We may earn an affiliate commission when you visit our partners.
Thierno Ibrahima Diop
Dans ce projet guidé, vous allez charger, nettoyer et explorer des données de produits alimentaires issues de la base de données Open Food Facts. Vous allez d’abord vous familiariser avec Jupyter, lire les données, analyser les valeurs manquantes, nettoyer les données en se basant sur les connaissances métiers mais aussi sur des techniques statistiques, vous allez ensuite remplir les valeurs manquantes.
Pour réaliser cette analyse, vous allez utiliser JupyterLab avec les librairies data science en python telles que Pandas, Matplotlib, SeaBorn et missigno.
Register for this course and see more details by visiting:
OpenCourser.com/course/bdtv2v/nettoyer
What's inside
Syllabus
Project Overview
Dans ce projet guidé, vous allez charger, nettoyer et explorer des données de produits alimentaires issues de la base de données Open Food Facts. Vous allez d’abord vous familiariser avec Jupyter, lire les données, analyser les valeurs manquantes, nettoyer les données en se basant sur les connaissances métiers mais aussi sur des techniques statistiques, vous allez ensuite remplir les valeurs manquantes. Pour réaliser ce projet, vous allez utiliser JupyterLab avec des librairies data science en python telles que Pandas, Matplotlib et SeaBorn.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Vous donne les connaissances et les outils pour charger, nettoyer et explorer des données alimentaires à partir de sources ouvertes
Approfondit la manipulation et l'analyse de données en Python
Développe des compétences en matière de traitement des données manquantes
Save this course
Save Nettoyer vos données avec Python to your list so you can find it easily later:
Save
Save
Reviews summary
Jupyter for python data cleaning
This course is a 5-star rated data cleaning course that uses Jupyter notebooks and Python. Students found the course to be clear and well-taught.
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Nettoyer vos données avec Python with these
activities:
Review basics of Jupyter
Show steps
Familiarize yourself with the Jupyter environment to make the most of the course.
Browse courses on
Jupyter
Show steps
-
Review the Jupyter Notebook interface and basic commands.
-
Practice loading a sample dataset into a Jupyter Notebook.
-
Execute basic data manipulation commands in Jupyter.
Participate in a study group
Show steps
Enhance understanding through collaborative learning and discussions.
Show steps
-
Form a study group with classmates.
-
Meet regularly to discuss course materials and assignments.
-
Share knowledge and insights with peers.
Practice data cleaning techniques
Show steps
Improve your data cleaning skills through repetitive exercises.
Browse courses on
Data Cleaning
Show steps
-
Identify and remove duplicate rows.
-
Handle missing values using Pandas and missingno library.
-
Clean data based on business rules and statistical techniques.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Attend a data science workshop
Show steps
Enhance your data science knowledge and skills through structured workshops.
Browse courses on
Data Science
Show steps
-
Identify relevant workshops and attend them.
-
Participate actively in discussions and exercises.
-
Apply workshop learnings to your coursework.
Explore advanced data visualization techniques
Show steps
Expand your data visualization capabilities by following guided tutorials.
Browse courses on
Data Visualization
Show steps
-
Create interactive data visualizations using SeaBorn.
-
Generate complex plots using Matplotlib.
-
Explore advanced visualization techniques.
Develop a data analysis report
Show steps
Demonstrate your data analysis skills by creating a comprehensive report.
Browse courses on
Data Analysis
Show steps
-
Define the problem and research question.
-
Analyze the data using appropriate techniques.
-
Develop insights and conclusions.
-
Communicate findings effectively in a written report.
Contribute to an open-source data science project
Show steps
Gain practical experience by contributing to the data science community.
Browse courses on
Open Source
Show steps
-
Identify suitable open-source data science projects.
-
Explore the codebase and understand the project's goals.
-
Make meaningful contributions to the project.
Review basics of Jupyter
Show steps
Familiarize yourself with the Jupyter environment to make the most of the course.
Browse courses on
Jupyter
Show steps
Show steps
Show steps
- Review the Jupyter Notebook interface and basic commands.
- Practice loading a sample dataset into a Jupyter Notebook.
- Execute basic data manipulation commands in Jupyter.
Participate in a study group
Show steps
Enhance understanding through collaborative learning and discussions.
Show steps
Show steps
Show steps
- Form a study group with classmates.
- Meet regularly to discuss course materials and assignments.
- Share knowledge and insights with peers.
Practice data cleaning techniques
Show steps
Improve your data cleaning skills through repetitive exercises.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Identify and remove duplicate rows.
- Handle missing values using Pandas and missingno library.
- Clean data based on business rules and statistical techniques.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Attend a data science workshop
Show steps
Enhance your data science knowledge and skills through structured workshops.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Identify relevant workshops and attend them.
- Participate actively in discussions and exercises.
- Apply workshop learnings to your coursework.
Explore advanced data visualization techniques
Show steps
Expand your data visualization capabilities by following guided tutorials.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Create interactive data visualizations using SeaBorn.
- Generate complex plots using Matplotlib.
- Explore advanced visualization techniques.
Develop a data analysis report
Show steps
Demonstrate your data analysis skills by creating a comprehensive report.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Define the problem and research question.
- Analyze the data using appropriate techniques.
- Develop insights and conclusions.
- Communicate findings effectively in a written report.
Contribute to an open-source data science project
Show steps
Gain practical experience by contributing to the data science community.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify suitable open-source data science projects.
- Explore the codebase and understand the project's goals.
- Make meaningful contributions to the project.
Career center
Learners who complete Nettoyer vos données avec Python will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist is responsible for collecting, cleaning, analyzing data, and presenting actionable insights to make informed decisions. This course would help build a foundation in data cleaning techniques, which is a critical skill for Data Scientists. The course also covers data exploration and visualization, which are essential for presenting insights effectively.
Data Analyst
Data Analyst
A Data Analyst is responsible for gathering, cleaning, and analyzing data to identify trends and patterns. This course would provide a strong foundation in data cleaning techniques, which is essential for Data Analysts. The course also covers data exploration and visualization, which are important skills for presenting insights.
Business Analyst
Business Analyst
A Business Analyst is responsible for analyzing business processes and identifying areas for improvement. This course would provide a foundation in data cleaning techniques, which can be useful for Business Analysts who need to clean data for analysis. The course also covers data exploration and visualization, which can be helpful for presenting insights to stakeholders.
Statistician
Statistician
A Statistician is responsible for collecting, analyzing, and interpreting data to make informed decisions. This course would provide a foundation in data cleaning techniques, which is essential for Statisticians. The course also covers data exploration and visualization, which are important skills for presenting insights.
Software Engineer
Software Engineer
A Software Engineer is responsible for designing, developing, and maintaining software applications. This course would provide a foundation in data cleaning techniques, which can be helpful for Software Engineers who need to clean data for analysis or integration. The course also covers data exploration and visualization, which can be useful for debugging and testing software.
Data Engineer
Data Engineer
A Data Engineer is responsible for building and maintaining data pipelines and infrastructure. This course would provide a foundation in data cleaning techniques, which is essential for Data Engineers. The course also covers data exploration and visualization, which can be useful for understanding data and designing data pipelines.
Product Manager
Product Manager
A Product Manager is responsible for managing the development and launch of new products. This course may be useful for Product Managers who need to understand data cleaning techniques for product development and market research.
Marketing Analyst
Marketing Analyst
A Marketing Analyst is responsible for analyzing marketing data to identify trends and opportunities. This course may be useful for Marketing Analysts who need to understand data cleaning techniques for data analysis and reporting.
Financial Analyst
Financial Analyst
A Financial Analyst is responsible for analyzing financial data to make investment recommendations. This course may be useful for Financial Analysts who need to understand data cleaning techniques for data analysis and reporting.
Operations Manager
Operations Manager
An Operations Manager is responsible for overseeing the day-to-day operations of a business. This course may be useful for Operations Managers who need to understand data cleaning techniques for data analysis and process improvement.
Customer Success Manager
Customer Success Manager
A Customer Success Manager is responsible for ensuring that customers are satisfied with a company's products or services. This course may be useful for Customer Success Managers who need to understand data cleaning techniques for data analysis and customer feedback.
Sales Manager
Sales Manager
A Sales Manager is responsible for leading and managing a sales team. This course may be useful for Sales Managers who need to understand data cleaning techniques for data analysis and sales forecasting.
For more career information including salaries, visit:
OpenCourser.com/course/bdtv2v/nettoyer
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Nettoyer vos données avec Python.
Ce livre fournit des recettes et des exemples pratiques pour nettoyer et manipuler des données avec Pandas, la bibliothèque de manipulation de données la plus populaire de Python.
Ce livre fournit une documentation complète sur les fonctionnalités de manipulation de données de Pandas, y compris le nettoyage et la transformation des données.
Ce livre fournit des solutions pratiques pour les tâches quotidiennes de nettoyage de données avec Python.
Save
Ce livre fournit une référence rapide et concise du langage Python, ce qui est utile pour une consultation rapide des syntaxes et des fonctions clés.
Save
Ce livre fournit une référence complète du langage Python, ce qui est utile pour rechercher des détails spécifiques sur la syntaxe et les fonctionnalités de Python.
Bien que NumPy ne soit pas spécifique au nettoyage de données, il est un élément essentiel de l'écosystème scientifique Python et fournit des fonctionnalités utiles pour la manipulation de données.
Ce livre fournit une introduction aux structures de données et aux algorithmes en Python, ce qui est utile pour comprendre les techniques sous-jacentes utilisées dans les bibliothèques de nettoyage de données.
Ce livre fournit une introduction au Big Data et à son analyse avec Python, ce qui peut être utile pour comprendre les défis et les techniques impliqués dans le nettoyage de données à grande échelle.
Ce livre fournit des recettes et des exemples pour créer des visualisations de données claires et concises avec Matplotlib, la bibliothèque de visualisation la plus populaire de Python.
Bien que ce livre ne soit pas spécifiquement dédié au nettoyage de données, il fournit une vue d'ensemble de la science des données et de son importance dans les entreprises, ce qui est utile pour comprendre le contexte du nettoyage de données.
Bien que ce livre ne soit pas spécifiquement dédié au nettoyage de données, il fournit une introduction à l'apprentissage automatique, qui peut être utile pour comprendre l'utilisation du nettoyage de données dans des applications pratiques.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/bdtv2v/nettoyer
Share
Similar courses
Here are nine courses similar to
Nettoyer vos données avec Python.
Créer une Fonction Personnalisée en JS et Google App Script
OpenCourser.com/course/n9pjwt/creer
Créer une Fonction Personnalisée en JS et Google App...
Most relevant
Comprendre la Syntaxe de Base de SQL.
OpenCourser.com/course/a9jmvt/comprendre
Comprendre la Syntaxe de Base de SQL.
Most relevant
Le nettoyage de données
OpenCourser.com/course/7at9uk/le
Le nettoyage de données
Most relevant
Apprivoiser l’apprentissage automatique
OpenCourser.com/course/ukoi4z/apprivoiser
Apprivoiser l’apprentissage automatique
Most relevant
Compétences Excel professionnelles : Intermédiaire II
OpenCourser.com/course/kl51wq/competences
Compétences Excel professionnelles : Intermédiaire II
Most relevant
Tensorflow : Analyse de Sentiments avec Word Embedding
OpenCourser.com/course/6nbsfy/tensorflow
Tensorflow : Analyse de Sentiments avec Word Embedding
Most relevant
Poser des questions pour prendre des décisions basées sur les données
OpenCourser.com/course/ve62ga/poser
Poser des questions pour prendre des décisions basées sur...
Most relevant
Modèles de séquence
OpenCourser.com/course/zrziy6/modeles
Modèles de séquence
Most relevant
Transformer les Données avec R
OpenCourser.com/course/u2gx95/transformer
Transformer les Données avec R
Most relevant
Effort
2 heures
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Thierno Ibrahima Diop
Language
Français
Good to know
Vous donne les connaissances et les outils pour charger, nettoyer et explorer des données alimentaires à partir de sources ouvertes
Approfondit la manipulation et l'analyse de données en Python
Développe des compétences en matière de traitement des données manquantes
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser