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Une autre de mes passions est l'activité de formation, que j'ai déjà eu l'occasion de pratiquer, notamment à la faculté de Strasbourg. Depuis 2 ans, je suis Data Scientist à l'Institut National de Recherche en Agronomie (INRA). - UdemyFreebies.com Amandine Velt | Data Science - Python - R, Récupérer des données et les intégrer dans son environnement de travail, Connaitre les différentes structures de données (listes, dictionnaires, tableaux, dataframes, ...), Manipuler efficacement des données avec Python et la librairie Pandas, Explorer et visualiser des données avec Matplotlib et Seaborn, Utiliser Python pour faire de la data science, Installation de l'environnement de travail (Windows, Mac, Linux), Les bibliothèques Python essentielles à la Data Science, Prise en main du tableau de bord de Jupyter Notebook, Prise en main des notebooks de Jupyter Notebooks, Importer une bibliothèque et utiliser ses fonctions, Testez vos connaissance sur les bases de Python, Accéder aux éléments d'une liste (slicing), Ajouter/modifier/supprimer des éléments d'une liste, Diverses manipulations des listes (tri, somme, concaténation, ...), Introduction de l'exercice : manipuler la liste des prix de 58 maisons, Correction de l'exercice : manipuler la liste des prix de 58 maisons, Ajouter/modifier/supprimer des éléments d'un dictionnaire, Introduction : exercice sur les dictionnaires, Correction : exercice sur les dictionnaires, Accéder aux éléments d'un array (slicing), Exercice : manipuler un array de 20 maisons vendues en 2008 aux Etats-Unis, Correction : manipuler un array de 20 maisons vendues en 2008 aux Etats-Unis, Utilisation de la bibliothèque Pandas pour manipuler les données, Ajouter/supprimer des colonnes d'un dataframe, Filtrer un Dataframe selon des conditions, Grouper un Dataframe sur une ou plusieurs colonnes (groupby), Introduction de l'exercice : étudier le marché Android via le Google play store, Correction de l'exercice : étudier le marché Android via le Google play store, Utilisation de Matplotlib et Seaborn pour la visualisation de données, Importation et description des données de vente du Black Friday, Ajouter un titre principal et des labels aux axes, Changer les couleurs (et colorer selon certaines variables), Exercice : visualiser les données de vente d'un magasin le jour du Black Friday, Correction : visualiser les données d'un magasin le jour du Black Friday, Coupon : apprendre la Data Science avec R de A à Z, Mon livre aux éditions ENI : Python pour la Data Science, AWS Certified Solutions Architect - Associate, Les personnes souhaitant apprendre à programmer en Python, Les personnes souhaitant apprendre à faire de la Data Science avec Python, Les personnes intéressées par la Data Science en général. 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Le but de ce cours de Data Science est réellement de vous apprendre à programmer en Python, de vous faire pratiquer afin de devenir totalement autonome pour analyser tous les jeux de données qui vous intéresse. Currently he works as the Head of Data Science for Pierian Data Inc. and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Here a just a few of the topics we will be learning: Enroll in the course and become a data scientist today! Find the best data science course for your level and needs and accelerate your data science career Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science and programming. However, this Python programming course is designed specifically to cater to the needs of the Machine Learning or Data Science learner. What you'll learn. Spotle Learn. Any student wishing to pursue a career in the field of Data Science; Anyone who wants to approach this new field for work or personal growth; Created by AI 4 MY Last updated 10/2018 English English [Auto-generated] Size: 3.67 GB. Python is the most important language in the field of data, and its libraries for analysis and modeling are the most relevant tools to use. You will learn about variables, data types, data structures (lists, sets, tuples, dictionaries), decision and looping structures, and functions. It covers all the most important topics in machine learning, and gives just enough theoretical knowledge to have some basic understanding of the algorithms behind the scenes. 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