Choosing The Best Programming Languages for Data Science

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  • 2 weeks ago
  • Posted: January 10, 2019 at 11:30 am

When we talk about our future- it relies on technologies like artificial intelligence and machine learning. You know the best part is they all revolve around the data. Without data, there will be no chatbots, voice assistance, and Google search, or anything leveraging information to provide us with the desired outcomes.

Organisations are using data in various ways to drive their business growth such as in critical decision making, predictions, providing a personalized experience to the customers, predictive maintenance, streamlining their business operations and many other tasks.

With this much of advantages, the requirement of data scientists and engineers is drastically increasing among organisations. So if you are planning to explore this art of visualizing data, it is the best time. Here below I’m describing the top programming language which is currently very popular among data scientists. Let’s take a closer look:

#Python:

Python is considered as the most famous language among developers and data scientists assisting them with the development of enterprise, standalone, gaming, PC, and web application.

The powerful python libraries offer more than 130,000 packages to simplify our work. Python is the first choice for data scientists as it is very easy to learn due to its simple syntax and dynamic programming nature.

Python libraries like NumPy, SciPy, Matplotlib, SciKit-Learn, Theano, Keras, Pybrain etc., allows us to deal with the large or unstructured data sets. Data science with Python is nothing new; it is being used from the decades for complex ML works.

Big giants like Google, Facebook, Instagram, Quora, Spotify, and Netflix are already using it to accomplish their complex operational works in the development.

“According to the IBM’s prediction- the data scientists demand will soar 28 percent by the end of 2020.”

#R:
R programming language is getting data scientist’s attention due to its huge set of libraries reducing the major challenges faced in crunching and analyzing massive data sets. Whether it is about visualization, regression or classification- R fits best in all these scenarios.

It is an open source alternative to the applications like SAS or Matlab. Most of the data scientists find it as the most interesting language with software environment to deal with the statistical computing operations and graphics. The public package CRAN of R avails around 8000 contributed packages to fulfil developers demand.

It offers packages like dplyr, lattice, jsonlite, plotly, ggvis, rCharts etc., with the abilities to visualize correlation metrics, develop static graphics systems, interactive JS charts etc., like tasks. So, if you are willing to get involved with this art of dealing data then R programming language is one of the best options.

“The data science market is expected to reach 128.21 billion USD by 2022.”

#Julia:
Taking our conversation forward to the recently developed high-level Julia languages- it is getting famous after its introduction by MIT. All of its syntaxes are user-friendly and simple as the Python. It is being used for parallel computing, distributed executions, getting accurate and high-performance numerical based outcomes, and various other tasks associated with the field of data science.

Its extensive mathematical libraries are very helpful for data analysis. One can use packages and functions like Plots, StatPlots, boxPlot, etc., with supported Python or R libraries. It provides asynchronous input/output, logging, process control with scientific tooling to deal with the multidimensional datasets in minutes.

The high-speed performance of Julia is capable of handling the large and complex projects using a significant amount of data. Various benchmarks in Julia are almost thirty times faster than the Python programming making a plus for its side. The incredible interface engine offered by Julia is valuable for metaprogramming as well.

#Java:
Java which is getting used since the 90s is also a great choice among data engineers and data scientists. If you think that Java is just limited to the gaming, web or app development- you are not aware of the innovations in the field of machine learning and artificial intelligence empowered by it.

Java is a high-level and object-oriented language to deal with the real-world complexities. The best part is- the Java compiled bytecode can be easily executed over other environments. For creating ETL production code or writing advanced ML algorithms- Java is the ELKI, MALLET, Deeplearning4j, MOA etc. For those who are dealing with the complex numerical best option.

Java provides a great set of libraries for data science and machine learning such as Weka, computations- Java provides varieties of tools and packages to deal with it. Big organizations like Google, NASA, Oracle etc., are involved in getting the best from it.

“The data science market is growing at a CAGR of 35.6 percent between the periods of 2017-2022.”

#Scala:

Scala which is pronounced as the Scalable Language is also an open-source programming language for data analysis. The static type system and functional based programming make it stand among other languages like Java, Python etc., used in data science. The biggest Scala work can be seen through the Hadoop framework offered by Apache for big data analysis.

Another famous framework for big data called as Spark is using Scala in its environment. For the organization dealing with massive datasets generated every second such as in airline industries where it requires clustering and processing of the gathered information- Scala can be very useful.

Now, you have multiple options to start your data science career today. Just stick to one programming language you feel easy and start exploring the unlimited opportunities soaring across the digital lake. This field is not just limited to these 5 programming languages; you can also go through the other options such as Structured Query Language (SQL), JavaScript, or C etc.

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