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.
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.
- 5 stars67.06%
- 4 stars22.24%
- 3 stars6.63%
- 2 stars2.14%
- 1 star1.91%
來自TOOLS FOR DATA SCIENCE的熱門評論
Everything was good, except the stuff with Zeppelin notebooks. That was just confusing and it felt like there were large chunks of explanations missing for people with no programming background
It's a good introductory course to know all the open-source tools available for data science. However, it will not teach you how to code for these tools apart from introducing a few basic lines.
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.
Great course with practical approach to tools that come handy beside data science with python such as git and github, Some basic R coding and a great introduction to IBM Watson studio and cloud.