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.
Johns Hopkins University
Skills you'll gain: Shiny (R Package), Rmarkdown, Exploratory Data Analysis, Model Evaluation, Regression Analysis, Version Control, Statistical Analysis, R Programming, Data Manipulation, Data Cleansing, Data Science, Statistical Inference, Predictive Modeling, Statistical Hypothesis Testing, Machine Learning Algorithms, Plotly, Plot (Graphics), Interactive Data Visualization, Machine Learning, GitHub
Beginner · Specialization · 3 - 6 Months

Johns Hopkins University
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 Science, Statistics, Mathematical Modeling, Data Analysis, Data Modeling, Applied Mathematics
Advanced · Specialization · 3 - 6 Months

Skills you'll gain: Rmarkdown, Ggplot2, R (Software), R Programming, Data Analysis, Tidyverse (R Package), Data Visualization, Statistical Programming, Data Visualization Software, Data Cleansing, Data Manipulation, Data Import/Export, Package and Software Management, Data Structures
Beginner · Course · 1 - 3 Months
Johns Hopkins University
Skills you'll gain: Rmarkdown, Exploratory Data Analysis, Version Control, Statistical Analysis, R Programming, Data Manipulation, Data Cleansing, Data Science, Plot (Graphics), Statistical Programming, Statistical Visualization, Ggplot2, R (Software), Dimensionality Reduction, Data Visualization Software, Data Integration, Knitr, Data Sharing, GitHub, Machine Learning
Beginner · Specialization · 3 - 6 Months

Johns Hopkins University
Skills you'll gain: Shiny (R Package), Rmarkdown, Model Evaluation, Regression Analysis, Exploratory Data Analysis, Statistical Inference, Predictive Modeling, Statistical Hypothesis Testing, Machine Learning Algorithms, Plotly, Interactive Data Visualization, Probability & Statistics, Statistical Machine Learning, Data Presentation, Data Visualization, Statistical Analysis, Statistical Modeling, R Programming, Machine Learning, GitHub
Intermediate · Specialization · 3 - 6 Months

Johns Hopkins University
Skills you'll gain: Bioinformatics, Unix Commands, Biostatistics, Exploratory Data Analysis, Statistical Analysis, Unix, Data Science, Data Management, Statistical Methods, Command-Line Interface, Statistical Hypothesis Testing, Linux Commands, Data Analysis Software, Data Quality, Data Structures, Data Analysis, Computer Science, Molecular Biology, R Programming, Python Programming
Intermediate · Specialization · 3 - 6 Months

University of Colorado System
Skills you'll gain: Tidyverse (R Package), Clinical Data Management, Extract, Transform, Load, Data Modeling, Clinical Research, Health Insurance Portability And Accountability Act (HIPAA) Compliance, Medical Privacy, Data Manipulation, Predictive Modeling, Database Design, Google Cloud Platform, Intensive Care Unit, Data Ethics, Data Quality, Natural Language Processing, Bioinformatics, Informatics, Text Mining, Clinical Informatics, R Programming
Intermediate · Specialization · 3 - 6 Months

Skills you'll gain: Jupyter, Computer Programming Tools, Data Visualization Software, Data Science, GitHub, R (Software), Big Data, R Programming, Model Deployment, Application Programming Interface (API), Machine Learning, Cloud Computing, Git (Version Control System), Other Programming Languages, Version Control, Python Programming, Open Source Technology
Beginner · Course · 1 - 3 Months

Illinois Tech
Skills you'll gain: Model Evaluation, Statistical Analysis, Bayesian Statistics, Data Visualization, Statistical Inference, Exploratory Data Analysis, Data Cleansing, Regression Analysis, Analytics, Data Analysis, Logistic Regression, Machine Learning, R Programming, Data Preprocessing, Statistical Modeling, Data Validation, R (Software), Data Mining, Statistical Programming, Predictive Modeling
Build toward a degree
Intermediate · Specialization · 3 - 6 Months

University of Colorado Boulder
Skills you'll gain: Statistical Inference, Statistical Modeling, Statistical Hypothesis Testing, Regression Analysis, R Programming, Data Ethics, Research Design, Statistical Methods, Data Science, Data Analysis, Statistical Analysis, Data Modeling, Predictive Modeling, Mathematical Modeling, Probability & Statistics, Quantitative Research, Machine Learning, Model Evaluation, Logistic Regression, Feature Engineering
Build toward a degree
Intermediate · Specialization · 3 - 6 Months

Johns Hopkins University
Skills you'll gain: Statistical Analysis, R Programming, Statistical Programming, Data Analysis, Debugging, Simulations, Computer Programming Tools, Programming Principles, Data Manipulation, Data Structures, Performance Tuning, Data Import/Export
Intermediate · Course · 1 - 4 Weeks

University of Washington
Skills you'll gain: Feature Engineering, Unsupervised Learning, Data Ethics, Statistical Visualization, Data Storytelling, Big Data, Relational Databases, Supervised Learning, Database Systems, Dataflow, Data Visualization Software, Databases, Statistical Machine Learning, Model Evaluation, Predictive Analytics, R Programming, Data Analysis, SQL, Predictive Modeling, Python Programming
Intermediate · 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.‎