How do you use Python for advanced data analytics?

There are more than 700 programming languages available in the marketplace today. [1] Some languages cater better to building online games, while others work better for software design and data analytics. With technological advancement, a new language or an update to the existing one pops up every now and then. So it is essential for data analysts to regularly reassess their needs and reflect on their context in order to choose the most appropriate programming language.

Top programming languages for data analytics in 2021

Python

Python has been around since 1991. It is one of the best programming languages widely used in data analytics. It is easy to use, fast, and manipulates data seamlessly. It supports various data analytics activities such as data collection, analysis, modelling, and visualisation.

Javascript

Javascript is a universal language that offers the possibility of managing multiple tasks simultaneously. Rich and attractive visualisations can easily be created in Javascript. It’s also easy to learn and use.

R language

R is widely used to manage statistical and graphical aspects of data science. It consists of multiple libraries for data science and can be extremely useful for exploring data sets.

There are several other programming languages available for you to explore, such as Java, Scala, Julia, SQL, C/C++, Matlab, SAS, and so on. To learn more about the features and application of other programming languages, read the following article.

Read: Top Programming Languages for Data Science in 2020 [2]

Why choose Python for data analytics?

Python has been the first choice when it comes to choosing a programming language for data analysis. [2] A study from 2018 with a 18,827 sample size voted Python (87%) as the top programming language for data analysis and data science, followed by SQL (44%) and R language (31%), respectively. [3] Python consists of over 70,000 libraries and has about 8.2 million users worldwide.

Python is advanced and offers the best features in the industry today. Reasons for this are that it increases productivity, quality of coding, and saves significant amounts of time for programmers. Python has been popular for a number of reasons, including its simple syntax that mimics natural language, its versatility, its affordability (open-source, free-to-use), its active support community, a dynamic resource for other programmers to seek support and guidance from, and its extensive archive of modules and libraries. Python also provides seamless integration with other data science technologies such as TensorFlow and SQL.

Are you keen to learn more about why Python is the go-to programming language for data analytics? Read this article to understand more about the different Python libraries offered, and how it is incredibly beneficial in machine learning and deep learning.

Read: Best Python Libraries for Machine Learning and Deep Learning [4]

1. Fowler T. How many computer programming languages are there? [Internet]. CareerKarma; 2020 Jul 21. Available from: https://careerkarma.com/blog/how-many-coding-languages-are-there/
2. Costa CD. Top Programming Languages for Data Science in 2020 [Internet]. Towards Data Science; 25 Aug 2020. Available from: https://towardsdatascience.com/top-programming-languages-for-data-science-in-2020-3425d756e2a7
3. Mah P. Python Use By Data Scientists Growing [Internet]. CDO Trends; 15 Jul 2020. Available from: https://www.cdotrends.com/story/14950/python-use-data-scientists-growing
4. Costa CD. Best Python Libraries for Machine Learning and Deep Learning [Internet]. Towards Data Science; 25 Mar 2020. Available from: https://towardsdatascience.com/best-python-libraries-for-machine-learning-and-deep-learning-b0bd40c7e8c