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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


  • Status: Free Trial
    Free Trial
    D

    DeepLearning.AI

    Mathematics for Machine Learning and Data Science

    Skills you'll gain: Descriptive Statistics, Bayesian Statistics, Statistical Hypothesis Testing, Probability & Statistics, Sampling (Statistics), Statistical Methods, Probability Distribution, Linear Algebra, Statistical Inference, Model Optimization, Machine Learning Methods, Statistics, A/B Testing, Applied Mathematics, Probability, Calculus, Dimensionality Reduction, Applied Machine Learning, Data Manipulation, Machine Learning

    4.6
    Rating, 4.6 out of 5 stars
    ·
    3.2K reviews

    Intermediate · Specialization · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    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, Analysis, Data Modeling

    4.4
    Rating, 4.4 out of 5 stars
    ·
    800 reviews

    Advanced · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    I

    Imperial College London

    Mathematics for Machine Learning

    Skills you'll gain: Dimensionality Reduction, Linear Algebra, Regression Analysis, NumPy, Calculus, Unsupervised Learning, Applied Mathematics, Statistical Methods, Descriptive Statistics, Model Optimization, Mathematical Software, Machine Learning Methods, Jupyter, Statistics, Numerical Analysis, Applied Machine Learning, Geometry, Data Science, Data Manipulation, Data Transformation

    4.6
    Rating, 4.6 out of 5 stars
    ·
    15K reviews

    Beginner · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    U

    University of Pittsburgh

    Mathematical Foundations for Data Science and Analytics

    Skills you'll gain: Statistical Analysis, NumPy, Probability Distribution, Matplotlib, Plot (Graphics), Statistics, Data Science, Probability & Statistics, Probability, Statistical Modeling, Predictive Modeling, Data Analysis, Linear Algebra, Predictive Analytics, Statistical Methods, Mathematics and Mathematical Modeling, Applied Mathematics, Python Programming, Machine Learning, Logical Reasoning

    3.8
    Rating, 3.8 out of 5 stars
    ·
    12 reviews

    Beginner · Specialization · 1 - 3 Months

  • I

    IIT Guwahati

    Bachelor of Science in Data Science & AI

    Skills you'll gain: Package and Software Management, Time Series Analysis and Forecasting, Cloud Computing Architecture, Linear Algebra, Data Structures, R (Software), Data Warehousing, Database Design, Model Evaluation, Database Management, Social Network Analysis, Algorithms, Java Programming, Deep Learning, Portfolio Management, Oral Comprehension, C (Programming Language), Econometrics, Reinforcement Learning, Spreadsheet Software

    Earn a degree

    Degree · 1 - 4 Years

  • U

    University of Huddersfield

    BSc Data Science

    Skills you'll gain: Linear Algebra, Graph Theory, Statistical Software, Computational Logic, Integral Calculus, Matplotlib, Probability & Statistics, Statistical Programming, Plot (Graphics), Calculus, R Programming, Differential Equations, Data Processing, Data Visualization Software, Applied Mathematics, Theoretical Computer Science, Logical Reasoning, General Mathematics, Advanced Mathematics, Mathematical Modeling

    Earn a degree

    Degree · 1 - 4 Years

What brings you to Coursera today?

  • Status: New
    New
    Status: Free Trial
    Free Trial
    P

    Packt

    Data Science with Python

    Skills you'll gain: Plotly, PyTorch (Machine Learning Library), NumPy, Matplotlib, Pandas (Python Package), Plot (Graphics), Data Visualization Software, Interactive Data Visualization, Machine Learning Methods, Python Programming, Applied Machine Learning, Scatter Plots, Numerical Analysis, Data Manipulation, Deep Learning, Image Analysis, Linear Algebra, Data Wrangling

    Intermediate · Course · 1 - 3 Months

  • Status: New
    New
    Status: Free Trial
    Free Trial
    J

    John Wiley & Sons

    Foundations of Data Science and Statistical Methods

    Skills you'll gain: Statistical Methods, Exploratory Data Analysis, Data Quality, Statistics, Data Analysis, Data Science, Statistical Analysis, Probability & Statistics, Data Storage, Data Collection, Data Management, Data Pipelines, Statistical Machine Learning, Data-Driven Decision-Making, Applied Mathematics, Probability Distribution, Machine Learning, Linear Algebra

    Beginner · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    H

    Howard University

    Introduction to Linear Algebra and Python

    Skills you'll gain: Linear Algebra, Data Visualization, Applied Mathematics, Plot (Graphics), Scientific Visualization, NumPy, Mathematical Software, Algebra, Jupyter, Data Analysis Software, Mathematical Modeling, Computer Programming Tools, Data Science, Software Development Tools, Python Programming, Software Installation, Data Manipulation, Git (Version Control System), Version Control

    4.4
    Rating, 4.4 out of 5 stars
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    16 reviews

    Beginner · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    J

    Johns Hopkins University

    Linear Algebra from Elementary to Advanced

    Skills you'll gain: Linear Algebra, Algebra, Applied Mathematics, Advanced Mathematics, Artificial Intelligence and Machine Learning (AI/ML), Engineering Analysis, Mathematical Theory & Analysis, Geometry, Applied Machine Learning, Markov Model

    4.7
    Rating, 4.7 out of 5 stars
    ·
    234 reviews

    Beginner · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    U

    University of Colorado Boulder

    Computer Vision

    Skills you'll gain: Image Analysis, Computer Vision, Autoencoders, Convolutional Neural Networks, Vision Transformer (ViT), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Deep Learning, Generative Model Architectures, Artificial Intelligence and Machine Learning (AI/ML), Computer Graphics, Visualization (Computer Graphics), Machine Learning Methods, Model Deployment, Embeddings, Artificial Intelligence, Data Ethics, Data Processing, Applied Machine Learning, Linear Algebra

    Build toward a degree

    4.5
    Rating, 4.5 out of 5 stars
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    89 reviews

    Intermediate · Specialization · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    J

    Johns Hopkins University

    GPU Programming

    Skills you'll gain: Artificial Neural Networks, Image Analysis, Event-Driven Programming, Program Development, Scalability, Applied Machine Learning, Deep Learning, C and C++, Memory Management, Performance Tuning, Software Development, C++ (Programming Language), Dataflow, Digital Signal Processing, Linear Algebra, Distributed Computing, System Programming, Hardware Architecture, Mathematical Software, Machine Learning

    Build toward a degree

    3
    Rating, 3 out of 5 stars
    ·
    206 reviews

    Intermediate · Specialization · 3 - 6 Months

1234…14

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

  • Mathematics for Machine Learning and Data Science: DeepLearning.AI
  • Advanced Statistics for Data Science: Johns Hopkins University
  • Mathematics for Machine Learning: Imperial College London
  • Mathematical Foundations for Data Science and Analytics: University of Pittsburgh
  • Bachelor of Science in Data Science & AI: IIT Guwahati
  • BSc Data Science: University of Huddersfield
  • Data Science with Python: Packt
  • Foundations of Data Science and Statistical Methods: John Wiley & Sons
  • Introduction to Linear Algebra and Python: Howard University
  • Linear Algebra from Elementary to Advanced: Johns Hopkins University

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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