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Jupyter Notebooks on IBM Watson Studio (Data Science Experience) - Part 2/2

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Open Source tools for Data Science

4.6 (10,189 notes) | 58K étudiants inscrits

Cours 2 de 9 dans Science des données IBM Spécialisation

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.

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4.6 (10,189 notes)

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SH

Feb 01, 2019

All the tools required for ML kick starting was explained very clearly and it helped me a lot in building the understanding of what tools need to be learnt in the field of ML and Data Science.

PM

Jun 04, 2019

Does a good job of showing areas to obtain access to use Python, R, and Scala. You can however tell they're pushing IBM products when in reality there are many other options such as Anaconda.

À partir de la leçon

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.

What is IBM Watson Studio (Data Science Experience)?0:59

Creating an account on IBM Watson Studio (Data Science Experience)1:45

Jupyter notebooks on Watson Studio (Data Science Experience) - Part 1/23:34

Jupyter Notebooks on IBM Watson Studio (Data Science Experience) - Part 2/24:07

Enseigné par

Polong Lin
Data Scientist

Transcription

Great. So this is an empty Jupiter notebook. You'll notice in the top right hand corner you'll see, "Kernel starting, please wait," in a black circle next to the interpreter you've chosen, which means that the interpreter is busy and you won't be able to run or execute any code just yet. There, now, the circle is a white empty circle, which means your interpreter is ready. Okay, let's try writing some code here. So, in this gray code cell here, I can type in something like one plus one and I can click on the button here and that says Run to run the code. And what this does is it runs against the interpreter here Python 2 with Spark 2.1. I can also type in something like x is equal to Hello World and then print the contents of x. Instead of clicking on the Run button, I can also press Shift + Enter to execute the code cell. If you want to add a new cell, you can click on any of the cells from the left hand side, click on Insert and you can choose to Insert Cell Above or Below. Here I'll add in a Cell Above and what I'll do is add a little title for my notebook. So, here I want to add in some text, not code. So, I can write something like My Title and what I can do is change the format from Code to Markdown. And then from here, you can simply click within the Markdown cell and press Shift + Enter to convert that into its proper text or Markdown format. And if you'd like to edit this again you can just simply double click within the cell and change it however you like. Press Shift Enter and it will render. There are many different ways to style your text. For example, well let me first convert this to Markdown so you can see. You can add in any kind of HTML text including images here. You can change the size of your text using the Markdown formatting. You can also add in lists, numbers, and tables and here you can also of course make your text Bold or Italics. So, go ahead and run this so you can see the effect. And just know there's one thing that's a little bit unintuitive, and that's this part here you'll see that I've written out Bold and Italics. And originally these were on two separate lines. See Bold and Italics are there. So to make sure that Bold and Italics are actually appearing in separate different lines here this is on the same line to appear as separate lines simply add in two empty spaces after each of these lines. And that way that will ensure that the next line, Italics will be on its own separate line. So that's a little bit unintuitive, I thought it'd be good for you to know. So, I think those are the key things that you need to know for notebooks on IBM Data Science Experience. You can create other notebooks for your projects. You can save your notebooks as well by going to File, Save and Save Version, but just note that these notebooks will save automatically every two minutes, so you don't have to worry too much about saving. You can also download these notebooks if you like to your local desktop. And if you ever get stuck with your Kernels, you can go ahead and click on Kernel here in this menu. You can click on Interrupt or you can click on Restart, which will restart the Kernel. And if you do want to switch your interpreters from Python to R for example, you can go ahead and click on Change Kernel and choose the language that you want to work with. Note that you can only use one language at a time, so you can only use R or you can only use Python 2 or Python 3 in the same notebook. And just one last step is that if you like a little bit more real state so you can see more of your notebook, you can click on this button in the right hand corner and that will free up a little bit more of the space here so you can see more of your notebook. So that's all for this video. Happy coding with Jupiter notebooks on Data Science Experience.

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