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.
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IBMÀ propos de ce cours
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Sous-titres : Arabe, Français, Portugais (européen), Italien, Vietnamien, Allemand, Russe, Anglais, Espagnol, Persan
Résultats de carrière des étudiants
12%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Niveau débutant
Approx. 18 heures pour terminer
Anglais
Sous-titres : Arabe, Français, Portugais (européen), Italien, Vietnamien, Allemand, Russe, Anglais, Espagnol, Persan
Offert par
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.
Programme du cours : ce que vous apprendrez dans ce cours
3 heures pour terminer
Data Scientist's Toolkit
In this module, you will get an overview of the programming languages commonly used, including Python, R, Scala, and SQL. You’ll be introduced to the open source and commercial data science tools available. You’ll also learn about the packages, APIs, data sets and models frequently used by data scientists.
3 heures pour terminer
17 vidéos (Total 84 min)
10 heures pour terminer
Open Source Tools
In this module, you will learn about three popular tools used in data science: GitHub, Jupyter Notebooks, and RStudio IDE. You will become familiar with the features of each tool, and what makes these tools so popular among data scientists today.
10 heures pour terminer
12 vidéos (Total 54 min)
4 heures pour terminer
IBM Tools for Data Science
In this module, you will learn about an enterprise-ready data science platform by IBM, called Watson Studio. You'll learn about some of the features and capabilities of what data scientists use in the industry. You’ll also learn about other IBM tools used to support data science projects, such as IBM Watson Knowledge Catalog, Data Refinery, and the SPSS Modeler.
4 heures pour terminer
15 vidéos (Total 72 min)
1 heure pour terminer
Final Assignment: Create and Share Your Jupyter Notebook
In this module, you will demonstrate your skills by creating and configuring a Jupyter Notebook. As part of your grade for this course, you will share your Jupyter Notebook with your peers for review.
1 heure pour terminer
1 lecture
Avis
Meilleurs avis pour TOOLS FOR DATA SCIENCE
par CB1 nov. 2018
Tools are fantastic and will make a significant contribution to my education. Videos need to be updated for changes to Watson Studios. Support from IBM on their cloud services should also be improved.
par NK19 mai 2019
The course video contents and the tools versions are not the same.There are some significant differences .Videos should be updated.In general the course is a good fundamental course about the tools.
par FD17 nov. 2020
Some of the lab assignments had instructions that didn't line up with how the programs actually worked. This was particularly the case for modular flow where auto-numerics seemed impossible to use.
par RR24 avr. 2019
To the contrast of other reviews, I find the content very well bifurcated and fed to the learners. The course very easily digestable and I have had a great amount of fun learning it.. Go for it!!!!
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