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Setting Up Anaconda and Jupyter Notebook

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Setting Up Anaconda and Jupyter Notebook

This course offers an introduction to data science and looks at what a data scientist does. It then moves on to data science in Python and, through a range of guided walkthroughs, shows you how to use Python and its features. You will learn how to set up Anaconda and Jupyter Notebook and learn, using real-world examples, how to write Python code in Jupyter, with useful tips within the context of data science.

The course also looks at object-oriented planning, as well as Python variables and Python functions, and finally, it takes a look at Python data types and functions.

Learning Objectives

  • Understand data science and the role of data scientist
  • Set up Anaconda and Jupyter notebooks
  • Improve your knowledge of coding with Python
  • Understand how to work with Python variables, functions, and data

Intended Audience

This course is intended for:

  • Individuals looking for an introduction to data science
  • Those looking to enhance their knowledge of Python and its features


To get the most from this course, you should already have some knowledge of Python and programming languages in general.


Let's now move on to have a look at Anaconda, Jupyter, and Python. Anaconda is a Python distribution. It includes Python with it, but it still has extra stuff stuck on the side of it. It gives us lots, and lots, and lots of data science modules. It gives us user interface based virtual environments management. Instead of having to do it all by the command line, we can just stick on modules that we want to import into our project. It also gives us Jupyter Notebooks. So, what we want to do is, once you get into the desktop, if we search for 'AMA' in a Windows search, we'll have this Anaconda navigation popping up. Once that pops up, click on it, and it should load. We'll just get an idea of what Anaconda gives us, and then we'll get started on understanding how to manipulate cells. This is Anaconda. The Anaconda navigator is where you tend to base all your activities. When it comes to working with Jupyter, I would recommend using the Jupyter Notebook which launches in your browser. Some people prefer using Jupyter Lab. I'll be using Jupyter Notebooks. It's very, very similar, just one of them works in a slightly different way. So, what do we have? We've got various other things. We have Our Studio, which we can use it if we want to install it, and various other gadgets like that, but we are focusing on Jupyter. We have our Environments. If you click on Environments, what we get is a list of all the Python modules that have been pre-installed into our base environment here. 


So, some of the things that we've been speaking about already, like pandas, are pre-installed. We've got NumPy pre-installed. Scrapy is a Python web scraping framework. For example, that's not installed by default. If we click on it here and we hit 'apply', then we would be able to install this into our virtual environment, and then work with that however we want to. This is our virtual environment. It's just a space where we keep useful modules. We can create a new virtual environment if we wanted to click 'create' here. Then, we would have, essentially, a blank canvas upon which to load in anything that we needed to use. We can import existing ones. All those sorts of things. If you want start doing something slightly more extended, you'll need to find it here. So, Scrapy's in here. Careas (ph 02.24), which we mentioned earlier, won't be installed by default, but it's here. A deep learning library. We can launch a terminal from here as well, where if something doesn't appear within Anaconda's not installed list, we can write a little code saying, 'Conda install blah, blah, blah.' And then that will install into your virtual environment and library. For lesser known libraries, for smaller libraries, for experimental libraries, you will need to generally copy and paste a 'Conda install', and then a link to a GitHub which will pull that code into your environment for you. So, what do we want to do? We want to click 'launch' on Jupyter Notebook, and then we get taken to a representation of our file system, and we can move things around, and delete things, and add things within our file system here. 


I would recommend clicking to go to your documents folder and just have all of your work in there, either in a folder, however you want to organise it, but keep it all in documents. Just put everything in documents you possibly want to use. If we click on 'new' in the top right-hand corner and hit Python 3, what we will attain is a Python 3 Notebook. Now, notebooks are simply ways of running Python code. In the top right-hand corner, you'll have 'new', and then you'll just hit 'Python 3', and that will create a new Python 3 Notebook. We can give it a title if we want to. We can call it, 'My best day ever.' 'My best day ever' is the name of my Python Notebook. This is what we call a REPL session. REPL meaning reading, execute, print, loop. So, what happens is, whenever you write a bit of code, it will be read, it'll be executed. If there is output, it'll be printed to the screen, and then it will be ready to go again. If we go classic with our print, 'Hello world,' then what happens is, it reads this, executes it, prints it, and then it's ready to go again, 'Hello world, again.' I can keep running this, and it will keep reading, executing, printing, and looping, again, and again, and again, and again. We can combine this with Markdown. We can run this with a Markdown type cell, where I can have my report title, let's put, 'Contents,' and then I have a list of course intro, Python intro, and then I can have, I don't know, let's put, 'This is a cell which prints hello world!' I can make this bold using various little key codes. 


If I run this now, then I get my title. I get some contents. I've got my core (ph 05.10) centre on my packet info, and then I've made a bold bit of text with, 'This is a cell which prints hello world.' The idea of the Jupyter Notebook is that you can combine documentation and information with executable pieces of Python code, which you can explore, which people can understand and read easily. These are two distinct types of cell, your Markdown cell, and your code cell.

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