With the rise of IoT, corporations have adopted technology at various levels of their administration to manage, guide, and evaluate performance. In the past, we have discussed the demands for data analysts and data scientists to keep up with emerging technologies that have taken over the world. With the significant marks of Big Data and IoT, it is clear that our world is operated by the likes of all kinds of data, but how are we processing these magnanimous data in the first place? Where can one start to get a foot into the world of data science? The number of big data applications on the market is growing exponentially; therefore, data scientists are considering different programming languages to increase the efficiency of these applications. Here are the top 5 programming languages to better understand data science.
Python
Python is known for its suitability for programmers of varying skill levels, from the students to intermediate developers, to experts and professionals, to develop programs not just for the web but also for desktop and command-line. One of the key features that make python a popular choice in the community is its in-built mathematical libraries and functions for calculating complex mathematical problems and data analysis. It also helps with mathematics, statistics, and scientific tasks with a simple syntax for easy adaptability. This enables even entry-level data scientists to use Python to create a quick prototype efficiently. Another factor for its favourability is the huge online developers' community support and their following for budding data scientists to seek guidance.
C/C++
C++ and C are both imperative languages to grasp the fundamentals of programming and computer science. This is because most new languages, frameworks, and tools use either C or C++ as their codebase. C is one of the closest languages to the inner workings of the computer as it can manipulate memory directly. Data scientists find C useful because it is a very compiled language. The source code has to be translated by a compiler into machine code. Its standard library is small and light on features, so other libraries have been developed to compensate for the missing functionalities. C++ is effective for its rapid processing capabilities therefore, it is able to compile over a gigabyte of data in less than a second. Since you can compile large data sets with C++ a lot more quickly, it is an excellent language for large, data-driven projects.
R-programming
R is open-source with the functionality of statistical software and data analysis tool. Data scientists are trained in an intensive environment for extensive research and visualising sufficient information from large datasets. This is because R is explicitly designed for data and has vastly more support for complicated analyses which explains why R is a popular choice of scholars and R&D professionals. R also includes statistical computing and graphics for data science projects.
SQL
SQL is an analytical technique that helps you extract valuable insights from data and provide effective frameworks for big data processing. It allows you to store, query, and change data. SQL is commonly used by companies to store structured data and can contain large amounts of data that you will encounter a lot of times as a data scientist. A Data Scientist requires SQL to work with structured data. Structured data is stored in relational databases. As a result, to query these databases, a data scientist must be proficient with SQL commands.
JavaScript
A fundamental part of data science is data visualization. Javascript is important in data science when you need to visualize interactive data in different ways. The data analysis process is so complex that you can’t simply imagine how would your data look. It is particularly important when you want to make available to everybody to those interactive visualizations through a web page for instance.
Data science as an occupation demands a lot of knowledge about many fields: statistics, data cleaning, visualisation, machine learning, reporting, good communication, and the list goes on. Importantly, it involves a lot of experimentation which needs the individual to be highly adaptable to changes and efficient in their programming skills. GoTraining offers all these courses for every level, at a reasonable price. Be sure to check out our site for more info.
