How to Begin Using Anaconda for Data Science

Programming languages are the core of the IT sector. The demand for programming languages has seen exponential growth over the last decade. Currently, the Python programming language is in high demand. According to Forbes, it is one of the five most in-demand programming languages. One of the most-used distributions of the language is Anaconda. Anaconda Python is extensively used in scientific computing, especially in data science. Even though it is available on all major platforms, the Python distribution functions exceptionally well on macOS. This article will cover what Anaconda is, how to install it on a Mac, how to use it for data science, and many other aspects. Let us get started.

What is Anaconda?

Anaconda is a fully-stacked open-source data science toolkit that boasts of a community of over 6 million users. It features thousands of data packages and a robust virtual environment manager. The utility is available across all major platforms and is easy to download.

How to Install Anaconda on Mac:

Here are the steps following which you can install and get started with Anaconda:

Step 1 – Go to the official website’s download page and click on “Mac.” Determine whether your system is based on 32-bit or 64-bit architecture and download the latest version of Python accordingly. 

Step 2 – Once the download is complete, navigate the “Downloads” folder and open the .pgk installer.

Step 3 – Follow the installer’s instructions to add Anaconda to your system successfully.

Step 4 – After the installation, you must load the alterations to your Mac’s “Path” environment variable. Next, open a new Terminal window and type “$ cd ~” followed by “source. Bashrc” in the following line.

Step 5 – Finally, open another Terminal window and type “python” to run and test some code.

How to Uninstall Anaconda on Mac:

Once you have installed the Python distribution on your Mac, it is pretty tedious to get rid of it. However, there are numerous ways to uninstall Anaconda on your Mac. To find out, click here.

Getting Started With Anaconda Python For Data Science:

Once you have installed Anaconda on your desktop, the Anaconda Navigator will automatically open up. The navigator is a graphical user interface that gets installed along with Anaconda. The user interface contains tons of options and features inside. Let us take a look at them:

  • Jupyter Notebook

As mentioned earlier, Anaconda features two GUIs, one of which is the Jupyter Notebook. It is entirely web-based and offers users an interactive notebook environment. Using Jupyter Notebook, users can conveniently read and edit documents while sharing analytics. The open-source Jupyter Notebook provides users with features that facilitate creating and sharing documents consisting of live code, text, complex equations, and visualizations. It is excellent for cleaning data and transforming it. Moreover, it also facilitates numeric simulations, modeling of statistics, and visualizing data.

  • Spyder

Similar to Jupyter Notebook, Spyder is an interactive IDE that is lightweight and easy to operate. The majority of the data scientists use Spyder to integrate with computation libraries like SciPy, IPython, etc. Also known as “Scientific Python Development IDE,” Spyder is a great cross-platform IDE that gets the job efficiently.

Useful Functions that Anaconda Commands can Perform:

There are numerous functions you can perform by using Anaconda commands for Data Science. Here are some of the most useful ones:

  • You can view all the packages installed.
  • You can locate a specific package and install it to elevate the functionalities.
  • You can conveniently remove packages that you no longer require.

Additional Applications You Can Explore and Install:

Once the application is installed, you can choose an extensive range of applications that the distribution includes. Here are some applications you can consider installing to manage and analyze data in a more detailed manner:

  • Orange 3 – Orange 3 offers users the feature to analyze and visualize data precisely. Moreover, it also provides insights regarding the data flow. The application features a component-based data mining structure.
  • Datalore – A powerful tool that significantly benefits data analysis projects is Datalore. It offers integration with Jetbrains and provides the user with coding assistance. Users can edit and execute Python-based notebooks online and share the results with others using the application.

Why is Anaconda Preferred Over Other Python Distributions For Data Science?

 It is preferred over other Python distributions because of the following reasons:

  • It Is a Complete Package – Compared to other major Integrated Development Environments (IDEs), Anaconda features a simple and intuitive interface. Moreover, the utility simplifies managing packages and the virtual environment.
  • It Features Numerous Pre-Installed Packages – Anaconda is a preferred choice because it features numerous pre-loaded Python packages, which makes working with it very convenient.
  • It Sports Easy-to-Use Graphical User Interfaces – This Python distribution comes pre-loaded with Jupyter Notebook & Spyder, both of which are incredibly user-friendly IDEs for Python.

Data Science is a rapidly expanding field in the IT sector. Two of the most commonly used languages for managing and analyzing data are Python and R. Using distributions like Anaconda allows individuals and companies to view, edit, and manage data efficiently.

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