Analyse de Données et Intelligence Artificielle pour Débutants

Aperçu

Learn the basic concepts of data analytics, AI, business intelligence, big data, machine learning, and deep learning. What you'll learn:

A brief overview of the history of analyzing data, from medieval statistics to the sophisticated techniques developed by the likes of Google and Microsoft.A look at data stores, which are growing exponentially, and the challenges of wrangling “big data.”Understanding of data mining—what it entails, different approaches, and who’s leading the way.A two-part discussion of business intelligence, including the principles of sound dashboard design and data presentation.The key differences between the four types of analytics—diagnostic, descriptive, predictive, and prescriptiveAn overview of specific analytics processes and models.A first look at AI, its evolution, its functions, and what it can do for businesses today.An exploration of machine learning—how systems can learn from data, identify patterns, and make decisions with little human intervention.A survey of deep learning technologies, including a variety of neural networks.An overview of the most important machine learning data modeling techniquesA practical and honest appraisal of the analytics and AI landscape today and moving forward, including the tremendous promise and the potential pitfalls.Resources for continued study on these topics. **This course includes downloadable exercise files to work with**The richest data store is only as good as your ability to search, sort, analyze, and present the data within it.

This introductory-level course will give students a broad overview of the theory and practice of data analytics and the many ways in which artificial intelligence (AI) contributes to it.Your instructor will begin with a brief history of data analytics and then proceed into discussions of data warehouses, data mining, business intelligence, machine learning, and other emerging AI techniques to make sense of big data.Students will learn how data is captured, cleansed, analyzed, and presented on business intelligence dashboards that captivate and persuade an audience. “It is a capital mistake to theorize before one has data," Sherlock Holmes once said.Whether you are investigating analytics as a potential career move or wish to better understand the terminology you encounter with increasing frequency in your professional circles, this course will give you the foundation you are looking for.This program includes 3 hours of instruction and a practice-based assessment, which will help students simulate real-world data analytics scenarios that are critical for success in today's increasingly complex workplace.Students will gain:

A brief overview of the history of analyzing data, from medieval statistics to the sophisticated techniques developed by the likes of Google and Microsoft.A look at data stores, which are growing exponentially, and the challenges of wrangling “big data.”Understanding of data mining—what it entails, different approaches, and who’s leading the way.A two-part discussion of business intelligence, including the principles of sound dashboard design and data presentation.The key differences between the four types of analytics—diagnostic, descriptive, predictive, and prescriptive—and how they relate to and build upon each other, and how they apply to various industries.An overview of specific analytics processes and models.A first look at AI, its evolution, its functions, and what it can do for businesses today.An exploration of machine learning—how systems can learn from data, identify patterns, and make decisions with little human intervention.A survey of deep learning technologies, including a variety of neural networks.An overview of the most important machine learning data modeling techniquesA practical and honest appraisal of the analytics and AI landscape today and moving forward, including the tremendous promise and the potential pitfalls.Resources for continued study on these topics.This course includes:

3 hours of video tutorials20 individual video lecturesCourse and Exercise files to follow alongCertificate of completion

Programme

• Introduction à l'analyse de données et à l'IA
Aperçu de l'analyse de données
Essentiels de l'intelligence artificielle
Structure du cours et téléchargement des fichiers d'exercices
• Fondamentaux de l'analyse de données
Types de données : structurées et non structurées
Collecte et nettoyage des données
Introduction aux outils de visualisation de données
• Statistiques de base pour l'analyse de données
Statistiques descriptives : moyenne, médiane, mode
Statistiques inférentielles : échantillonnage et tests d'hypothèses
Corrélation et causalité
• Prise de décision basée sur les données
Compréhension des problématiques commerciales
Stratégies basées sur les données
Analyse et interprétation des résultats des données
• Introduction aux concepts de l'IA
Évolution historique et rôle actuel de l'IA
Terminologies de base en IA
Aperçu des applications populaires de l'IA
• Fondamentaux de l'apprentissage automatique
Apprentissage supervisé vs non supervisé
Algorithmes courants : arbres de décision, k-means, et régression linéaire
Introduction aux réseaux neuronaux
• Outils et plateformes pour l'analyse de données et l'IA
Aperçu des outils : Python, R, Excel
Introduction aux notebooks Jupyter
Instructions de téléchargement et d'installation des outils logiciels
• Exercices pratiques et études de cas
Exercices pratiques avec jeux de données fournis
Études de cas réelles dans diverses industries
Discussions en groupe et travail de projet
• Considérations éthiques en science des données et en IA
Confidentialité et sécurité des données
Biais dans les algorithmes d'IA
Pratiques éthiques en IA
• Conclusion et prochaines étapes
Récapitulatif des concepts clés appris
Ressources pour un apprentissage complémentaire
Chemins de carrière en analyse de données et en IA

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Enseigné par

Simon Sez IT

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Matières

Data Science