Welcome to “Other Languages for Data Science”. After watching this video, you will be able to review other languages like Java, Scala, C++, JavaScript, and Julia, and explore how each is used in Data Science Previously, we reviewed Python, R, and SQL. In this lesson, we will review some other languages that have compelling use cases for data science. Scala, Java, C++, and Julia are probably the most traditional data science languages. However, JavaScript, PHP, Go, Ruby, Visual Basic and many others have found their place in the data science community. Let us go through some notable highlights about a few of them. Java is a general-purpose tried and tested object-oriented programming language. It has huge adoption in the enterprise space and was designed to be fast and scalable. Java applications are compiled to bytecode and run on the Java Virtual Machine or JVM. Some notable data science tools built with Java include: Weka for data mining, Java-ML for machine learning, Apache MLlib makes machine learning scalable, and Deeplearning4 for deep learning. Hadoop is another application of Java which manages data processing and storage for big data applications running in clustered systems. Scala is a general-purpose programming language that provides support for functional programming and is a strong static type system. The Scala language was constructed to address the shortcomings of Java. It is also inter-operable with Java as it runs on the JVM. The name Scala is a combination of scalable and language. This language is designed to evolve with the requirements of its users. For data science, the most popular program built with Scala is Apache Spark. Spark is a fast and general-purpose cluster computing system that provides APIs, which make parallel jobs easy to write. It has an optimized engine that supports general computation graphs. Spark includes Shark, which is a query engine, MLlib for machine learning, GraphX for graph processing, and Spark Streaming. It was designed to be faster than Hadoop. C++ is a general-purpose programming language. It is an extension of the C programming language or "C with Classes.” C++ improves processing speed, enables system programming, and provides broader control over the software application. Many organizations that use Python or other high-level languages for data analysis and exploratory tasks rely on C++ to develop programs that feed data to customers in real-time. For data science, TensorFlow is a popular Deep Learning library for dataflow that was built with C++. Although C++ is the foundation of TensorFlow, it runs on a python interface, so users don’t require the knowledge of C++ to run it. MongoDB is a NoSQL database for big data management that was built with C++. Caffe is a deep learning algorithm repository built with C++ with Python and Matlab bindings. A core technology for the world wide web, JavaScript is a general-purpose language that extended beyond the browser with the creation of Node.js and other server-side approaches. Javascript is NOT related to the Java language. For Data Science, undoubtedly TensorFlow.js is the most popular implementation. TensorFlow.js makes machine learning and deep learning possible in Node.js as well as in the browser. TensorFlow.js was also adopted by other open-source libraries including brain.js and machinelearn.js. Another implementation of JavaScript for Data Science is R-js. The project R-js has re-written linear algebra specifications from the R Language into typescript. This sets the foundation for future projects to implement more powerful math base frameworks like Numpy and SciPy of Python. Typescript is a superset of JavaScript. Finally, Julia was designed at MIT for high-performance numerical analysis and computational science. Julia provides speedy development like Python or R, while producing programs that run as fast as C or Fortran programs. It’s compiled which means that Julia code is executed directly on the processor as executable code. It calls C, Go, Java, MATLAB, R, Fortran, and Python libraries, and has refined parallelism. Julia as a language is only 8 years old, written in 2012, but there is a lot of promise for its future impact on the data science industry. One great application of Julia for Data Science is JuliaDB, which is a package for working with large persistent data sets. In this video, you learned that Data science tools built with Java include Weka, Java-ML, Apache MLlib, and Deeplearning4. For data science, a popular program built with Scala is Apache Spark, that includes Shark, MLlib, GraphX, and Spark Streaming. For data science, TensorFlow, MongoDB and Caffe were built with C++. Programs built for Data Science with JavaScript include TensorFlow.js and R-js. One great application of Julia for Data Science is JuliaDB.
