Online Courses and Tutorials

Onlinecourses.tech provides you with the latest online courses information by assisting over 45,000 courses and 1 million students.

Learn programming, marketing, data science and more.

Get started today

Skip to main content

Featured Post

Machine Learning

Master machine learning fundamentals in four hands-on courses

About This Specialization This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data. Created by: Industry Partners: 4 courses Follow the suggested order or choose your own. Projects Designed to help you practice and apply the skills you learn. Certificates Highlight your new skills on your resume or

Initiation à la programmation (en C++)

Initiation à la programmation (en C++)


About this course: Ce cours initie aux bases de la programmation en utilisant le langage C++ : variables, boucles, fonctions, ...
Il ne présuppose pas de connaissance préalable. Les aspects plus avancés (programmation orientée objet) sont donnés dans un cours suivant, «Introduction à la programmation orientée objet (en C++)». Il s'appuie sur de nombreux éléments pédagogiques : vidéos sous-titrées, quizz dans et hors vidéos, exercices, devoirs notés automatiquement, notes de cours.

Who is this class for: Ce cours s'adresse à toute personne désireuse d'apprendre les concepts de base de la programmation. Aucun prérequis n'est nécessaire, mais la formulation des exercices présuppose une « culture » niveau lycée (sans que ce soit rédhibitoire non plus).

Created by:   École Polytechnique Fédérale de Lausanne
  • Jean-Cédric Chappelier
    Taught by:    Jean-Cédric Chappelier, Dr.
    School of Computer and Communication Sciences
  • Jamila Sam
    Taught by:    Jamila Sam, Dr
    School of Computer and Communication Sciences
LevelBeginner
Commitment8 semaines de cours, 4-6 heures/semaine
Language
FrenchSubtitles: English
How To PassPass all graded assignments to complete the course.
User Ratings
Average User Rating 4.7See what learners said
Syllabus
WEEK 1
Initiation à la programmation
Cette semaine vous accueille dans le cours et vous présente les premiers concepts de base de la programmation. 

9 videos9 readings
  1. Video: Bienvenue
  2. Reading: Déroulement du cours
  3. Reading: Contributeurs
  4. Reading: Installation d'un environnement de développement C++11 sous Linux
  5. Video: Installation sous Linux (Ubuntu)
  6. Reading: Installation d'un environnement de développement C++11 sous Mac OS X
  7. Video: Installation sous MacOS 10.9 et sup.
  8. Video: Installation sous MacOS 10.7 et 10.8
  9. Reading: Installation d'un environnement de développement C++11 sous Window
  10. Video: Installation sous Windows
  11. Reading: [optionnel] Debugging sous Geany
  12. Video: Introduction
  13. Video: Variables
  14. Video: Variables : lecture/écriture
  15. Video: Expressions
  16. Reading: Notes de cours
  17. Reading: Exercices
  18. Reading: Compléments de cours (variables et expressions)
  19. Ungraded Programming: (non noté) Premiers programmes
Graded: Variables et expressions
WEEK 2
Structures de contrôle (1) : branchements conditionnels
Nous abordons cette semaine, et continuerons la semaine prochaine, les « structures de contrôle » qui permettent de décrire comment certaines données peuvent influencer les traitements à effectuer. Nous commençons par les « branchements conditionnels » grâce auxquels une portion de programme peut être exécutée ou non suivant différents tests.

3 videos3 readings
  1. Video: Branchements conditionnels
  2. Video: Conditions
  3. Video: Erreurs de débutant le type bool
  4. Reading: Notes de cours
  5. Reading: Exercices
  6. Reading: Compléments de cours (branchements conditionnels)
Graded: Branchements conditionnels et booléens
Graded: Branchements conditionnels
WEEK 3
Structures de contrôle (2) : boucles et itérations
Cette semaine, nous terminons la présentation des « structures de contrôle » avec les boucles et les itérations qui permettent de faire répéter certaines parties d'un programme. 

5 videos2 readings
  1. Video: Itérations : introduction
  2. Video: Itérations : approfondissement et exemples
  3. Video: Itérations : quiz
  4. Video: Boucles conditionnelles
  5. Video: Blocs d'instructions
  6. Reading: Notes de cours
  7. Reading: Exercices
Graded: Boucles et itérations
Graded: Boucles et itérations
WEEK 4
Fonctions
Cette semaine aborde un sujet fondamental en programmation : les « fonctions » qui permettent de beaucoup mieux structurer les programmes et d'éviter d'avoir à récrire plusieurs fois la même portion de code. 

7 videos2 readings
  1. Video: Fonctions : introduction
  2. Video: Fonctions : appels
  3. Video: Fonctions : passage des arguments
  4. Video: Fonctions : prototypes
  5. Video: Fonctions : définitions
  6. Video: Fonctions : méthodologie
  7. Video: Fonctions : arguments par défaut et surcharge
  8. Reading: Notes de cours
  9. Reading: Exercices
Graded: Fonctions
Graded: Fonctions
WEEK 5
Tableaux
Après plusieurs semaines sur les traitements, nous revenons cette semaine et les suivantes sur les données pour présenter des types de données plus avancés que les types de base. Cette semaine : les tableaux qui permettent de regrouper plusieurs données de même type.

7 videos4 readings
  1. Reading: Errata
  2. Video: Tableaux : introduction
  3. Video: Tableaux : déclaration et initialisation des vector
  4. Video: Tableaux : utilisation des vector
  5. Video: Tableaux : exemples simples (vector)
  6. Video: Tableaux : fonctions spécifiques vector
  7. Video: Tableaux : tableaux dynamiques multidimensionnels
  8. Video: Tableaux : array
  9. Reading: Notes de cours
  10. Reading: Exercices
  11. Reading: Complément de cours : les tableaux « à la C »
Graded: Tableaux
WEEK 6
Chaînes de caractères et structures
Après les tableaux, cette semaine continue la présentation de nouveaux types de données avec les « chaînes de caractères », ensembles de lettres, et les « structures », regroupement de données devant logiquement aller ensemble. 

4 videos2 readings
  1. Video: string : introduction
  2. Video: string : traitements
  3. Video: Typedef : alias de types
  4. Video: Structures
  5. Reading: Notes de cours
  6. Reading: Exercices
Graded: Chaînes de caractères et structures
Graded: Tableaux, chaînes et structures
WEEK 7
Pointeurs et références
Cette semaine nous terminons notre présentation de nouveaux types de données avec les « pointeurs » et « références » qui permettent de faire référence à d'autres données existantes ou d'en créer de nouvelles dynamiquement. 

6 videos2 readings
  1. Video: Pointeurs et références : introduction
  2. Video: Références
  3. Video: Pointeurs : concept et analogie
  4. Video: Pointeurs : déclaration et opérateurs de base
  5. Video: Pointeurs : allocation dynamique
  6. Video: Pointeurs « intelligents »
  7. Reading: Notes de cours
  8. Reading: Exercices
Graded: Pointeurs et références
WEEK 8
Etude de cas
Nous voulons terminer notre cours avec une étude de cas, la création d'un jeu de « Puissance 4 », nous permettant de revoir tous les concepts abordés au long du cours. Nous en profitons pour présenter un dernier type de données : le type « énuméré ». 

7 videos3 readings
  1. Video: Puissance 4 : introduction
  2. Video: Puissance 4 : premières fonctions
  3. Video: Puissance 4 : fonction joue 1ère version
  4. Video: Puissance 4 : fonction joue variantes et révision
  5. Video: Puissance 4 : moteur de jeu
  6. Video: Puissance 4 : fonctions est_ce_gagne et compte
  7. Video: Puissance 4 : finalisation
  8. Reading: Code source de l'étude de cas
  9. Reading: Notes de cours
  10. Reading: Exercices
Graded: Types énumérés
How It Works
Coursework
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Help from Your Peers
Help from Your Peers
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Certificates
Certificates
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
Creators
École Polytechnique Fédérale de Lausanne

Comments

Popular posts from this blog

Hands-on Text Mining and Analytics by Yonsei University

About this course: This course provides an unique opportunity for you to learn key components of text mining and analytics aided by the real world datasets and the text mining toolkit written in Java. Hands-on experience in core text mining techniques including text preprocessing, sentiment analysis, and topic modeling help learners be trained to be a competent data scientists. Empowered by bringing lecture notes together with lab sessions based on the y-TextMiner toolkit developed for the class, learners will be able to develop interesting text mining applications.



LevelIntermediateLanguage English, Subtitles: Chinese (Simplified) How To PassPass all graded assignments to complete the course.
Syllabus

Big Data Hadoop Certification Training

Big Data Hadoop training will make you an expert in HDFS, MapReduce, Hbase, Hive, Pig, Yarn, Oozie, Flume and Sqoop using real-time use cases on Retail, Social Media, Aviation, Tourism, Finance domain. You will get Hadoop certification at the end of the course

About the Training
This Hadoop training is designed to make you a certified Big Data practitioner by providing you rich hands-on training on Hadoop ecosystem and best practices about HDFS, MapReduce, HBase, Hive, Pig, Oozie, Sqoop. This course is stepping stone to your Big Data journey and you will get the opportunity to work on a Big data Analytics project after selecting a data-set of your choice. You will get Hadoop certification after the project completion.

Training Objectives
The hadoop training is designed to help you become a top Hadoop developer. During this course, our expert instructors will train you to: Master the concepts of HDFS and MapReduce frameworkUnderstand Hadoop 2.x ArchitectureSetup Hadoop Cluster and write Co…

Learn to Program and Analyze Data with Python

About This Specialization This Specialization builds on the success of the Python for Everybody course and will introduce fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language. In the Capstone Project, you’ll use the technologies learned throughout the Specialization to design and create your own applications for data retrieval, processing, and visualization. Created by: 5 courses Follow the suggested order or choose your own. Projects Designed to help you practice and apply the skills you learn. Certificates Highlight your new skills on your resume or LinkedIn. Courses

An Introduction to Interactive Programming in Python (Part 1)

About this course: This two-part course is designed to help students with very little or no computing background learn the basics of building simple interactive applications. Our language of choice, Python, is an easy-to learn, high-level computer language that is used in many of the computational courses offered on Coursera. To make learning Python easy, we have developed a new browser-based programming environment that makes developing interactive applications in Python simple. These applications will involve windows whose contents are graphical and respond to buttons, the keyboard and the mouse. In part 1 of this course, we will introduce the basic elements of programming (such as expressions, conditionals, and functions) and then use these elements to create simple interactive applications such as a digital stopwatch. Part 1 of this class will culminate in building a version of the classic arcade game "Pong".
Who is this class for: Recommended Background - A knowledge o…

Front-End JavaScript Frameworks: Angular

About this course: This course concentrates mainly on Javascript based front-end frameworks, and in particular the Angular framework (Currently Ver. 4.x). This course will use Typescript for developing Angular application. Typescript features will be introduced in the context of Angular as part of the exercises. You will also get an introduction to the use of Angular Material and Angular Flex-Layout for responsive UI design. You will be introduced to various aspects of Angular including components, directives and services. You will learn about data binding, Angular router and its use for developing single-page applications. You will also learn about designing both template-driven forms and reactive forms. A quick introduction to Observables, reactive programming and RxJS in the context of Angular is included. You will then learn about Angular support for client-server communication and the use of REST API on the server side. You will use Restangular for communicating with a server sup…

Программирование на Python

About this course: Python – простой, гибкий и невероятно популярный язык, который используется практически во всех областях современной разработки. С его помощью можно создавать веб-приложения, писать игры, заниматься анализом данных, автоматизировать задачи системного администрирования и многое другое. “Программирование на Python” читают разработчики, применяющие Python в проектах, которыми ежедневно пользуются миллионы людей. Курс покрывает все необходимые для ежедневной работы программиста темы, а также рассказывает про многие особенности языка, которые часто опускают при его изучении. В ходе курса вы изучите конструкции языка, типы и структуры данных, функции, научитесь применять объектно-ориентированное и функциональное программирование, узнаете про особенности реализации Python, научитесь писать асинхронный и многопоточный код. Помимо теории вас ждут практические задания, которые помогут проверить полученные знания и отточить навыки программирования на Python. После успешного о…

Launch Your Career in Data Science

A nine-course introduction to data science, developed and taught by leading professors.
About This Specialization Ask the right questions, manipulate data sets, and create visualizations to communicate results. This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material. Created by: Industry Partners: 10 courses Follow the suggested order or choose your own. Projects Designed to help you practice and apply the skills you learn.

Master of Computer Science in Data Science

A flexible and affordable degree from one of the top Computer Science programs in the world, focused on one of the hottest fields of the new millennium

Enroll in the Master of Computer Science in Data Science (MCS-DS) and gain access to the computational and statistical knowledge needed to turn big data into meaningful insights. Build expertise in four core areas of computer science—data visualization, machine learning, data mining, and cloud computing—while learning key skills in statistics and information science. This completely online degree is an affordable gateway to one of the most lucrative and fastest growing careers of the new millennium. The MCS-DS is offered by CS @ ILLINOIS, a U.S. News & World Report top five CS graduate program, in collaboration with the University’s Statistics Department and top-ranked iSchool. Join our alumni network of entrepreneurs, educators, and technical visionaries, who have revolutionized the way people communicate, shop, conduct business,…

Machine Learning

Master machine learning fundamentals in four hands-on courses

About This Specialization This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data. Created by: Industry Partners: 4 courses Follow the suggested order or choose your own. Projects Designed to help you practice and apply the skills you learn. Certificates Highlight your new skills on your resume or

Introduction to Data Science in Python

About this course: This course will introduce the learner to the basics of the python programming environment, including how to download and install python, expected fundamental python programming techniques, and how to find help with python programming questions. The course will also introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the DataFrame as the central data structure for data analysis. The course will end with a statistics primer, showing how various statistical measures can be applied to pandas DataFrames. By the end of the course, students will be able to take tabular data, clean it,  manipulate it, and run basic inferential statistical analyses. This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Ne…

Archive