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.
IBMÀ propos de ce cours
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Résultats de carrière des étudiants
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39%
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21%
a obtenu une augmentation de salaire ou une promotion
Certificat partageable
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Niveau débutant
Approx. 19 heures pour terminer
Anglais
Sous-titres : Anglais, Coréen
Résultats de carrière des étudiants
38%
ont commencé une nouvelle carrière après avoir terminé ce cours
39%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
21%
a obtenu une augmentation de salaire ou une promotion
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau débutant
Approx. 19 heures pour terminer
Anglais
Sous-titres : Anglais, Coréen
Offert par
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.
Programme du cours : ce que vous apprendrez dans ce cours
4 heures pour terminer
Data Scientist's Toolkit
This week, 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.
4 heures pour terminer
17 vidéos (Total 84 min)
17 vidéos
Course Introduction3 min
Languages of Data Science2 min
Introduction to Python3 min
Introduction to R Language3 min
Introduction to SQL3 min
Other Languages6 min
Categories of Data Science Tools2 min
Open Source Tools for Data Science - Part 17 min
Open Source Tools for Data Science - Part 25 min
Commercial Tools for Data Science5 min
Cloud Based Tools for Data Science8 min
Libraries for Data Science4 min
Application Programming Interfaces (API)4 min
Data Sets - Powering Data Science6 min
Sharing Enterprise Data - Data Asset eXchange3 min
Machine Learning Models7 min
The Model Asset Exchange5 min
4 exercices pour s'entraîner
Practice Quiz - Languages 30 min
Practice Quiz - Tools30 min
Practice Quiz - Packages, APIs, Data Sets, Models30 min
Graded Quiz30 min
9 heures pour terminer
Open Source Tools
This week, 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.
9 heures pour terminer
12 vidéos (Total 66 min)
12 vidéos
GitHub - Getting Started3 min
GitHub - Working with Branches5 min
Git and GitHub via command line (Optional)9 min
Branching and merging via command line (Optional)5 min
Contributing to repositories via pull request (Optional)8 min
Getting Started with Jupyter Notebook5 min
Getting Started with JupyterLab6 min
Jupyter Architecture6 min
What is RStudio IDE?4 min
Installing Packages and Loading Libraries in RStudio IDE2 min
Plotting Within RStudio IDE3 min
4 exercices pour s'entraîner
Practice Quiz - GitHub30 min
Practice Quiz - Jupyter Notebook30 min
Practice Quiz - RStudio IDE30 min
Graded Quiz30 min
4 heures pour terminer
IBM Tools for Data Science
This week, 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)
15 vidéos
Watson Studio Introduction4 min
Creating an Account on IBM Watson Studio2 min
Jupyter Notebook in Watson Studio - Part 1 2 min
Jupyter Notebook in Watson Studio - Part 23 min
Linking GitHub to Watson Studio2 min
Other IBM Tools for Data Science1 min
IBM Watson Knowledge Catalog6 min
Data Refinery7 min
SPSS Modeler Flows in Watson Studio6 min
IBM SPSS Modeler7 min
SPSS Statistics7 min
Model Deployment with Watson Machine Learning4 min
Auto AI in Watson Studio4 min
IBM Watson OpenScale7 min
3 exercices pour s'entraîner
Practice Quiz - Watson Studio30 min
Practice Quiz - Other IBM Tools30 min
Graded Quiz30 min
3 heures pour terminer
Final Assignment: Create and Share Your Jupyter Notebook
This week, 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.
3 heures pour terminer
1 lecture
1 lecture
IBM Digital Badge2 min
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 GC12 avr. 2020
It serves perfecty its aim that is giving a first glance of the open course tools for data science. Of course each tool is briefly touched and it hands over the student the duty to deepen each tool.
par AJ15 sept. 2020
Absolutely Loved this course!! Challenging at times to keep up with all the terms and processes. The course provided great insight into Data Science. Would highly recommend it as your first course.
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