Pick a couple of random industries and you will surely be able to find ways in which they use high-end software. This could not be truer for science. From research on the tiniest organisms to questions on space, scientists require specialised computing programs.
It is impossible to select the best programming language for data science. Each is designed to fulfill a specific need. But there are a few things that the ideal data science programming languages will have in common:
They can handle big data and contain enhanced tools to work with massive amounts of memory
Due to the handling of complex math calculations, they but be brief and to the point
They should have a number of built-in functions for the user to work with mathematical models
If you consider the top 20 programming languages towards the end of 2017, there are a few that tick the boxes. Let’s take a closer look at them.
4 Programming Languages That Tick The Data Science Boxes
R was created all the way back in 1997! It’s more often used to develop stochastic software and for drawing graphics. It might throw some developers off at first as numbers start at 1 instead of 0. On top of this, the format of assignment operators is not common. On the plus side, R supports a great number of methods and libraries. It’s a popular choice in Silicone Valley, used by the tech giants like Google and Facebook. Approximately 11,800 mathematical packages have been attributed to R.
It’s hard to find a developer who is not familiar with the advantages of Python. The world was first introduced to Python in 1991. Today, it is a favourite for blockchain, AI, and machine learning. Although not as efficient as others, Python is extremely good at handling middle levels of mathematical data. The Bank of America uses Python for statical calculations.
Java is among the most popular object-orientated high-level programming languages and many view it as one of the fastest. It has a massive choice of additional libraries, JIT technology, and platform-orientated code. While it’s great for structures and prototypes, it’s not ideal for statistics.
Similar to Java, developers see Scala as a safe-type program, perfect to handle big data. Scala is also used for Machine Learning. It holds generics, existential types and methods for advanced data abstraction. The code written in Scala can be incorporated into Java and vice versa. Lift and Play were both written in Scala.
Leading Libraries For Big Data
For each of the programming languages we covered above, there are numerous libraries that prevent scientists from having to implement complex mathematical calculations and other operations with large amounts of data from fresh. Here are some that are worthy of a mention.
R- stringr, dplyr, and quantmod
Stringr is designed for data collection as well as being highly functional for string manipulation. If you need extensive data analysis, dplyr is your library. Quantmod is beneficial for the economics industry, particularly for building analytical models. It was also created for users to simply import and visualise data.
Python- NumPy, SciPy, and Matplotlib
The NumPy library is excellent for a wide range of operations, matrices, and vectors. You’ll be able to perform calculations and transformations. SciPy is definitely used for its advanced calculation abilities and a variety of linear algebra, mathematical analysis spheres, and matrix methods. Matplotlib is the preferred library for the visualisation of digital data.
Java- JSAT, Java-ML, and Retina Library
Java Statistical Analysis Tool is essential if you want to create solutions based on Machine Learning. Java Machine Learning Library is a mature API used for the implementation of many types of data mining and data analysis algorithms. If you need a solution for processing great quantities of data in a string format, Retina Library should be your choice.
Scala- Breeze, Vega, Epic
Breeze is a very clever library that has taken the best of NumPy and MATLAB, allowing you to process matrices, vectors, as well as digital signs. If you are looking for help with visualising data and data analysis, Vegas is great. Epic can be used for prediction, a necessary part of Machine Learning based projects.
Each programming language and its libraries are going to have advantages and disadvantages depending on your project and team. To choose the ideal programming language for your data science project, consider the experience your team has and the precise needs of your project.