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At Neartec, we can provide your Programming Development staff right here on the West Coast though our nearshore outsourcing platform based out of Tijuana, Mexico.
The benefits of Programing Development include:
At Neartec, we design in any programming language, according to your development needs, such as those shown below:
Launched in 1995 as a direct descendant of the old programming language S, R has become stronger. Written in C, Fortran and itself, the project is currently supported by the R Foundation for Statistical Computing
– Excellent range of high quality open source packages. R has a package for almost every quantitative and statistical application imaginable. This includes neural networks, non-linear regression, phylogeny, mapping, and many, many others.
– The basic installation comes with comprehensive statistical functions and methods. R also handles matrix algebra particularly well.
– Data visualization is a key strength with the use of libraries such as ggplot2.
R is a powerful language that excels in a variety of data visualization and statistical applications, and being open source allows for a very active community of contributors. Its recent growth in popularity is a testament to how effective it is.
Guido van Rossum introduced Python in 1991. Since then, it has become an extremely popular language in general use, and is widely used in the data science community.
– Python is a very popular and general-purpose programming language. It has a wide range of specific modules and community support. The main desktop GIS like ArcGIS (with ArpPy), QGIS (with PyQGIS) or gvSIG introducing Python.
– Python is an easy to learn language. The low entry barrier makes it a first language, which is ideal for those who are new to programming.
– Packages like pandas, scikit-learn and Tensorflow make Python a solid choice for advanced self-learning applications.
Python is a very good language choice for data science, and not just at the entry level. Much of the data science process revolves around the ETL (Extraction-Transformation-Load) process. This makes the generality of Python fit perfectly. Libraries like Google’s Tensorflow make Python a very exciting language for machine learning.
SQL (“structured query language”) defines, manages and queries relational databases. The language appeared in 1974 and has since undergone many implementations, but the basic principles remain the same.
License: Varies, as some implementations are free and others are proprietary.
– Very efficient in queries, updating and manipulation of relational databases.
– The declarative syntax makes SQL a very readable language. There is no ambiguity about what to do
– SQL is used in a wide range of applications, so it is a very useful language to be familiar with. Modules like SQLAlchemy make integration of SQL with other languages easy.
SQL is more useful as a data processing language than as an advanced analytical tool. However, much of the information science process depends on ETL and the longevity and efficiency of SQL is proof that it is a very useful language for the modern data scientist.
Java is an extremely popular language that runs on the Java Virtual Machine (JVM). It is an abstract computer system that allows perfect portability between platforms. Currently supported by Oracle Corporation.
License: Free! Legacy, proprietary versions.
– Ubiquity. Many modern systems and applications are based on a Java back-end. The ability to integrate data science methods directly into the existing code base is powerful.
– Strongly typed. Java is a good language when it comes to ensuring type security. For mission-critical big data applications, this is very important.
– Java is a general purpose, high performance compiled language. This makes it suitable for writing efficient ETL production code and very computationally intensive machine learning algorithms.
There is much to be said for learning Java as a data science language of first choice. Many companies will appreciate the ability to integrate data science production code directly into an existing code base, and we find the performance of Java and type security very advantageous.
However, a variety of specific statistics packages are not available. That said, this is a language to consider, especially if you already know R and/or Python.
Developed by Martin Odersky and released in 2004, Scala is a language that runs on the Java Virtual Machine (JVM). It is a multi-paradigmatic language, which allows both object-oriented and functional approaches. The Apache Spark cluster computing framework is written in Scala.
– Scala + Spark = High performance cluster computing. Scala is an ideal language for those working with large volume data sets.
– Multi-paradigmatic: Scala programmers can have the best of both worlds. Both object-oriented and functional programming.
– Scala is compiled in the Java bytecode and executed on a JVM. This allows for interoperability with the Java language itself, making Scala a very powerful general-purpose language, as well as being suitable for data science.
When it comes to using cluster computing to work with Big Data, Scala + Spark are fantastic solutions. If you have experience with Java and other static typing languages, you will also appreciate the features of Scala.
Launched in 2011, Julia impressed the world of numerical computing. Her profile was raised by early adoption by several major organizations, including many in the financial industry.
– Julia is a JIT (‘just-in-time’) compiled language, which allows it to perform well. It also offers the simplicity, dynamic typing and scripting capabilities of a language interpreted as Python.
– Julia was designed specifically for numerical analysis. But it also offers general purpose programming.
– Readability. Many language users mention this as a key advantage.
MATLAB is a numerical computing language used in academia and industry. It was developed and licensed by MathWorks, a company established in 1984 to commercialize the software.
Licensing: Owner – prices vary by case.
– Designed for numerical computing. MATLAB is suitable for quantitative applications with sophisticated mathematical requirements, such as signal processing, Fourier transforms, matrix algebra, and image processing.
– Data visualization. MATLAB has extensive built-in plotting capabilities.
– MATLAB is often taught as part of undergraduate courses in quantitative subjects such as physics, engineering, and applied mathematics. As a result, it is widely used in these fields.
The widespread use of MATLAB in a variety of quantitative and numerical fields in both industry and academia makes it a serious option for data science.