À propos de ce cours : 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, RStudio IDE, Apache Zeppelin and Data Science Experience. 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 Cognitive Class 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 Data Science Experience and demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.

Les destinataires de ce cours : This course is aimed any anyone in Data Science and has little or no knowledge about the tools used by Data Scientists.

Créé par : IBM

Enseigné par : Polong Lin, Data Scientist
IBM Developer Skills Network

Informations de base	Cours 2 de 9 dans IBM Data Science Professional Certificate Specialization
Niveau	Débutant
Engagement	3 weeks of study, 2-3 hours/week
Langue	Anglais
Comment réussir	Réussissez tous les devoirs notés pour terminer le cours.
Notes des utilisateurs	4.6 étoiles Note moyenne des utilisateurs 4.6Voir ce que disent les étudiants

Programme de cours

SEMAINE 1

Introducing Cognitive Class Labs

This week, you will get an overview of the various data science tools available to you, hosted on Cognitive Class Labs. You will create an account and start exploring some of the features.

4 vidéos, 1 quiz pour s'exercer

Vidéo: Account features
Vidéo: Creating an account
Vidéo: Managing data within My Data
LTI Item: Lab - Getting Started with Cognitive Class Labs
LTI Item: Lab - Exploring My Data on Cognitive Class Labs
Practice Quiz: Practice Quiz

Noté: Quiz 1 - Cognitive Class Labs

Jupyter Notebooks

This week, you will learn about a popular data science tool, Jupyter Notebooks, its features, and why they are so popular among data scientists today.

2 vidéos

Vidéo: Getting started with Jupyter Notebooks
LTI Item: Lab - Jupyter Notebooks - The Basics
LTI Item: Lab - Jupyter Notebooks - More Features
LTI Item: Lab - Jupyter Notebooks - Advanced Features
Discussion Prompt: Interesting Jupyter Notebooks on the Internet

Noté: Quiz 2 - Jupyter Notebooks

SEMAINE 2

Apache Zeppelin Notebooks

This week, you will learn about Apache Zeppelin Notebooks, its feature, and how they are different from Jupyter Notebooks.

3 vidéos

Vidéo: Zeppelin for Scala
Vidéo: Getting started with Zeppelin
LTI Item: Lab - Zeppelin Notebooks - The Basics
LTI Item: Lab - Zeppelin Notebooks - Tutorial

Noté: Quiz 3 - Zeppelin Notebooks

RStudio IDE

This week, you will learn about a popular data science tool used by R programmers. You'll learn about the user interface and how to use its various features.

4 vidéos

Vidéo: Uploading files, Installing packages and loading libraries in RStudio IDE
Vidéo: Getting started with RStudio IDE
Vidéo: RStudio Environment and History
LTI Item: Lab - RStudio - The Basics
LTI Item: Lab - RStudio - Creating an interactive map in R

Noté: Quiz 4 - RStudio IDE

SEMAINE 3

IBM Watson Studio

This week, you will learn about an enterprise-ready data science platform by IBM, called Watson Studio (formerley known as Data Science Experience). You'll learn about some of the features and capabilities of what data scientists use in the industry.

4 vidéos, 2 lectures

Reading: Preface: What is IBM Watson Studio (formerly known as Data Science Experience?
Vidéo: Creating an account on IBM Watson Studio (Data Science Experience)
Vidéo: Jupyter notebooks on Watson Studio (Data Science Experience) - Part 1/2
Vidéo: Jupyter Notebooks on IBM Watson Studio (Data Science Experience) - Part 2/2
Reading: Lab - IBM Watson Studio
LTI Item: OPTIONAL: IBM Cloud Promo Code

Noté: Quiz 5 - IBM Watson Studio

Project: Create and share a Jupyter Notebook

1 lecture

Reading: IBM Digital Badge

Noté: Create and share your Jupyter Notebook

FAQ

Quand aurai-je accès aux vidéos de cours et aux devoirs ?

Que dois-je faire si j'ai besoin de plus de temps pour terminer le cours ?

À quoi ai-je droit si je paie ce cours ?

Puis-je suivre ce cours gratuitement ?

Quelle est la politique de remboursement ?

Une aide financière est-elle disponible ?

Comment cela fonctionne

Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

Help from Your Peers

Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.

Certificates

Earn official recognition for your work, and share your success with friends, colleagues, and employers.

Créateurs

IBM

IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.

Tarification

	Acheter le cours
Accéder au contenu du cours	Disponible
Accéder au contenu noté	Disponible
Recevoir une Note Finale	Disponible
Obtenir un Certificat de Cours partageable	Disponible

Notation et examens

Note moyenne 4.6 sur 5 sur 1,145 notes

good (gentle) introduction into IBM Watson environment.

While this course is a very good introduction and generates and overview for the tools i would like to have some more fundamental information about for example what is spark and where is used (examples of real life scenarios etc etc.)

Maybe it will be covered in the next courses.