A graph is a non-linear data structure consisting of nodes and edges where the nodes are connected via edges. There are different ways to represent graphs including using an adjacency matrix or adjacency lists. Common graph terminology includes vertices, edges, degree, and traversal algorithms like depth-first search (DFS) and breadth-first search (BFS) which are used to search graphs. DFS uses a stack and explores nodes as deep as possible before backtracking while BFS uses a queue and explores all neighbor nodes at the present depth before moving deeper.
This document discusses three methods for generating characters in computer graphics: the stroke method, bitmap method, and starburst method. The stroke method uses line and arc functions to generate characters by assigning starting and end points. The bitmap method stores characters as arrays of pixels and allows for larger font sizes but produces aliased characters. The starburst method uses a fixed 24-bit line segment pattern to generate characters but requires more memory and produces lower quality characters with limited faces compared to the other methods.
Hierarchical clustering methods group data points into a hierarchy of clusters based on their distance or similarity. There are two main approaches: agglomerative, which starts with each point as a separate cluster and merges them; and divisive, which starts with all points in one cluster and splits them. AGNES and DIANA are common agglomerative and divisive algorithms. Hierarchical clustering represents the hierarchy as a dendrogram tree structure and allows exploring data at different granularities of clusters.
The document describes an algorithm for multiplying two n x n matrices on a 2D mesh parallel computing model. It involves initially staggering the two matrices across the processors in n-1 steps. It then performs a dot product computation of corresponding elements across all processor pairs to calculate the product matrix. This takes advantage of the parallelism available in the mesh to perform the multiplication in O(n) time using n^2 processors.
The Naive Bayes algorithm classifies data points into classes based on probability. It calculates the prior and likelihood probabilities of each feature for each class using the training data. The posterior probabilities are then computed using Bayes' theorem. For a new data point, the algorithm predicts the class with the highest posterior probability. The program implements Naive Bayes by encoding categorical classes numerically, splitting data into training and test sets, computing mean, standard deviation and probabilities for each class, and making predictions on the test set.
Single source stortest path bellman ford and dijkstraRoshan Tailor
This document discusses algorithms for finding shortest paths in weighted graphs:
- Dijkstra's algorithm finds single-source shortest paths in graphs with non-negative edge weights using a greedy approach and priority queue. It runs in O(ElogV) time with a Fibonacci heap.
- Bellman-Ford algorithm can handle graphs with negative edge weights by relaxing all edges V-1 times to detect negative cycles. It runs in O(VE) time.
- Examples are provided to illustrate the relaxation process and execution of both algorithms on sample graphs. Key properties like handling of negative weights and cycles are also explained.
This document discusses four parallel searching algorithms: Alpha-Beta search, Jamboree search, Depth-First search, and PVS search. Alpha-Beta search prunes unpromising branches without missing better moves. Jamboree search parallelizes the testing of child nodes. Depth-First search recursively explores branches until reaching a dead end, then backtracks. PVS search splits the search tree across processors, backing up values in parallel at each level. However, load imbalance can occur if some branches are much larger than others.
This document describes graph search algorithms like breadth-first search (BFS) and depth-first search (DFS). It provides details on how BFS works, including that it maintains distances from the source vertex and uses a queue to search levels outwards. BFS runs in O(V+E) time, visiting each vertex and edge once. It outputs the shortest path distances and predecessor graph. The document proves BFS is correct by showing the distances computed are less than or equal to the actual shortest path distances.
A graph is a non-linear data structure consisting of nodes and edges where the nodes are connected via edges. There are different ways to represent graphs including using an adjacency matrix or adjacency lists. Common graph terminology includes vertices, edges, degree, and traversal algorithms like depth-first search (DFS) and breadth-first search (BFS) which are used to search graphs. DFS uses a stack and explores nodes as deep as possible before backtracking while BFS uses a queue and explores all neighbor nodes at the present depth before moving deeper.
This document discusses three methods for generating characters in computer graphics: the stroke method, bitmap method, and starburst method. The stroke method uses line and arc functions to generate characters by assigning starting and end points. The bitmap method stores characters as arrays of pixels and allows for larger font sizes but produces aliased characters. The starburst method uses a fixed 24-bit line segment pattern to generate characters but requires more memory and produces lower quality characters with limited faces compared to the other methods.
Hierarchical clustering methods group data points into a hierarchy of clusters based on their distance or similarity. There are two main approaches: agglomerative, which starts with each point as a separate cluster and merges them; and divisive, which starts with all points in one cluster and splits them. AGNES and DIANA are common agglomerative and divisive algorithms. Hierarchical clustering represents the hierarchy as a dendrogram tree structure and allows exploring data at different granularities of clusters.
The document describes an algorithm for multiplying two n x n matrices on a 2D mesh parallel computing model. It involves initially staggering the two matrices across the processors in n-1 steps. It then performs a dot product computation of corresponding elements across all processor pairs to calculate the product matrix. This takes advantage of the parallelism available in the mesh to perform the multiplication in O(n) time using n^2 processors.
The Naive Bayes algorithm classifies data points into classes based on probability. It calculates the prior and likelihood probabilities of each feature for each class using the training data. The posterior probabilities are then computed using Bayes' theorem. For a new data point, the algorithm predicts the class with the highest posterior probability. The program implements Naive Bayes by encoding categorical classes numerically, splitting data into training and test sets, computing mean, standard deviation and probabilities for each class, and making predictions on the test set.
Single source stortest path bellman ford and dijkstraRoshan Tailor
This document discusses algorithms for finding shortest paths in weighted graphs:
- Dijkstra's algorithm finds single-source shortest paths in graphs with non-negative edge weights using a greedy approach and priority queue. It runs in O(ElogV) time with a Fibonacci heap.
- Bellman-Ford algorithm can handle graphs with negative edge weights by relaxing all edges V-1 times to detect negative cycles. It runs in O(VE) time.
- Examples are provided to illustrate the relaxation process and execution of both algorithms on sample graphs. Key properties like handling of negative weights and cycles are also explained.
This document discusses four parallel searching algorithms: Alpha-Beta search, Jamboree search, Depth-First search, and PVS search. Alpha-Beta search prunes unpromising branches without missing better moves. Jamboree search parallelizes the testing of child nodes. Depth-First search recursively explores branches until reaching a dead end, then backtracks. PVS search splits the search tree across processors, backing up values in parallel at each level. However, load imbalance can occur if some branches are much larger than others.
This document describes graph search algorithms like breadth-first search (BFS) and depth-first search (DFS). It provides details on how BFS works, including that it maintains distances from the source vertex and uses a queue to search levels outwards. BFS runs in O(V+E) time, visiting each vertex and edge once. It outputs the shortest path distances and predecessor graph. The document proves BFS is correct by showing the distances computed are less than or equal to the actual shortest path distances.
A Bezier curve is a mathematically defined curve used in graphic applications. It is defined by four points: two anchors at the initial and terminal positions and two handles that control the shape of the curve. The curve can be altered by moving the handles. Bezier curves are commonly used to piece together complex curves from simpler component curves. Matching endpoints ensures continuity, while aligning handles ensures smooth tangency between connecting segments.
La visualisation est un élément important de la compréhension et de la (re)présentation des données dans les (data) sciences. Elle repose sur des principes et des outils que Christophe Bontemps (Toulouse School of Economics) décryptera à la lumière de son expérience et de ses lectures.
A Bezier curve is a parametric curve used in computer graphics defined by control points. It was developed by Pierre Bezier in 1962 and uses Bernstein polynomials as the basis. Key properties are that the curve interpolates the first and last control points, lies within the convex hull of the control points, and has its shape determined by all interior points. Higher degree curves are used for more complex shapes by piecing together lower degree Bezier sections.
ATTRIBUTES OF OUTPUT PRIMITIVES IN COMPUTER GRAPHICSnehrurevathy
This document discusses different types of filled area primitives and their attributes. It describes boundary fill and flood fill algorithms for filling regions. Boundary fill uses recursion to fill pixels adjacent to the boundary, while flood fill selects an interior seed point and fills surrounding pixels. The document also covers line attributes like type, width, and color. It explains character attributes such as font, size, color, and orientation used to display text. Marker attributes define symbols and their size/color that can represent points.
- Simpson's rule estimates the area under a curve by dividing it into equal width strips and fitting quadratic curves to points in each strip.
- The number of strips must be even and the number of ordinates is always one more than the number of strips.
- The formula for Simpson's rule involves summing terms with coefficients that alternate between 4/3, 1, 4/3, etc. depending on the number of strips.
The document discusses the mathematical foundation of computer science, specifically the inclusion-exclusion principle. It provides an overview of the principle, which is a counting technique for obtaining the number of elements in the union of finite sets. It describes how the principle works for two sets using a formula, and how it generalizes to three or more sets. The principle involves including the cardinalities of sets, excluding the intersections of pairs of sets, and continuing this pattern of inclusion and exclusion for higher-order intersections as needed. Some applications of the principle in discrete mathematics and combinatorics are then outlined.
It includes:
Introduction to Graphs
Applications
Graph representation
Graph terminology
Graph operations
Adding vertex and edge in Adjacency matrix representation using C++ program
Adjacency List implementation in C++
Homework Problems
References
Introduction
What is ML, DL, AL?
Decision Tree
Definition
Why Decision Tree?
Basic Terminology
Challenges
Random Forest
Definition
Why Random Forest
How does it work?
Advantages & Disadvantages
Definition: According to Arthur Samuel (1950) “Machine Learning is a field of study that gives computers the ability to learn without being explicitly programmed”.
Machine learning is the study and design of algorithms which can learn by processing input (learning samples) data.
The most widely used definition of machine learning is that of Carnegie Mellon University Professor Tom Mitchell: “A computer program is said to learn from experience ‘E’, with respect to some class of tasks ‘T’ and performance measure ‘P’ if its performance at tasks in ‘T’ as measured by ‘P’ improves with experience ‘E’”.
Decision Tree
Definition
Why Decision Tree?
Basic Terminology
Challenges
Random Forest
Definition
Why Random Forest
How does it work?
Line Drawing Algorithms - Computer Graphics - NotesOmprakash Chauhan
Straight-line drawing algorithms are based on incremental methods.
In incremental method line starts with a straight point, then some fix incrementable is added to current point to get next point on the line and the same has continued all the end of the line.
- A minimum spanning tree (MST) connects all nodes in a graph with the minimum total edge weight while avoiding cycles.
- There are different algorithms that can find an MST, such as Kruskal's algorithm and Prim's algorithm, which were introduced in the document.
- Kruskal's algorithm works by sorting the edges by weight and building the MST by adding the shortest edges that do not create cycles. Prim's algorithm grows the MST from an initial node by always adding the shortest edge connected to the current MST.
This presentation includes all the details regarding the Backus Naur Form and the Extended Backus Naur Form.For more information visit : https://www.youtube.com/watch?v=hl2NLbIaU7U&t=255s
The document discusses the problem of determining the optimal way to fully parenthesize the product of a chain of matrices to minimize the number of scalar multiplications. It presents a dynamic programming approach to solve this problem in four steps: 1) characterize the structure of an optimal solution, 2) recursively define the cost of an optimal solution, 3) compute the costs using tables, 4) construct the optimal solution from the tables. An example is provided to illustrate computing the costs table and finding the optimal parenthesization of a chain of 6 matrices.
The document describes m-way search trees, B-trees, heaps, and their related operations. An m-way search tree is a tree where each node has at most m child nodes and keys are arranged in ascending order. B-trees are similar but ensure the number of child nodes falls in a range and all leaf nodes are at the same depth. Common operations like searching, insertion, and deletion are explained for each with examples. Heaps store data in a complete binary tree structure where a node's value is greater than its children's values.
1. Discretization involves dividing the range of continuous attributes into intervals to reduce data size. Concept hierarchy formation recursively groups low-level concepts like numeric values into higher-level concepts like age groups.
2. Common techniques for discretization and concept hierarchy generation include binning, histogram analysis, clustering analysis, and entropy-based discretization. These techniques can be applied recursively to generate hierarchies.
3. Discretization and concept hierarchies reduce data size, provide more meaningful interpretations, and make data mining and analysis easier.
The document discusses constructing a directed acyclic graph (DAG) to represent the computation of values in a basic block of code. It describes how to build the DAG by processing each statement and creating nodes for operators and values. The DAG makes it possible to analyze the code block to optimize computations by removing duplicate subexpressions and determine which values are used inside and outside the block.
This document provides an overview of basic graph algorithms. It begins with examples of graphs in everyday life and a brief history of graph theory starting with Euler. It then covers basic graph terminology and properties like nodes, edges, degrees. Common representations of graphs in computers like adjacency lists and matrices are described. Breadth-first search and depth-first search algorithms for traversing graphs are introduced. Finally, applications of graph algorithms like finding paths, connected components, and topological sorting are mentioned.
There are three main methods for generating characters using software in computer graphics:
1) The stroke method uses a sequence of line and arc drawing functions to generate characters by assigning start and end points. It can generate various fonts but produces aliased characters for diagonals.
2) The bitmap method stores character pixel data in an array and plots each pixel to generate the character. It can easily generate various fonts and sizes but also produces aliased characters.
3) The starburst method uses a fixed pattern of 24 line segments represented by bits to highlight lines and generate characters. It requires extra memory for the bit patterns and produces lower quality characters due to its limited templates.
The document provides an introduction to OpenGL programming. It discusses that OpenGL is a hardware-independent API for 3D graphics. It originated from SGI's GL library and was developed as the cross-platform OpenGL standard. The document outlines OpenGL's core functionality and architecture, as well as common libraries like GLUT and GLU. It provides examples of basic OpenGL programs and concepts like the rendering pipeline, coordinate systems, and event handling.
Dijkstra's algorithm is a solution to the single-source shortest path problem in graph theory. It finds the shortest paths from a source vertex to all other vertices in a weighted graph where all edge weights are non-negative. The algorithm uses a greedy approach, maintaining a set of vertices whose final shortest path from the source vertex has already been determined.
A Bezier curve is a mathematically defined curve used in graphic applications. It is defined by four points: two anchors at the initial and terminal positions and two handles that control the shape of the curve. The curve can be altered by moving the handles. Bezier curves are commonly used to piece together complex curves from simpler component curves. Matching endpoints ensures continuity, while aligning handles ensures smooth tangency between connecting segments.
La visualisation est un élément important de la compréhension et de la (re)présentation des données dans les (data) sciences. Elle repose sur des principes et des outils que Christophe Bontemps (Toulouse School of Economics) décryptera à la lumière de son expérience et de ses lectures.
A Bezier curve is a parametric curve used in computer graphics defined by control points. It was developed by Pierre Bezier in 1962 and uses Bernstein polynomials as the basis. Key properties are that the curve interpolates the first and last control points, lies within the convex hull of the control points, and has its shape determined by all interior points. Higher degree curves are used for more complex shapes by piecing together lower degree Bezier sections.
ATTRIBUTES OF OUTPUT PRIMITIVES IN COMPUTER GRAPHICSnehrurevathy
This document discusses different types of filled area primitives and their attributes. It describes boundary fill and flood fill algorithms for filling regions. Boundary fill uses recursion to fill pixels adjacent to the boundary, while flood fill selects an interior seed point and fills surrounding pixels. The document also covers line attributes like type, width, and color. It explains character attributes such as font, size, color, and orientation used to display text. Marker attributes define symbols and their size/color that can represent points.
- Simpson's rule estimates the area under a curve by dividing it into equal width strips and fitting quadratic curves to points in each strip.
- The number of strips must be even and the number of ordinates is always one more than the number of strips.
- The formula for Simpson's rule involves summing terms with coefficients that alternate between 4/3, 1, 4/3, etc. depending on the number of strips.
The document discusses the mathematical foundation of computer science, specifically the inclusion-exclusion principle. It provides an overview of the principle, which is a counting technique for obtaining the number of elements in the union of finite sets. It describes how the principle works for two sets using a formula, and how it generalizes to three or more sets. The principle involves including the cardinalities of sets, excluding the intersections of pairs of sets, and continuing this pattern of inclusion and exclusion for higher-order intersections as needed. Some applications of the principle in discrete mathematics and combinatorics are then outlined.
It includes:
Introduction to Graphs
Applications
Graph representation
Graph terminology
Graph operations
Adding vertex and edge in Adjacency matrix representation using C++ program
Adjacency List implementation in C++
Homework Problems
References
Introduction
What is ML, DL, AL?
Decision Tree
Definition
Why Decision Tree?
Basic Terminology
Challenges
Random Forest
Definition
Why Random Forest
How does it work?
Advantages & Disadvantages
Definition: According to Arthur Samuel (1950) “Machine Learning is a field of study that gives computers the ability to learn without being explicitly programmed”.
Machine learning is the study and design of algorithms which can learn by processing input (learning samples) data.
The most widely used definition of machine learning is that of Carnegie Mellon University Professor Tom Mitchell: “A computer program is said to learn from experience ‘E’, with respect to some class of tasks ‘T’ and performance measure ‘P’ if its performance at tasks in ‘T’ as measured by ‘P’ improves with experience ‘E’”.
Decision Tree
Definition
Why Decision Tree?
Basic Terminology
Challenges
Random Forest
Definition
Why Random Forest
How does it work?
Line Drawing Algorithms - Computer Graphics - NotesOmprakash Chauhan
Straight-line drawing algorithms are based on incremental methods.
In incremental method line starts with a straight point, then some fix incrementable is added to current point to get next point on the line and the same has continued all the end of the line.
- A minimum spanning tree (MST) connects all nodes in a graph with the minimum total edge weight while avoiding cycles.
- There are different algorithms that can find an MST, such as Kruskal's algorithm and Prim's algorithm, which were introduced in the document.
- Kruskal's algorithm works by sorting the edges by weight and building the MST by adding the shortest edges that do not create cycles. Prim's algorithm grows the MST from an initial node by always adding the shortest edge connected to the current MST.
This presentation includes all the details regarding the Backus Naur Form and the Extended Backus Naur Form.For more information visit : https://www.youtube.com/watch?v=hl2NLbIaU7U&t=255s
The document discusses the problem of determining the optimal way to fully parenthesize the product of a chain of matrices to minimize the number of scalar multiplications. It presents a dynamic programming approach to solve this problem in four steps: 1) characterize the structure of an optimal solution, 2) recursively define the cost of an optimal solution, 3) compute the costs using tables, 4) construct the optimal solution from the tables. An example is provided to illustrate computing the costs table and finding the optimal parenthesization of a chain of 6 matrices.
The document describes m-way search trees, B-trees, heaps, and their related operations. An m-way search tree is a tree where each node has at most m child nodes and keys are arranged in ascending order. B-trees are similar but ensure the number of child nodes falls in a range and all leaf nodes are at the same depth. Common operations like searching, insertion, and deletion are explained for each with examples. Heaps store data in a complete binary tree structure where a node's value is greater than its children's values.
1. Discretization involves dividing the range of continuous attributes into intervals to reduce data size. Concept hierarchy formation recursively groups low-level concepts like numeric values into higher-level concepts like age groups.
2. Common techniques for discretization and concept hierarchy generation include binning, histogram analysis, clustering analysis, and entropy-based discretization. These techniques can be applied recursively to generate hierarchies.
3. Discretization and concept hierarchies reduce data size, provide more meaningful interpretations, and make data mining and analysis easier.
The document discusses constructing a directed acyclic graph (DAG) to represent the computation of values in a basic block of code. It describes how to build the DAG by processing each statement and creating nodes for operators and values. The DAG makes it possible to analyze the code block to optimize computations by removing duplicate subexpressions and determine which values are used inside and outside the block.
This document provides an overview of basic graph algorithms. It begins with examples of graphs in everyday life and a brief history of graph theory starting with Euler. It then covers basic graph terminology and properties like nodes, edges, degrees. Common representations of graphs in computers like adjacency lists and matrices are described. Breadth-first search and depth-first search algorithms for traversing graphs are introduced. Finally, applications of graph algorithms like finding paths, connected components, and topological sorting are mentioned.
There are three main methods for generating characters using software in computer graphics:
1) The stroke method uses a sequence of line and arc drawing functions to generate characters by assigning start and end points. It can generate various fonts but produces aliased characters for diagonals.
2) The bitmap method stores character pixel data in an array and plots each pixel to generate the character. It can easily generate various fonts and sizes but also produces aliased characters.
3) The starburst method uses a fixed pattern of 24 line segments represented by bits to highlight lines and generate characters. It requires extra memory for the bit patterns and produces lower quality characters due to its limited templates.
The document provides an introduction to OpenGL programming. It discusses that OpenGL is a hardware-independent API for 3D graphics. It originated from SGI's GL library and was developed as the cross-platform OpenGL standard. The document outlines OpenGL's core functionality and architecture, as well as common libraries like GLUT and GLU. It provides examples of basic OpenGL programs and concepts like the rendering pipeline, coordinate systems, and event handling.
Dijkstra's algorithm is a solution to the single-source shortest path problem in graph theory. It finds the shortest paths from a source vertex to all other vertices in a weighted graph where all edge weights are non-negative. The algorithm uses a greedy approach, maintaining a set of vertices whose final shortest path from the source vertex has already been determined.
Neha Dolliyar is a software tester with over 2 years of experience working at BSL Information Technology in Gurgaon, India. She has expertise in writing test cases, defect tracking, and working with developers to resolve issues. Some of her major projects include testing an ASP.NET load management system, an iOS task management app, and an ASP.NET CRM web application. She is proficient with testing tools like Jira, Bugzilla, and Mantis. Neha holds a B.Tech in computer science and seeks new opportunities to expand her quality assurance and testing skills.
This short document promotes creating presentations on SlideShare using Haiku Deck. It features an Instagram photo credit and a call to action encouraging the reader to get started making their own Haiku Deck presentation on SlideShare. In just a few words, it pitches the idea of easily making digital presentations.
James Okarimia - IFRS Implementation and How the Banks should Approach IT.JAMES OKARIMIA
This document discusses IFRS 9 implementation for banks. It provides an overview of IFRS 9 requirements, including classification and measurement of financial assets and liabilities, impairment methodology, and hedge accounting. It recommends banks take a transformation program approach with three phases: assess, design, implement. It identifies key areas of impact like governance, policies, methodology, models, and data. It also discusses challenges of the tight timeline, wide organizational impact, data needs, and complexity involved in IFRS 9 implementation.
James Okarimia Aligning Finance , Risk and Compliance to Meet RegulationJAMES OKARIMIA
1) Banks face significant challenges from the increasing number and scope of regulations like Dodd-Frank, Basel III, and IFRS that they must comply with. 2) To meet these compliance requirements, financial institutions must transform their IT infrastructure to provide the necessary transparency, analytics, and reporting. 3) A unified platform can help financial institutions meet regulatory needs while also improving efficiency and competitive advantage by providing a single view of data across the organization.
This short document promotes creating presentations using Haiku Deck, a tool for making slideshows. It encourages the reader to get started making their own Haiku Deck presentation and sharing it on SlideShare. In a single sentence, it pitches the idea of using Haiku Deck to easily design presentations.
IFRS Implementation and How the Banks should Approach ItJAMES OKARIMIA
This document discusses how banks should approach implementing IFRS 9 accounting standards. It notes that IFRS 9 implementation will require significant effort across governance, policies, processes, data, and systems. It recommends treating implementation as a transformation program with three phases: assess, design, implement. Key areas of impact include governance, policies and methodology, models, and data requirements. Challenges include a tight timeline, wide organizational impact, data needs, and complexity. Senior management must drive implementation from the top down with cross-functional teams and a focus on change management.
The document discusses the history and development of the Internet. It began in the late 1960s as a small network connecting 4 universities that grew rapidly. By the 1990s, the Internet was commercialized and widely available to the public. The Internet is a global system of interconnected computer networks that use common communication standards to link devices worldwide. Information is transmitted through packets routed between nodes and networks using routing tables. The Internet provides access to a vast amount of information and allows for communication around the world.
La generación de empleos fue uno de los beneficios para el estado, pues en algún momento estuvieron cerca de 2 mil personas laborando. Además se compraron materiales como la piedra de cantera en Santo Tomás Chautla, por eso podemos decir que la derrama económica fue otro de los factores importantes en el ámbito local y regional de Puebla.
Les politiques publiques de données ouvertes (Open Data) se sont multipliées et renforcées à l’échelle mondiale, via des textes législatifs nationaux et des engagements internationaux. Mises en œuvre par des acteurs à différentes échelles comme les institutions internationales, les États, les régions et les métropoles, elles couvrent des domaines multiples dans la science, les transports, l’environnement, la culture. Elles répondent à l’engagement de transparence du pouvoir et sont pensées comme des outils du développement dans les pays émergents. La croissance exponentielle de ces données ouvertes, librement accessibles et exploitables, est une opportunité pour l’éducation. En contexte francophone - Canada, France, Suisse,… - comme anglophone, nous présenterons comment leur traitement numérique permet l’acquisition des savoirs et des capacités, mais aussi le développement des compétences douces comme la résolution de problème, la créativité et le travail collaboratif. Par ce traitement et les projets de réutilisation, on développe le regard critique et une méthodologie rigoureuse, ce qui participe à la construction de la citoyenneté numérique, notamment face au risque de désinformation. Initié à réfléchir sur l’articulation donnée-information-connaissance, l’élève peut alors lui-même devenir producteur de données et explorer des voies professionnelles liées à l’écologie, l’entreprenariat numérique, l’administration ou le ROSO (OSINT).
Cycle de Formation Théâtrale 2024 / 2025Billy DEYLORD
Pour la Saison 2024 / 2025, l'association « Le Bateau Ivre » propose un Cycle de formation théâtrale pour particuliers amateurs et professionnels des arts de la scène enfants, adolescents et adultes à l'Espace Saint-Jean de Melun (77). 108 heures de formation, d’octobre 2024 à juin 2025, à travers trois cours hebdomadaires (« Pierrot ou la science de la Scène », « Montage de spectacles », « Le Mime et son Répertoire ») et un stage annuel « Tournez dans un film de cinéma muet ».
Newsletter SPW Agriculture en province du Luxembourg du 12-06-24BenotGeorges3
Les informations et évènements agricoles en province du Luxembourg et en Wallonie susceptibles de vous intéresser et diffusés par le SPW Agriculture, Direction de la Recherche et du Développement, Service extérieur de Libramont.
Le fichier :
Les newsletters : https://agriculture.wallonie.be/home/recherche-developpement/acteurs-du-developpement-et-de-la-vulgarisation/les-services-exterieurs-de-la-direction-de-la-recherche-et-du-developpement/newsletters-des-services-exterieurs-de-la-vulgarisation/newsletters-du-se-de-libramont.html
Bonne lecture et bienvenue aux activités proposées.
#Agriculture #Wallonie #Newsletter #Recherche #Développement #Vulgarisation #Evènement #Information #Formation #Innovation #Législation #PAC #SPW #ServicepublicdeWallonie
Conseils pour Les Jeunes | Conseils de La Vie| Conseil de La JeunesseOscar Smith
Besoin des conseils pour les Jeunes ? Le document suivant est plein des conseils de la Vie ! C’est vraiment un document conseil de la jeunesse que tout jeune devrait consulter.
Voir version video:
➡https://youtu.be/7ED4uTW0x1I
Sur la chaine:👇
👉https://youtube.com/@kbgestiondeprojets
Aimeriez-vous donc…
-réussir quand on est jeune ?
-avoir de meilleurs conseils pour réussir jeune ?
- qu’on vous offre des conseils de la vie ?
Ce document est une ressource qui met en évidence deux obstacles qui empêchent les jeunes de mener une vie épanouie : l'inaction et le pessimisme.
1) Découvrez comment l'inaction, c'est-à-dire le fait de ne pas agir ou d'agir alors qu'on le devrait ou qu'on est censé le faire, est un obstacle à une vie épanouie ;
> Comment l'inaction affecte-t-elle l'avenir du jeune ? Que devraient plutôt faire les jeunes pour se racheter et récupérer ce qui leur appartient ? A découvrir dans le document ;
2) Le pessimisme, c'est douter de tout ! Les jeunes doutent que la génération plus âgée ne soit jamais orientée vers la bonne volonté. Les jeunes se sentent toujours mal à l'aise face à la ruse et la volonté politique de la génération plus âgée ! Cet état de doute extrême empêche les jeunes de découvrir les opportunités offertes par les politiques et les dispositifs en faveur de la jeunesse. Voulez-vous en savoir plus sur ces opportunités que la plupart des jeunes ne découvrent pas à cause de leur pessimisme ? Consultez cette ressource gratuite et profitez-en !
En rapport avec les " conseils pour les jeunes, " cette ressource peut aussi aider les internautes cherchant :
➡les conseils pratiques pour les jeunes
➡conseils pour réussir
➡jeune investisseur conseil
➡comment investir son argent quand on est jeune
➡conseils d'écriture jeunes auteurs
➡conseils pour les jeunes auteurs
➡comment aller vers les jeunes
➡conseil des jeunes citoyens
➡les conseils municipaux des jeunes
➡conseils municipaux des jeunes
➡conseil des jeunes en mairie
➡qui sont les jeunes
➡projet pour les jeunes
➡conseil des jeunes paris
➡infos pour les jeunes
➡conseils pour les jeunes
➡Quels sont les bienfaits de la jeunesse ?
➡Quels sont les 3 qualités de la jeunesse ?
➡Comment gérer les problèmes des adolescents ?
➡les conseils de jeunes
➡guide de conseils de jeunes
Formation M2i - Onboarding réussi - les clés pour intégrer efficacement vos n...M2i Formation
Améliorez l'intégration de vos nouveaux collaborateurs grâce à notre formation flash sur l'onboarding. Découvrez des stratégies éprouvées et des outils pratiques pour transformer l'intégration en une expérience fluide et efficace, et faire de chaque nouvelle recrue un atout pour vos équipes.
Les points abordés lors de la formation :
- Les fondamentaux d'un onboarding réussi
- Les outils et stratégies pour un onboarding efficace
- L'engagement et la culture d'entreprise
- L'onboarding continu et l'amélioration continue
Formation offerte animée à distance avec notre expert Eric Collin
2. Trouver les coordonnées X et Y
• Pour réaliser une représentation en oursin des flux domicile-
travail, vous avez tout d’abord besoin de connaître les
coordonnées x et y du lieu de départ (ici le domicile) et les
coordonnées x et y du lieu de destination (ici le lieu de travail).
• Le plus simple consiste à chercher ces coordonnées sur Google
Maps : https://www.google.fr/maps
3. • 1. Cliquez sur le lieu de départ que vous souhaitez localiser
2. pour faire apparaître ses coordonnées (latitude et longitude)
3. Copiez ces coordonnées
4. • Collez ces coordonnées dans un fichier de type Excel
Les données sont ici
classées dans deux
colonnes nommées x et y
5. • 1. Cliquez sur le lieu de destination que vous souhaitez localiser
2. pour faire apparaître ses coordonnées (latitude et longitude)
3. Copiez ces coordonnées
6. • Collez ces coordonnées dans un fichier de type Excel
Les données sont ici
classées dans deux
colonnes nommées w et z
Elles sont identiques car un
seul lieu de destination
8. • Ouvrir QGIS, puis dans le bandeau à gauche
cliquez sur l'icône « Ajouter une couche de
texte délimité »
9. • Ouvrir QGIS, puis dans le bandeau à gauche
cliquez sur l'icône « Ajouter une couche de
texte délimité » une fenêtre apparaît
10. • Cliquez sur l’onglet « Parcourir » pour
sélectionner le fichier CSV à importer
11. • Sélectionnez la projection de la couche
Cliquez sur « OK » pour faire
apparaître la couche de points
12. • La couche de points géolocalisés apparaît dans
la fenêtre de QGIS
13. Dans le bandeau du haut, cliquez sur le Menu « Extension » >
« Installer/Gérer les extensions »
14.
Cliquez sur l’extension RT
QSpider pour installer ce plugin
dans QGIS
Dans le bandeau du haut, cliquez sur le Menu « Extension » >
« Installer/Gérer les extensions »
15. Dans le bandeau du haut, cliquez sur le Menu « Extension » >
RT QSpider > Table to vector converter
16. Dans le bandeau du haut, cliquez sur le Menu « Extension » >
RT QSpider > Table to vector converter
Dans la fenêtre qui apparaît
cliquez sur « Line »
17. Dans le bandeau du haut, cliquez sur le Menu « Extension » >
RT QSpider > Table to vector converter
Renseignez la Table avec
les données de votre
fichier CSV (x, y, w, z) en
respectant bien les
correspondances…
18. Dans le bandeau du haut, cliquez sur le Menu « Extension » >
RT QSpider > Table to vector converter
Renseignez la Table avec
les données de votre
fichier CSV (x, y, w, z) en
respectant bien les
correspondances comme
ci-contre
19. 1. Donnez un nom à votre fichier puis cliquez sur
« Enregistrer »
2. L’oursin est créé !!
20. 1. Donnez un nom à votre fichier puis cliquez sur
« Enregistrer »
2. L’oursin est créé !!