With that, let's get started! Python To be able to use Python on your local computer, you first need to install it.
There are different ways to install Git on Mac OS. You can install Git using Homebrew, MacPorts, or by downloading the Git installer package. Select Install for all users of this computer and click Continue to begin the installation: If you need to change the installation location click Change.
There are many different python distributions, but for data science, the Anaconda Python Distribution is the most popular. Benefits of Anaconda Anaconda is a package manager, an environment manager, and Python distribution that contains a collection of many open source packages. An installation of Anaconda comes with many packages such as numpy, scikit-learn, scipy, and pandas preinstalled and it is also the.
The image below shows a Jupyter Notebook in action. Jupyter notebooks contain both code and rich text elements, such as figures, links, and equations. You can learn more about Jupyter Notebooks. Some other benefits of Anaconda include:.
If you need additional packages after installing Anaconda, you can use Anaconda's package manager conda or pip to install those packages.This is highly advantageous as you don't have to manage dependencies between multiple packages yourself. Conda even makes it easy to switch between Python 2 and 3 (you can learn more ). Anaconda comes with Spyder, a Python Integrated Development Environment. An Integrated Development Environment is a coding tool which allows you to write, test and debug your code as they typically offer code completion, code insight by highlighting, resource management and debugging tools among many other features. It is also possible to integrate Anaconda with other Python Integrated Development Environments including PyCharm and Atom. You can learn more about different Python Integrated Development Environments.
How to Install Anaconda (Python) Here are some links to guides below on how to install Anaconda on your operating system. R Programming Language Most people generally install RStudio alongside the R programming language.
The RStudio integrated development environment (IDE) is generally considered the easiest and best way to work with the R Programming language. Benefits of RStudio An install of the R programming language gives you a set of functions and objects from the R language and an R interpreter that allows you to build and run commands. RStudio gives you an integrated development environment that works alongside the R interpreter. When you open RStudio, an screen like the one above appears. A few features in contained in the four RStudio Panes are: (A) a Text Editor. (B) Dashboard to Work Environment.
(C) R Interpreter. (D) Help Window and Package Management System.
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All these features make it so RStudio is all you really need after installing R. A common question people often ask is how to install packages in R. The video below shows one of the ways to take advantage of the RStudio's Package Manager to install a package tidyverse. You can learn more about the useful features of RStudio in Datacamp's two course series, Working with the RStudio IDE (, ). How to Install R and RStudio Here are some links to guides below on how to install R and RStudio on your operating system. Unix Shell Navigating directories, copying files, using virtual machines, and more are a regular part of a data scientist's job.
You will often find the Unix Shell utilized to accomplish these tasks. Some Uses of a Unix Shell 1 - Many Cloud Computing Platforms are Linux based (utilize a flavor of Unix Shell). For instance, if you want to, or do it requires some Unix Shell knowledge. There are times when you may have a use for a, but it is less common. 2 - Unix Shell provides a number of useful commands such as: wc command which counts the number of lines or words in a file, cat command which concatenates/merges files, head and tail commands which help you subset large files. You can learn more about this in.
3 - You will often find Unix Shell integrated with other technologies as you will see throughout the rest of the article. Integration with Other Technologies You will often find Unix Shell commands integrated in other technologies. For example, it is common to find shell commands in Jupyter Notebooks alongside Python code. In Jupyter Notebook, you can access shell commands by escaping to the shell by using an!
In the code below, the result of the shell command ls (which lists all the files in the current directory) is assigned to the Python variable myfiles. Myfiles =!ls The image below shows some Python Code integrated in a workflow to combine multiple datasets. Notice a Unix Shell command (enclosed in the red rectangle) integrated in a Jupyter Notebook.
Keep in mind that the code in the image above isn't some unique way to do a task, but just a small example of how you may see Unix utilized. If you want to learn how to use Unix for Data Science, Datacamp has a free course which I highly recommend. It is a skill that lots of aspiring data scientists forget about, but it is a very important skill in the workplace. Unix Shell on Mac Mac comes with a Unix shell so you usually don't need to install anything!
An important point is that there is a variety of Unix systems that have different commands. Sometimes you find that you don't have a Unix command (like wget) found on another Unix system. Similar to how you have package managers through RStudio and Anaconda, Mac can have a package manager called Homebrew if you install it.
The link below goes over how to install and use Homebrew. Unix Shell Commands on Windows Windows does not come with a Unix Shell. Keep in mind that what Unix Shell does for you is give you useful commands for Data Science. There are many different ways to get these useful commands on Windows. You can with the optional Unix tools so that you can have Unix commands on your Command Prompt. Alternatively, you could (10mb), Cygwin (100mb minimum), among many other options. Git Git is the most widely used version control system.
A version control system is something that records changes to a file or set of files over time so that you can recall specific versions later. Git is an important technology as it really helps you work with others and it is something you will find in a lot of workplaces. Some of the benefits of learning Git include:.
Nothing version controlled using Git is ever lost, so you can always go back to see previous versions of your programs. Git notifies you when your work conflicts with someone else's, so it's harder (but not impossible) to accidentally overwrite work. Git can synchronize work done by different people on different machines, so it scales as your team does. Knowing Git makes it easier to contribute to open source development of packages in R and Python.
Integration with Other Technologies One of the cool things about Git is you often find it integrated with other technologies. Earlier I mentioned that the RStudio integrated development environment (IDE) is generally considered the best way to work with the R Programming language. And most many Python Integrated Development Environments (IDE) (learn more ) offer version control support. If you want to learn how to use Git for Data Science, DataCamp has a free course which I highly recommend. How to Install Git Here are some links to guides below on how to install Git on your operating system. Conclusion This tutorial provides a way to setup a local data science environment on your local computer. An important point to emphasize is that these technologies can and are often integrated together.
If you any questions or thoughts on the tutorial, feel free to reach out in the comments below or through. Also, feel free to check out my other installation based tutorials located on my or my.
This tutorial shows you how to install the Git version control system on a Mac. It assumes a little familiarity with the Command Line. If you don’t know how to use the Command Line, then check out ‘s excellent series of video tutorials over at: you can find the first one and the rest. Once you’re done installing Git, you’ll need to learn a few commands to get Git doing what you want it to do. The are great, even if they do currently cost $12: if you can find a good, free resource that covers the fundamentals of Git then add a comment to this post. If you’re writing code of any variety you should be using a version control system, a piece of software that sits in the background and tracks the changes you make to your code.
Whatever you’re coding, a version control system will allow you to be more adventurous, as you won’t be worrying about breaking stuff whenever you make changes. There are available. Until relatively recently, Drupal.org used the, but earlier this year switched to. Git has a number of advantages over CVS, including a cleaner syntax, a more active developer community, the facility to code collaboratively via and, if you are familiar with British English, So, if you want to contribute to the work that is done by the Drupal community at Drupal.org – writing modules or themes, or contributing patches to Drupal core, for example – you need to use Git.
There are a number of different ways to install Git on the computer you use for development. I use a Mac, and this is the simplest installation method I’ve found:. Get hold of a copy of Apple’s XCode Developer Tools from You’ll need to jump through a few hoops in order to get the download started, but it’s a relatively straightforward process.
Apple started charging for XCode as of version 4; Macports only requires the most recent release of version 3, which is – at the time of writing – still available at no charge. Once the XCode.dmg file is downloaded, run the installer. Download and install Macports from.
Macports is an easy to use system that allows you to install open source software from the command line. It’s worth setting up on your Mac, since once it’s installed you’ll be able to use it to install other open source software. Unless you specificially know that you want to install Macports via a different method, download the.dmg file and install via that. With XCode and Macports installed on your Mac, run the following command from the command line to install Git: sudo port install git-core +bashcompletion +doc +gitweb +svn Macports will go off and do its thing, configuring and installing all of the different components that are required for Git to work. Once Macports is finished you’ll be returned to the command line.
Type “git” and hit return. If you’re given a load of information about the Git version control system then the installation worked and you’re ready to start using Git to keep track of your code. If you don’t get that information then it’s likely that something went wrong with the install process; post a comment and I’ll do my best to respond.
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