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.

Skills you'll gain: Data Literacy, Data Mining, Data Processing, Big Data, Cloud Computing, Data Science, Digital Transformation, Data-Driven Decision-Making, Data Storage, Deep Learning, Machine Learning
★ 4.7 (78K) · Beginner · Course · 1 - 4 Weeks

Skills you'll gain: Generative AI, Model Evaluation, Supervised Learning, Generative Model Architectures, AWS SageMaker, Recurrent Neural Networks (RNNs), Unsupervised Learning, Data Preprocessing, Large Language Modeling, Time Series Analysis and Forecasting, LLM Application, Applied Machine Learning, Model Optimization, Deep Learning, A/B Testing, Python Programming, Machine Learning, Data Analysis, AWS CloudFormation, AI Personalization
★ 4.2 (35) · Intermediate · Specialization · 3 - 6 Months

John Wiley & Sons
Skills you'll gain: Statistics, Probability & Statistics, Data Analysis, Statistical Methods, Data Literacy, Data Collection, Unsupervised Learning, Text Mining, Analytical Skills, Statistical Inference, Unstructured Data, Probability, Predictive Modeling, Data Science, Deep Learning, Machine Learning, Machine Learning Methods, Data Visualization, Business Communication, Communication
Intermediate · Course · 3 - 6 Months

Packt
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

Skills you'll gain: Unsupervised Learning, Exploratory Data Analysis, Autoencoders, Feature Engineering, Dimensionality Reduction, Supervised Learning, Generative AI, Classification Algorithms, Regression Analysis, Time Series Analysis and Forecasting, Recurrent Neural Networks (RNNs), Convolutional Neural Networks, Reinforcement Learning, Generative Adversarial Networks (GANs), Generative Model Architectures, Artificial Intelligence and Machine Learning (AI/ML), Deep Learning, Data Science, Machine Learning, Python Programming
★ 4.6 (3.6K) · Intermediate · Professional Certificate · 3 - 6 Months

Illinois Tech
Skills you'll gain: NoSQL, Machine Learning Algorithms, Big Data, Model Evaluation, Apache Hadoop, Recurrent Neural Networks (RNNs), Deep Learning, Statistical Analysis, Data Visualization, Data Analysis, Exploratory Data Analysis, Apache Spark, Statistical Methods, Data Presentation, Generative AI, Convolutional Neural Networks, Transfer Learning, Analysis, Data Infrastructure, Machine Learning
★ 4.5 (40) · Intermediate · Specialization · 3 - 6 Months

Duke University
Skills you'll gain: Deep Learning, MLOps (Machine Learning Operations), Responsible AI, Model Evaluation, Data Ethics, Data Management, Experience Design, Unsupervised Learning, Model Training, User Experience Design, Data Quality, Model Deployment, Data Preprocessing, Human Centered Design, Machine Learning, Regression Analysis, Project Management, Product Management, Product Design, Data Science
★ 4.7 (1.1K) · Beginner · Specialization · 3 - 6 Months

Skills you'll gain: PyTorch (Machine Learning Library), Transfer Learning, Model Evaluation, Fine-tuning, Vision Transformer (ViT), Keras (Neural Network Library), Deep Learning, Convolutional Neural Networks, Reinforcement Learning, Model Optimization, Autoencoders, Generative AI, Model Training, Unsupervised Learning, Tensorflow, Artificial Neural Networks, Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Statistical Methods, Logistic Regression
★ 4.5 (4.2K) · Intermediate · Professional Certificate · 3 - 6 Months

Skills you'll gain: PySpark, Databricks, Apache Spark, MLOps (Machine Learning Operations), Microsoft Azure, Big Data, Data Lakes, Model Training, Machine Learning Methods, Data Processing, Deep Learning, Data Transformation, Model Deployment, Data Pipelines, Data Manipulation, Model Evaluation, Machine Learning, Distributed Computing, Exploratory Data Analysis
★ 3.1 (79) · Intermediate · Course · 1 - 3 Months

Skills you'll gain: Model Evaluation, Keras (Neural Network Library), Model Training, Tensorflow, Image Analysis, Artificial Neural Networks, Applied Machine Learning, Network Model, Deep Learning, Computer Vision, Machine Learning, Network Architecture
★ 4.6 (848) · Beginner · Guided Project · Less Than 2 Hours

Skills you'll gain: Model Optimization, Image Analysis, Transfer Learning, Fine-tuning, Multimodal Prompts, Model Evaluation, Technical Documentation, Data Preprocessing, Debugging, Computer Vision, PyTorch (Machine Learning Library), Feature Engineering, Tensorflow, Model Training, Embeddings, Systems Design, Ethical Standards And Conduct, Large Language Modeling, Root Cause Analysis, Deep Learning
Intermediate · Specialization · 3 - 6 Months

Skills you'll gain: Prompt Engineering, Prompt Patterns, Generative AI, Generative Model Architectures, ChatGPT, Generative Adversarial Networks (GANs), Responsible AI, OpenAI, Data Ethics, AI literacy, Large Language Modeling, Leadership and Management, AI Enablement, Business Leadership, Strategic Leadership, Business Ethics, Hugging Face, Legal Risk, Artificial Intelligence and Machine Learning (AI/ML), Deep Learning
★ 4.7 (12K) · Beginner · Specialization · 3 - 6 Months
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:
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.‎