Data Science

Enhance Your Data Science Capabilities Using Anaconda on Linux

Anaconda is a tool which is designed to provide the Python and R distributions, among others, to the users in a visually intuitive manner. It provides a unique interface that allows its users to easily switch between their distributions of choice for data science related workflows. Anaconda makes the package management really simple using the inbuilt environments for different projects. Handling multiple versions of the same packages is something that most programmers usually have a difficult time with. This is where Anaconda comes in: with its simple yet powerful utility, the package management is made really unique. Not only can it allow the easy utility for different versions of packages, it also provides easy utility to using different versions of the same distribution.

For example, there are many packages still in use that can only be used with Python 2. With the Python development community already having migrated to Python 3, handling two different versions of the same distribution can become a taxing task for someone who is not well-versed in the ways of the Bash shell and environment variables. Anaconda makes it really simple to install and switch between the package versions and the Python distribution versions. This simple yet strong utility that is provided by Anaconda makes it one of the most used software tools in the world of data science today.


To use Anaconda on your Linux machine, you need to follow these steps:

1. Since Anaconda provides a graphical user interface, we need to install some dependencies using the Linux terminal.

Run the following command in the terminal:

$ apt-get install libgl1-mesa-glx libegl1-mesa libxrandr2 libxrandr2 libxss1 libxcursor1 libxcomposite1 libasound2 libxi6 libxtst6


You should see an output which is similar to the following output if you already have the dependencies installed. If not, your output differs from this:

2. We now download the Anaconda using the wget tool in the terminal.

Run the following command in the terminal:

$ wget


You should see an output which is similar to the following:

If you do not have the wget installed on your machine, you can manually download the Anaconda tool by opening this link and clicking on the install button.

You can also install the wget by running the following command in the terminal:

$ sudo apt-get install wget


3. Run the following command to install Anaconda for Python 3. Replace the “.sh” file name with the name of the file that you downloaded. Replace the ~/Downloads with your actual path:

$ bash ~/Downloads/


You should see an output which is similar to the following:

4. Press “enter” to go through and review the licensing information. Press and hold “enter” to scroll through.

5. Type in “yes” when prompted whether or not you accept the license terms of the Anaconda tool.

6. You will be prompted to select the location where you want to install Anaconda. By default, it provides you with the root directory option to install Anaconda in. You should opt for the proper location where you want it to be installed by providing it with a path.

7. You will now be prompted to choose whether or not you want the installer to initialize Anaconda3. Type “yes” here since Anaconda recommends that you select “yes” so that you do not have to deal with initialization steps when working with Anaconda every time you run it.


8. To run the Anaconda navigator, run the following command in the terminal:

$ anaconda-navigator


You should see the navigator open up:

Note: If you opted for “no” in step 7, you have to initialize the Anaconda environment before running the command in step 8.

Run the following command before running the command 8:

$ conda activate

You can now select from a wide range of different tools that are tailored for specific use cases. These can be used to write the code that ranges from simple automation scripts to custom deep neural networks which are used for specific real world problems.

User Guide

Anaconda can be used for a wide range of different use cases. Most Python and R developers use Anaconda because it provides them with easy environment management. An environment is an isolated space where the packages and tools that you install are isolated from the rest of the computing space and from the other environments. In most cases, developers are required to use specific versions of different tools for specific applications. This can become a hassle when you are forced to install more than one version of the same tool. To solve this versioning issue, developers install specific versions of software in custom environments which enables those tools to only be accessed in that specific environment. Anaconda provides easy utility to old and new environments so that you can increase your productivity without the fear of versioning errors slowing you down.

Moreover, the Anaconda Navigator provides a list of extensive resources that help the new developers learn their specific language or tool of choice from the best resources available. This list is constantly updated and even provides training exercises and courses that the user can visit along with the links to some of the most common tool documentations.


With the world of data science becoming ever important, the tools that provide transparent and easy access to most of the important resources in any data scientist needs are becoming paramount to the coding world. Anaconda provides some of the best resources that are needed to code. With support for notebooks, integrated development environments (IDEs), data visualization tools and cloud computing resources, all available under the same umbrella, Anaconda provides utility like never before.

With its intuitive graphical user interface that offers multitudes of tools to choose from, Anaconda is truly a tool that outperforms many in the data analytics domain.

About the author

Zeeman Memon

Hi there! I'm a Software Engineer who loves to write about tech. You can reach out to me on LinkedIn.