice.
Le cours spécialité football est structuré en plusieurs unités d’apprentissage visant à développer des compétences variées propres à votre discipline. Il permet notamment d’acquérir une connaissance approfondie des lois et de l’historique du football, ainsi qu’une compréhension des différents aspects tactiques et des exigences techniques, cognitives, physiques et physiologiques.