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Cours en Data Science

Les cours en data science peuvent vous aider à comprendre comment analyser des données, créer des modèles et évaluer leurs performances. Vous pouvez développer des compétences en statistique, apprentissage automatique, préparation des données et visualisation. De nombreux cours utilisent des langages et bibliothèques courants pour travailler sur des projets pratiques.


Cours et certificats populaires en Data Science


  • G

    Google

    Foundations of Data Science

    Skills you'll gain: Data Storytelling, Data Ethics, Data Analysis, Data-Driven Decision-Making, Analytics, Workflow Management, Data Science, Advanced Analytics, Analytical Skills, Business Solutions, Technical Communication, Process Design, Project Management, Communication, Stakeholder Communications, Machine Learning

    ★ 4.7 (3.9K) · Advanced · Course · 1 - 3 Months

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • G

    Google

    Google Advanced Data Analytics

    Skills you'll gain: Data Storytelling, Data Visualization, A/B Testing, Sampling (Statistics), Data Analysis, Exploratory Data Analysis, Regression Analysis, Data Visualization Software, Data Presentation, Data Ethics, Feature Engineering, Statistical Hypothesis Testing, Analytics, Statistical Analysis, Data Science, Tableau Software, Machine Learning, Object Oriented Programming (OOP), Web Presence, Python Programming

    ★ 4.8 (11K) · Advanced · Professional Certificate · 3 - 6 Months

    Status: Free Trial
    Free Trial
    Category: Build toward a degree
    Build toward a degree
  • J

    Johns Hopkins University

    Advanced Statistics for Data Science

    Skills you'll gain: Statistical Hypothesis Testing, Sampling (Statistics), Regression Analysis, Bayesian Statistics, Statistical Analysis, Probability & Statistics, Statistical Inference, Statistical Methods, Statistical Modeling, Linear Algebra, Probability, Probability Distribution, R Programming, Biostatistics, Data Analysis, Data Science, Statistics, Mathematical Modeling, Data Modeling, Applied Mathematics

    ★ 4.4 (797) · Advanced · Specialization · 3 - 6 Months

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • D

    Duke University

    MLOps | Machine Learning Operations

    Skills you'll gain: Fine-tuning, MLOps (Machine Learning Operations), Model Deployment, Cloud Deployment, Pandas (Python Package), AWS SageMaker, NumPy, Microsoft Azure, Hugging Face, GitHub Copilot, Unit Testing, Responsible AI, DevOps, Cloud Computing, Python Programming, Machine Learning, GitHub, Big Data, Data Management, Data Analysis

    ★ 4.2 (606) · Advanced · Specialization · 3 - 6 Months

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • G

    Google

    The Power of Statistics

    Skills you'll gain: A/B Testing, Sampling (Statistics), Data Analysis, Analytics, Statistics, Descriptive Statistics, Statistical Analysis, Statistical Hypothesis Testing, Probability & Statistics, Statistical Software, Advanced Analytics, Probability Distribution, Data Science, Statistical Inference, Statistical Programming, Statistical Methods, Probability, Python Programming

    ★ 4.8 (890) · Advanced · Course · 1 - 3 Months

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • U

    University of Michigan

    More Applied Data Science with Python

    Skills you'll gain: Unsupervised Learning, Data Mining, Social Network Analysis, ChatGPT, Embeddings, LLM Application, Applied Machine Learning, Data Quality, Unstructured Data, Anomaly Detection, Machine Learning Methods, Data Science, Supervised Learning, Machine Learning, Data Preprocessing, Data Analysis, Social Media Analytics, Data Manipulation, Python Programming, Exploratory Data Analysis

    ★ 4.6 (18) · Advanced · Specialization · 3 - 6 Months

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered

What brings you to Coursera today?

  • C

    Corporate Finance Institute

    Practical Data Science for Data Analysts

    Skills you'll gain: Classification Algorithms, Data Preprocessing, Feature Engineering, Data Visualization, Model Evaluation, Data Import/Export, Matplotlib, Exploratory Data Analysis, Data Presentation, Data Science, Plot (Graphics), Regression Analysis, Financial Data, Data Analysis, Business Analytics, Data Cleansing, Machine Learning Algorithms, Correlation Analysis, Advanced Analytics, Predictive Analytics

    ★ 4.8 (30) · Advanced · Specialization · 3 - 6 Months

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • G

    Google

    Google Business Intelligence

    Skills you'll gain: Data Modeling, Database Design, Stakeholder Engagement, Dashboard, Business Intelligence, Business Intelligence Software, Extract, Transform, Load, Dashboard Creation, Tableau Software, Databases, Data Warehousing, Stakeholder Communications, Data Visualization, Web Presence, Interviewing Skills, Business Process, Business Analysis, Professional Development, Data Analysis, SQL

    ★ 4.8 (8K) · Advanced · Professional Certificate · 3 - 6 Months

    Status: Free Trial
    Free Trial
    Category: Build toward a degree
    Build toward a degree
  • I

    IBM

    IBM AI Enterprise Workflow

    Skills you'll gain: Feature Engineering, Model Deployment, Data Ethics, Exploratory Data Analysis, Model Evaluation, Unsupervised Learning, Data Presentation, Tensorflow, Application Deployment, Dimensionality Reduction, MLOps (Machine Learning Operations), Model Training, Probability Distribution, Apache Spark, Statistical Hypothesis Testing, Design Thinking, Market Opportunities, Data Science, Machine Learning, Python Programming

    ★ 4.4 (366) · Advanced · Specialization · 3 - 6 Months

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • G

    Google Cloud

    Advanced Machine Learning on Google Cloud

    Skills you'll gain: Model Deployment, Model Optimization, Convolutional Neural Networks, Google Cloud Platform, Natural Language Processing, Tensorflow, MLOps (Machine Learning Operations), Large Language Modeling, Reinforcement Learning, Model Training, Transfer Learning, Computer Vision, Keras (Neural Network Library), Systems Design, Applied Machine Learning, Image Analysis, AI Personalization, Cloud Deployment, Recurrent Neural Networks (RNNs), Machine Learning

    ★ 4.5 (1.5K) · Advanced · Specialization · 3 - 6 Months

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • C

    Coursera

    Star Schemas to Snowflake: Data Modeling for Analytics Teams

    Skills you'll gain: Disaster Recovery, Star Schema, Business Continuity Planning, Cost Benefit Analysis, Database Design, Business Continuity, Data Infrastructure, Snowflake Schema, Database Architecture and Administration, Relational Databases, Data Warehousing, Data Modeling, Data Architecture, Data Validation, SQL, Terraform, Infrastructure as Code (IaC), Extract, Transform, Load, Business Analytics, Cost Management

    Advanced · Specialization · 3 - 6 Months

    Category: New
    New
    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • C

    Coursera

    Level Up: Advanced SQL for Data Engineering

    Skills you'll gain: Star Schema, Data Infrastructure, Data Governance, Data Modeling, Data Strategy, Data Architecture, Data Migration, Database Design, Correlation Analysis, Stored Procedure, Data Pipelines, Data Lakes, Infrastructure as Code (IaC), Data Validation, Disaster Recovery, Role-Based Access Control (RBAC), Compliance Auditing, CI/CD, Resource Management, Performance Management

    Advanced · Specialization · 3 - 6 Months

    Category: New
    New
    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
1234…58

In summary, here are 10 of our most popular data science courses

  • Foundations of Data Science: Google
  • Google Advanced Data Analytics: Google
  • Advanced Statistics for Data Science: Johns Hopkins University
  • MLOps | Machine Learning Operations: Duke University
  • The Power of Statistics: Google
  • More Applied Data Science with Python: University of Michigan
  • Practical Data Science for Data Analysts: Corporate Finance Institute
  • Google Business Intelligence: Google
  • IBM AI Enterprise Workflow: IBM
  • Advanced Machine Learning on Google Cloud: Google Cloud

Frequently Asked Questions about Data Science

Data science is an interdisciplinary field that combines statistics, computer science, and domain expertise to extract meaningful insights from data. It plays a crucial role in decision-making across various industries, helping organizations to understand trends, predict outcomes, and optimize processes. In today's data-driven world, the ability to analyze and interpret data is essential for businesses to remain competitive and innovative.‎

A career in data science can lead to various roles, including data analyst, data engineer, machine learning engineer, and data scientist. These positions are in high demand across sectors such as finance, healthcare, technology, and marketing. Each role focuses on different aspects of data, from data collection and cleaning to advanced analytics and predictive modeling, offering diverse opportunities for professionals.‎

To pursue a career in data science, you should develop a strong foundation in several key skills. These include programming languages like Python and R, statistical analysis, data visualization, and machine learning. Familiarity with databases and tools such as SQL and Tableau is also beneficial. Additionally, soft skills like problem-solving, critical thinking, and effective communication are essential for translating data insights into actionable strategies.‎

There are numerous online courses available for learning data science. Some of the best options include the IBM Data Science Professional Certificate, which covers essential skills and tools, and the Applied Data Science Specialization, which focuses on practical applications. These courses provide a structured learning path and hands-on experience to help you build your data science expertise.‎

Yes. You can start learning data science on Coursera for free in two ways:

  1. Preview the first module of many data science courses at no cost. This includes video lessons, readings, graded assignments, and Coursera Coach (where available).
  2. Start a 7-day free trial for Specializations or Coursera Plus. This gives you full access to all course content across eligible programs within the timeframe of your trial.

If you want to keep learning, earn a certificate in data science, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎

To learn data science effectively, start by identifying your learning goals and the specific skills you want to acquire. Begin with foundational courses that cover basic concepts and gradually progress to more advanced topics. Engage in hands-on projects to apply your knowledge, and consider joining online communities or study groups to enhance your learning experience. Consistent practice and real-world application are key to mastering data science.‎

Data science courses typically cover a range of topics, including data manipulation, statistical analysis, machine learning, data visualization, and big data technologies. You may also encounter specialized subjects such as natural language processing, data ethics, and data engineering. This comprehensive curriculum prepares you to tackle various challenges in the field and equips you with the skills needed to analyze complex datasets.‎

For training and upskilling employees in data science, programs like the CertNexus Certified Data Science Practitioner Professional Certificate and the Fractal Data Science Professional Certificate are excellent choices. These courses are designed to enhance practical skills and provide a solid foundation in data science, making them suitable for workforce development.‎

This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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