In today's technologically advanced era where computer science and data science are closely-knit, the ability to control your computer like a software developer can be a worthy asset, even for a data analyst or scientist. The Unix command-line interface (CLI) enables us to do that and much more. Switching from a graphical user interface (GUI) to a command-line interface (CLI) can feel too much, but we are willing to help you. For starters, below are some reasons you should be learning the command line.
1. Command-Line Skills Are Famous and Pay Good
2019's Stack Overflow's Developer Survey claims that bash/shell is the 6th most used language overall, ranking ahead of R and Python. It was related to higher incomes than either R/Python, as per the survey. It is even ranked high on the most-liked technologies list and less on the most-disliked technologies list.
While Stack Overflow's survey covers software developers & engineers, the CLI is of specific relevance for data scientists as Bash/Shell correlates more with Data Science
technologies such as PyTorch and TensorFlow Python, and IPython/Jupyter. This is even supported by the latest Python Developers Survey executed by Python Software Foundation.
2. Command-Line Skills Help With Developing Repeatable Data Processes
A part of a data scientist's role is to ensure certain info is available systematically, mostly daily. Many times, this data is obtained, processed, & shown in the same manner. The command line is suitable for this reason as commands are easily automated & replicated.
Working with CLI, you can write some scripts that will download, configure, and test everything naturally. If you can't, you will have to resort to a GUI and make the same mouse & click movements repeatedly across many machines.
3. Command-Line Skills Make You More Flexible
In a data science role, you will often find you have more elasticity if you can use the terminal instead of counting on clicking through GUIs. Since the CLI is a program that operates other programs, the interaction between programs is mostly easier to adjust in the command line. Once you have mastered a command-line program, it is quite simple to write scripts, and shell scripts make developing data pipelines and workflows much easier. Moreover, knowing how to use the shell gives you another option for interacting with your computer.
Command-line can render you with more direct power & control for times when you need it rather than GUI.
4. Working With Text Files is Simpler
Text files are among the most common methods to save and manage data, and almost any data science project will involve some work with text files. Therefore, the ability to handle text files fast enough and efficiently is a valuable skill for a data scientist.
The shell has robust text processing tools like sed & AWK to help get used to files and enable data cleaning.
5. It's Less Resource-Intensive
While working with restricted computing resources or just maximizing your speed, using the CLI is virtually always better than using a GUI. Using a GUI means resources should be dedicated to providing the graphical output. This is true for working remotely and locally. While connecting remotely, GUIs eat up much more bandwidth than terminals, wasting resources.
The latency will be higher when using GUI, while it will be lower in CLI, making it easier to handle since you know accurately where your cursor is at any given time.
6. You Require Command Line Skills for the Cloud
Cloud services are mostly connected to and ran through a CLI. This is important for more advanced data science work like deep learning, where your local computing resources will mostly be inadequate for the tasks you would like to perform. According to Nucleus Research, in 2018, less than 10% of deep learning projects were being run on-premise. That trend was escalated with only 4% of projects running on-premise in 2018. Today, 96% of deep learning is running in the Cloud.
If you are keen to learn advanced techniques like deep learning, CL Skills will be essential for shifting your data to and from the Cloud efficiently.
7. Unix Shell Skills Transfer Well to Other Shells
There are some famous shells, and they are more alike than different, making it simple to switch between them. This is particularly useful when you are using online services that need some sort of CLI. Whereas GUI are limitless, and learning one won't essentially help you learn any others.
8. You Can Probably Type Quicker Than You Click
A study was conducted to verify that keyboard shortcuts are undoubtedly the most efficient method. 6 participants performed common commands using icon toolbars, keyboard shortcuts, and menu selection. The keyboard shortcuts were the most efficient.
In other terms, even when you feel you are working faster through a GUI, there is a good chance that, at least for certain tasks, you will be more efficient in the command line.
9. Auditing & Debugging is Simpler
Since it's effortless to track your activity on the command line, debugging and auditing are much more straightforward. You can easily go through the log to track each action you took in the shell, while if a misclick leads to an error when you are working with a GUI, there is likely to be no record of it.
10. The Command-Line is Easier Than You Think
There's a misconception that using the command line needs you to know many 100 commands. In fact, even though there are 100 commands available to use, you are likely to require only a small percentage of these commands to do the most common data scientist tasks.
Hopefully, this blog helped you as a data scientist to realize the significance of learning the command line program. Above are the 10 reasons why you should learn the command line program. Why not when it makes your life easier?
Harnil Oza is a CEO of HData Systems - Data Science Company & Hyperlink InfoSystem a top mobile app development company in Canada, USA, UK, and India having a team of best app developers who deliver best mobile solutions mainly on Android and iOS platform and also listed as one of the top app development companies by leading research platform.