Top 5 open source tools for learning data science.

Looking to start your journey as a data scientist? You’ll need the appropriate tools in order to gain literacy in what LinkedIn refers to as one of the most sought-after skills of 2016. Thankfully, access to (most of) the tools that allow you to process and digest these data minefields are readily available and free. The fact that they are open source allows for constant collaboration and innovation from top contributors. Here are the top 5 tools for your consideration:

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‍1. R.

Nope, not a typo. R is a programming language that allows for data manipulation and graphics, It is widely considered one of the most popular and easiest of the tools available. R makes use of the S language in its open source format and also includes an array of packages and guides to suit almost any user.

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2. Python.

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As a general-purpose programming language, Python has application in back-end web development and artificial intelligence, but it is also dynamic enough for data science. Don’t let its dynamic application scare you; Python is extremely beginner-friendly. Its syntax reads like English and is generally easy to understand. There are loads of tutorials that allow you to start learning gradually, and keep an eye out for the Monty Python references -- that’s where the language got its name!

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3. Weka.

Although not a programming language, Weka -- named for the New-Zealand based, flightless bird and also an acronym for the Waikato Environment for Knowledge Analysis -- is considered more of a workbench. Weka is a platform that facilitates a range of machine-learning activities, reducing or removing the need for multiple tools. It allows users to work with large sets of data and includes features such as preprocessing, classification, regression and clustering.

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4. SQL.

Pronounced “Sequel,” Structured Query Language is used for managing and querying data in relational database management systems (RDBMS). Along with Python and R, SQL forms the third side of the triangle of data science programming languages. With much of the world’s data housed in organised collections of tables known as relational databases, SQL is one of the fundamental languages to know in order to wrangle and extract data from them.

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5. Scala.

Scala is a great language to use for processing large amounts of data. In fact, many of the high performance data science frameworks built on Hadoop are usually written in Scala or Java. Scala is slowly being integrated into data scientists’ arsenal because of its increased speed and productivity. It may even begin to turn the dominant language triangle (mentioned in #4) into a robust square!

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With the accessibility of these free tools, there's no excuse not to start your learning journey with data science.