![]() Java also supports good libraries for ML, such as MLib.īest for: Big-data computing with light data analysis, general-purpose needs MATLAB For some basic tasks, there are a number of built-in classes that can compute summaries of a dataset efficiently. Some of the big data processing frameworks such as Hadoop and Spark are highly compatible with Java. It works well in combination with languages such as R because it offers more general features and libraries that can be useful for low-level cleaning. Java may be used for many general purposes, but some people leverage it for data science as a preprocessing tool to clean up the data. ![]() Meanwhile, various integrated development environments such as Jupyter Notebook offer an interactive experience for Julia coders.īest for: Hard science and mathematical analysis Java The compiler is able to target multiple chip architectures it’s not uncommon for scientists to find that Julia code is running several times faster than other languages. The most attractive quality of Julia may be its speed. There are more than 4000 different packages for different tasks across scientific computing. There are, for example, excellent libraries for exploring differential equations, Fourier transforms, and quantum physics. Today, it supports a good collection of routines for visualization, data science, and machine learning (ML). This language is a general-purpose tool for creating software that handles basic chores such as IO, but Julia has attracted a number of scientists over the years because it does a particularly good job with numerical tasks. Frameworks such as PyTorch and TensorFlow can also take advantage of specialized hardware to greatly speed up analysis.īest for: Beginners and those with broad general-purpose needs Julia The language is also very popular with artificial intelligence scientists and that can be very useful when the data analysis requires some help from AI. ![]() The scientists have also integrated the language with frameworks for parallel programming such as Apache Spark to help with processing especially large datasets. Packages such as NumPy, SciPy, Pandas, and Keras are just a few of the most notable. The real strength of the language is the large collection of libraries devoted to data science. Many scientists learn Python to do all of their computing, from data collection to analysis. This language began as a scripting language with a clean syntax, but it has grown to be one of the favorites in labs throughout the world. R makes it possible to work easily with other packages.īest for: Those with a broad need for data science and statistical analysis Python Others like to work with other development tools such as Eclipse or some command-line interfaces because they want to integrate code from other languages that may be used to collect or pre-clean the data. Many data scientists like to use integrated development environments such as R Studio, which is optimized for the task at hand. There are even some nice libraries such as Sweave and knitr that turn the data into polished, typeset reports using LaTeX. Over the years, other scientists have written and distributed very good open-source libraries that tackle many of the most common statistical and mathematical algorithms. ![]() The R language itself includes data structures such as data frames that are designed to work with large blocks of tabular data. R was built for statistical analysis and it remains a favorite for many devoted data scientists. Some data scientists are building data pipelines with several different technologies at each stage, each leveraging the best features of a particular language. Sometimes one is not enough, and several languages are the answer. Here’s a list of some of the best languages for data science-ones that make good choices for your next project. General-purpose languages that are already the foundation for the main workflow can be extended to either filter and clean the data or maybe even handle some of the analysis. There are also a number of other choices that can get the job done well. They’re great first choices, and no one can go wrong using them. ![]() There are a few languages, such as R and Python, that dominate the spotlight because they’re often used to teach the courses. The good news is that there are plenty of good programming languages for doing this work. The job of a data scientist is to turn all of those endless bits into coherent analysis so that data users can begin to look for answers in the sea of information. ![]()
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