The world of data analysis has seen a steady growth with the turn of the century. The concept which was once considered inconsequential has now become one of the most abundantly used business logic techniques all around the world. Data analysis requires a collection of data points so that the valuable information can be extracted from it. The data without any preprocessing is called a “raw data” and cannot be used for any specific inferential purpose. This is where data analysis comes in and is defined as the process or technique that uses computational, statistical, and mathematical models to extract the useful insights and inference from a grouping of data that would otherwise not amount to much.
Data analysis involves multiple techniques that can be implemented on the raw data so that it can be turned into a set that produces valuable and useful inferences. These techniques include the collection of data using different methods, cleaning of data by removing unnecessary information or by adding further categories to the data and augmenting them, organization and interpretation of data which means the visualization of the data in such a way that it becomes easier to generate some useful insights from it and understand the basic distributions that are present in the data, the application of statistical, mathematical, and computational models on this data to look for and identify the trends, patterns, and relationships in the data which would otherwise be difficult to interpret.
There are multiple tools that can be used for data analysis. Some of them require a code to be written while others employ a graphical interface which is used to select the specific functionalities to be implemented on the data. We will discuss the two different tools, both of which require a code to be written for data analysis. We will compare Matlab and Python and figure out which tool is best for what use case and how it can be implemented.
Python is an interpreted programming language with a simple and easy-to-learn syntax. It makes programming easy even for beginners which is why it is extremely popular. Despite being an easy-to-learn language, its applications which are powered by third party tools and frameworks are extremely utilitarian and powerful. Python has many libraries and frameworks which help the users to perform the data analysis tasks easily. NumPy, Pandas, Matplotlib and Sklearn are some of these data analytics frameworks. They contain popular built-in algorithms that can be run on any dataset just by calling a function that represents them.
NumPy is used for numerical computing that provides fast, vectorized operations for arrays and matrices.
Pandas is used to store the data in efficient data structures like DataFrames and manipulate this data as required using the built-in functions like map and apply which make the entire process really quick and efficient.
Matplotlib is used for creating visualizations, plots, charts and graphs and is commonly used in conjunction with NumPy and Pandas since the manipulation of data before visualization is done by these libraries.
Sklearn provides different types of algorithms that are able to make accurate predictions based on training on the data.
Matlab is a numerical computing environment and programming language that is widely used for data analysis. It has a large number of built-in functions to work with data, as well as a variety of add-on toolboxes for specialized applications such as statistics, signal processing, and image processing. It is geared towards technical and scientific computing. It primarily focuses on performing operations on matrices which is why it is very efficient when it comes to performing data analytics tasks. It comes equipped with functions for linear algebra, statistics, and optimization techniques – all of which increase its utility as an analytics tool. Matlab has the following built-in tools that help it perform the data analytics tasks:
Matrix Operations is what Matlab was originally built for, which means it is extremely quick with tasks that involve large amounts of data.
Visualization provides extensive support to create a range of different plots including 2D and 3D plots, histograms, scatter plots, and more – all of which increase its utility as a data analytics framework.
Signal and Image Processing tools are baked right into the language so that the data in signal form can be worked on and processed just like any other data.
All of these tools and functionalities are what make Matlab a great tool for data analysis and visualization.
|Support||Contains amazing third party support and many libraries and modules for data analysis.||Has built-in data analysis tools which limit its potential in data analytics.|
|Efficiency||Less efficient when it comes to building and training algorithms that are meant to accurately predict the data outcomes.||More efficient because of its focus on matrix operations and linear algebra.|
|Ease||The language itself is easy to learn but the other frameworks have a learning curve with respect to their logical scope.||The data preprocessing and analysis workflow come with a slight learning curve.|
|Tasks||The library support that is offered by third party modules and frameworks opens Python up to a wide range of different data analysis use cases.||The no-open-source third party library support only leaves the functionality that Matlab already has.|
Data analysis has different tools that come in handy while working on analytical tasks. Python is used to implement the data analysis workflows with libraries that provide a range of different functionalities whereas Matlab is used because of its efficiency and quick computational capabilities. Both of these languages have their benefits and drawbacks. Some outweigh the others while still being utilitarian and useful. Python is an abundantly used language which comes with multitudes of libraries and frameworks for different tasks like AI, data analysis, data visualization, automation tasks, and more. This makes Python a very good contender in this race, but there are certain tasks where Matlab outperforms Python. Matlab primarily focuses on matrix arithmetic which makes it quicker than Python. When faced with tasks which require training on large datasets with more features, Matlab accomplishes such tasks more quickly as compared to Python. This makes Matlab a better contender when it comes to working with large datasets. When it comes to selecting between Python and Matlab, it is important to understand the specific use case. If the task requires efficiency and needs to get done promptly, Matlab would be the better choice, but you would be limited with what you can do with your data. If you require a well-documented and full suite of experimentation run on your data, Python is clearly the way to go.