Do you know that Julia is an alternative programming language of Python and R? It is a very sophisticated programming language baring high and dynamic performance. Though we use this MIT originated language for executing general tasks like writing an application, it can indulge itself into high-performance numerical analysis and computational science.
Its design includes parametric polymorphism in a dynamic programming language along with multiple dispatches. So are you eager to know more about this new programming language? Let us explore the details of Julia.
Also Read: Best Python Tools For Machine Learning And Data Science
But before that, we should learn a short intro of Python, and R. So Python is a high-level, code-readable, general-purpose programming language. R is also a programming language which is free software for statistical computing and calculations, producing statistical software and data analysis.
Countenances of Julia
Data science is an imperative part of data managing, database producing and managing systems. It has given many programming languages like C, C++, Python, Java, R and now Julia.
Like other languages, it also has some special features that make it comparatively an alternative to others. They are:
1. Data Visualisation and Plotting
It is one of the essential facets of Julia. It can represent data graphically by producing images that depict the relationships among data. Also, the plotting features makes it able to balance between simplicity and speed with static and dynamic interface.
2. Ascendable Machine Learning
The machine learning and deep learning feature offer some powerful task completing advantages and tools for AI. Now it is easier to express algorithms with the help of the mathematical syntax of Julia.
This feature also has some capabilities of making decision trees, linear models and clustering.
3. Create, Organise and Entrench Your Code
The programming language has the ability to write UIs, embed codes statistically and also deploy them on servers. It also caters to lisp-lock macros and metaprogramming amenities.
4. Allows Various Interfaces With Your Data
Julia authorise you to perform aggregations, load datasets, operate joins and many other interactions. The software also saves it with effective formats in the disk.
Also Read: Introduction to PyOxidizer: An Open Source Utility for Python Application
Plus-Points of Julia
The rudimentary features are more or less the same as other languages. Some exquisite ones are said above. Julia is specially designed for a numerical and scientific reckoning.
Following are some pros that you can get using this programming language:
Contains Mathematically-Friendly Syntax: Julia has mathematical syntaxes which are much user-friendly than other programs like Matlab, R, Mathematica and Octave. The syntaxes look similar to the original formats of the symbols that we use in real. This helps even the non-programmers to understand the functions.
Gives Faster Services in Default Mode: Julia responds to its pre-programmed abilities as per instructions faster than other languages. Though, now, even R and Python have been made faster by optimising them with tools like cython, third party JIT compilers, etc. But Julia does not need such tools. It is designed to be faster right out from the gate.
Delivers Automatic Memory Management Service: Julia and Python has a common feature of control over garbage collection and does not load the user with the basics of allocating and acquitting memory. But the difference is that using Julia; you will not lose any feature, which is common to Python’s conveniences.
What Role Does Julia Play in Data Science?
One of the eminent inventions in the world of technology as well as data science is Julia, the programming language.
Being a dynamic programming language it is not only an expert in performing with high-level abstractions but also graciously suitable for, numerical and statistical computing that is an integral part of Data Science.
Plays a Dual Role of Python and C: In the world of Data Science, scientists say, “Julia talks like Python but walks like C”. It is easier to access the prototype ideas of high-level languages like Python and R. The main trouble arises when we use the low-level and high-level languages separately to solve problems.
So here, this new programming language can offer us two language supports by giving the friendliness of Python/ R and levelling the speed of C/C++.
Memory Backup is High: It is an auto-updating language. So it automatically saves all the data that it reads. The memory backup system is also prevailing in it.
Pre-Treating Functions of Algorithms: It has the ability to pre-process the algorithms given to it. It splits the pre-loaded data into training and testing parts. The codes snippets with which we work in it are used to standardise both the training and testing features.
Shows Model-Fitting and Evaluation Facilities: The main goal is to find a linear model and evaluate it to set a presentation. This is vastly done through this software.
Why is Julia an Alternative to Python and R?
In Data Science, Julia is used as an alternative to Python and R. The enticements behind this are:
1. Julia can contact with Python, C, R and FORTRAN libraries. Julia can border the external libraries of C and FORTRAN. It can also interface and share data between itself and Python.
Also Read: Top 10 Python Libraries of 2020 You Should Know
2. Julia is user-friendly and well-interactive. It has REPL (read-eval-print-loop), which is similar to Python. In Python, a quick response can be obtained through commands and uses are friendly.
3. Julia conjoins the advantages of dynamic and static typing. It allows us to make a chain of commands. So that it can be used for the allowance of general cases for controlling variables of specific types.
4. It is a compiled programming language. For faster functioning, Julia is just-in-time (JIT) which is compiled with LLVM compilers. This helps Julia to equivalence the speed of C.
5. Julia endorses metaprogramming. It can create and update their own codes for data. It can also generate Julia programs within itself.
6. It contains a full-fledged debugger. It launches debugging programs which carry out code in a local REPL and allows us to inspect the results, check the variables and insert breakpoints in code.
Conclusion
Thus, here is given a clear cut conception of the newly invented programming language, Julia. The above said details about this programming language could clearly say that Julia is an appropriate alternative of Python and R.
Hope, you have understood the basics of Julia and how it helps in Data Science.
