Julia Vs Python – Which programming language should you learn to enter the data science industry today?
Content Strategist- Ivy Pro School May 22, 2019 No Comments
Image Reference : https://www.techlabe.com/2018/11/julia-vs-python-whichprogramming.html
A new entrant on the block, Julia has managed to garner a lot of buzz around itself in the past year. But Python is not falling back in the race
The language still in its nascent stages (unveiled only in 2012, developed and incubated at MIT, yes MIT! ) made an appearance in TIOBE’s “interesting moves” picks at 39th position in September 2018.
But if you ask me, Julia has a long way to go and is yet to achieve what Python has already earned. Python was ranked the programming language of the year 2018 by TIOBE. Python is a favoured language among data professionals and widely used by them on a day to day basis with three out of four recommending aspiring data scientists to learn Python first.
But then again, we are Analysts, we need data to corroborate our sayings.
Let me try and breakdown some of the features of both the languages to get a more comprehensive view and perhaps you can declare the verdict yourself.
JULIA – Features
- Compiled and not interpreted: Julia is compiled by the LLVM and throws up issues like recompiling the code every time it is started up.
- Not properly Developed: Considering its recent entry, there is still room for improvements. Many R users, when shifted to Julia, realized that it doesn’t work as smooth as R. Julia’s tools didn’t seem to be as fluid and reliable as they are expected.
- Unable to identify issues: Julia is far behind from Python and R in terms of identifying issues and debugging tools. But soon more tools were expected to be developed for users.
- Column major or row major? Julia matrices are accessed column-major, whereas Python Numpy matrices are accessed row-order. Accessing a column-major array stored in the cache in the column-row order is usually more efficient than a row-column order due to the storage of the array in a sequential manner. Yes Julia has adopted that well.
- Visualizations – Julia does offer a few libraries for visualization but they mostly seem inspired by R. E.g. – Gadfly.jl inspired by ggplot2 for R.
- Safety issues regarding Interface: Julia poses the risk of unsafe interface to native APIs by default.
- Lack of powerful packages: “R” & Python has some terrific third party packages for data analysis, which Julia still lacks.
PYTHON – Features
- No compilation: Python is an interpreted language, there is no need to compile it.
- Versatility: The versatility of Python (including its easy readability and code friendly syntax) allows developers to perform multiple activities at one time combined with abundance of libraries and frameworks that facilitate coding and save significant development time. Python excels in all its roles right from being the most favoured scripting language, to automating stuff, to web development and so much more.
- Ease of learning: Even if you are a new Python user, chances are you can pretty much find the codes quite easily by just googling it. That simple!
- Strong Developer community : This article will give you a good idea about the trending programming languages and why Python is a feature in almost all articles in the same context, creditable to its high versatility and developer community unlike Julia which is still in its nascent stages.
- Slow execution? Not any more: Although some people argue that interpreted language can result in slow execution, Intel Distribution for Python, has come up with a set of tools that lets anyone speed Python application performance right out of the box, usually with no code changes required. It helps accelerate Python execution performance. However, you can boost the speed of Python by using third-party compilers such asPyPy and other external libraries as well.
- Third-party packages: One of the biggest attractions of Python is the huge no. of packages it can support and make up an essential part of every data scientist’s toolkit.
- Python for Machine Learning: Python is the most popular language for Machine Learning today. There are over 145,000 custom-built software packages, many of which use ‘machine learning’ to crunch patterns in big data. Enroll in a Machine Learning course to learn the best combination of theory and practical aspects of Machine Learning with Python.
- Employer likeability: Python and “R” are also among the most frequently mentioned skills in job postings for data science positions.( A cool 28% of all Silicon Valley job postings).
Industry Use Cases
Nearly three decades since its inception, companies (Google, Amazon for its Product recommendations, Netflix, LinkedIn, Facebook) heavily rely on Python for their programming, web development, IOT or data analysis needs.
According to TIOBE, Python is now becoming “increasingly ubiquitous” and a top choice at universities for all subjects that require programming, as well as in industry.
The key to its popularity is its easy learning curve, easy installation and deployment.
Julia is used at more than 700 universities and research bodies and also at companies such as Aviva, BlackRock, Capital One, Netflix but is yet to amass wide scale popularity of the likes of Python.
Are you still wondering? Take a read: Top 9 Reasons to learn Python to become Data Scientist & AI Expert
Python has been hailed as the top career skill of 2019.
“Through machine learning (ML) skills, which include data science skills like R/Python, data modelling and validation techniques, expertise in statistics and probability, and implementation of machine learning libraries and algorithms “ by Economic Times.
#Pro Tip : To sum it up, Julia was conceptualized as a top-tier language but it still has a long way to go before being adopted for mass consumption. If you are entering the data science field, learning the nuances of Python programming language will land you more lucrative jobs in the current job landscape.
Check out Ivy Professional School’s new course, Machine Learning With Python. Ivy’s expert faculty have been named among the Top 20 Analytics Academicians in the country in 2018.
Shromona Kahali – Content Strategist, Ivy Pro School
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