- Data Science
- Relational Database Management System (RDBMS)
- Cloud Databases
- Python Programming
- SQL
- Deep Learning
- Machine Learning
- Big Data
- Data Mining
- Github
- Jupyter notebooks
- Rstudio
Spécialisation Introduction à la science des données
Lancez votre carrière en science des données. Gain foundational data science skills to prepare for a career or further advanced learning in data science.
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Spécialisation Introduction à la science des donnéesRéseau de compétences IBMCe que vous allez apprendre
Describe what data science and machine learning are, their applications & use cases, and various types of tasks performed by data scientists
Gain hands-on familiarity with common data science tools including JupyterLab, R Studio, GitHub and Watson Studio
Develop the mindset to work like a data scientist, and follow a methodology to tackle different types of data science problems
Write SQL statements and query Cloud databases using Python from Jupyter notebooks
Compétences que vous acquerrez
À propos de ce Spécialisation
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Interested in learning more about data science, but don’t know where to start? This 4-course Specialization from IBM will provide you with the key foundational skills any data scientist needs to prepare you for a career in data science or further advanced learning in the field.
This Specialization will introduce you to what data science is and what data scientists do. You’ll discover the applicability of data science across fields, and learn how data analysis can help you make data driven decisions. You’ll find that you can kickstart your career path in the field without prior knowledge of computer science or programming languages: this Specialization will give you the foundation you need for more advanced learning to support your career goals.
You’ll grasp concepts like big data, statistical analysis, and relational databases, and gain familiarity with various open source tools and data science programs used by data scientists, like Jupyter Notebooks, RStudio, GitHub, and SQL. You'll complete hands-on labs and projects to learn the methodology involved in tackling data science problems and apply your newly acquired skills and knowledge to real world data sets.
In addition to earning a Specialization completion certificate from Coursera, you’ll also receive a digital badge from IBM recognizing you as a specialist in data science foundations.
This Specialization can also be applied toward the IBM Data Science Professional Certificate.
Projet d'apprentissage appliqué
All courses in the specialization contain multiple hands-on labs and assignments to help you gain practical experience and skills with a variety of data sets and tools like Jupyter, GitHub, and R Studio. Build your data science portfolio from the artifacts you produce throughout this program. Course-culminating projects include:
Creating and sharing a Jupyter Notebook containing code blocks and markdown
Devising a problem that can be solved by applying the data science methodology and explain how to apply each stage of the methodology to solve it
Using SQL to query census, crime, and demographic data sets to identify causes that impact enrollment, safety, health, and environment ratings in schools
Certificat partageable
Obtenez un Certificat lorsque vous terminez
Cours en ligne à 100 %
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Planning flexible
Définissez et respectez des dates limites flexibles.
Niveau débutant
Aucune connaissance prérequise.
Approximativement 4 mois pour terminer
Rythme recommandé de 5 heures/semaine
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Sous-titres : Anglais, Arabe, Français, Portugais (européen), Italien, Vietnamien, Allemand, Russe, Turc, Espagnol, Persan
Votre entreprise pourrait-elle bénéficier de la formation des employés à des compétences recherchées ?
Essayez Coursera pour les affairesCertificat partageable
Obtenez un Certificat lorsque vous terminez
Cours en ligne à 100 %
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Planning flexible
Définissez et respectez des dates limites flexibles.
Niveau débutant
Aucune connaissance prérequise.
Approximativement 4 mois pour terminer
Rythme recommandé de 5 heures/semaine
Anglais
Sous-titres : Anglais, Arabe, Français, Portugais (européen), Italien, Vietnamien, Allemand, Russe, Turc, Espagnol, Persan
Votre entreprise pourrait-elle bénéficier de la formation des employés à des compétences recherchées ?
Essayez Coursera pour les affairesComment fonctionne la Spécialisation
Suivez les cours
Une Spécialisation Coursera est une série de cours axés sur la maîtrise d'une compétence. Pour commencer, inscrivez-vous directement à la Spécialisation ou passez en revue ses cours et choisissez celui par lequel vous souhaitez commencer. Lorsque vous vous abonnez à un cours faisant partie d'une Spécialisation, vous êtes automatiquement abonné(e) à la Spécialisation complète. Il est possible de terminer seulement un cours : vous pouvez suspendre votre formation ou résilier votre abonnement à tout moment. Rendez-vous sur votre tableau de bord d'étudiant pour suivre vos inscriptions aux cours et vos progrès.
Projet pratique
Chaque Spécialisation inclut un projet pratique. Vous devez réussir le(s) projet(s) pour terminer la Spécialisation et obtenir votre Certificat. Si la Spécialisation inclut un cours dédié au projet pratique, vous devrez terminer tous les autres cours avant de pouvoir le commencer.
Obtenir un Certificat
Lorsque vous aurez terminé tous les cours et le projet pratique, vous obtiendrez un Certificat que vous pourrez partager avec des employeurs éventuels et votre réseau professionnel.
Ce Spécialisation compte 4 cours
Qu'est-ce que la science des données ?
57,216 évaluations
The art of uncovering the insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and they accurately predicted the flooding of the Nile river every year. Since then, people working in data science have carved out a unique and distinct field for the work they do. This field is data science. In this course, we will meet some data science practitioners and we will get an overview of what data science is today.
Tools for Data Science
24,661 évaluations
What are some of the most popular data science tools, how do you use them, and what are their features? In this course, you'll learn about Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. You will learn about what each tool is used for, what programming languages they can execute, their features and limitations. With the tools hosted in the cloud on Skills Network Labs, you will be able to test each tool and follow instructions to run simple code in Python, R or Scala. To end the course, you will create a final project with a Jupyter Notebook on IBM Watson Studio and demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers.
Data Science Methodology
18,053 évaluations
Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision making process is either lost or not maximized at all too often, we don't have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand.
Databases and SQL for Data Science with Python
15,903 évaluations
Much of the world's data resides in databases. SQL (or Structured Query Language) is a powerful language which is used for communicating with and extracting data from databases. A working knowledge of databases and SQL is a must if you want to become a data scientist.
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Réseau de compétences IBM
IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.
Foire Aux Questions
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