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.
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Английский
Субтитры: Английский, Корейский
Карьерные результаты учащихся
38%
начал новую карьеру, пройдя эти курсы
39%
получил значимые преимущества в карьере благодаря этому курсу
21%
стал больше зарабатывать или получил повышение
Сертификат, ссылками на который можно делиться с другими людьми
Получите сертификат по завершении
100% онлайн
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Прибл. 19 часов на выполнение
Английский
Субтитры: Английский, Корейский
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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.
Программа курса: что вы изучите
4 ч. на завершение
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 ч. на завершение
17 видео ((всего 84 мин.))
17 видео
Languages of Data Science2мин
Introduction to Python3мин
Introduction to R Language3мин
Introduction to SQL3мин
Other Languages6мин
Categories of Data Science Tools2мин
Open Source Tools for Data Science - Part 17мин
Open Source Tools for Data Science - Part 25мин
Commercial Tools for Data Science5мин
Cloud Based Tools for Data Science8мин
Libraries for Data Science4мин
Application Programming Interfaces (API)4мин
Data Sets - Powering Data Science6мин
Sharing Enterprise Data - Data Asset eXchange3мин
Machine Learning Models7мин
The Model Asset Exchange5мин
4 практических упражнения
Practice Quiz - Languages 30мин
Practice Quiz - Tools30мин
Practice Quiz - Packages, APIs, Data Sets, Models30мин
Graded Quiz30мин
9 ч. на завершение
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 ч. на завершение
12 видео ((всего 66 мин.))
12 видео
GitHub - Getting Started3мин
GitHub - Working with Branches5мин
Git and GitHub via command line (Optional)9мин
Branching and merging via command line (Optional)5мин
Contributing to repositories via pull request (Optional)8мин
Getting Started with Jupyter Notebook5мин
Getting Started with JupyterLab6мин
Jupyter Architecture6мин
What is RStudio IDE?4мин
Installing Packages and Loading Libraries in RStudio IDE2мин
Plotting Within RStudio IDE3мин
4 практических упражнения
Practice Quiz - GitHub30мин
Practice Quiz - Jupyter Notebook30мин
Practice Quiz - RStudio IDE30мин
Graded Quiz30мин
4 ч. на завершение
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 ч. на завершение
15 видео ((всего 72 мин.))
15 видео
Watson Studio Introduction4мин
Creating an Account on IBM Watson Studio2мин
Jupyter Notebook in Watson Studio - Part 1 2мин
Jupyter Notebook in Watson Studio - Part 23мин
Linking GitHub to Watson Studio2мин
Other IBM Tools for Data Science1мин
IBM Watson Knowledge Catalog6мин
Data Refinery7мин
SPSS Modeler Flows in Watson Studio6мин
IBM SPSS Modeler7мин
SPSS Statistics7мин
Model Deployment with Watson Machine Learning4мин
Auto AI in Watson Studio4мин
IBM Watson OpenScale7мин
3 практических упражнения
Practice Quiz - Watson Studio30мин
Practice Quiz - Other IBM Tools30мин
Graded Quiz30мин
3 ч. на завершение
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 ч. на завершение
1 материал для самостоятельного изучения
1 материал для самостоятельного изучения
IBM Digital Badge2мин
Рецензии
Лучшие отзывы о курсе TOOLS FOR DATA SCIENCE
от партнера CB1 нояб. 2018 г.
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.
от партнера NK19 мая 2019 г.
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.
от партнера GC12 апр. 2020 г.
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.
от партнера FC5 нояб. 2019 г.
It would be nice if you could update the material since some tools have changed either name or the way they look compared to the videos/images. Very good material though, I enjoyed the course much.
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