Programming Languages for Data Science
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| Top 10 Programming Languages |
Java
Java is one of the most commonly used open-ended programming languages used for many years. When it comes to big data, many tools like Spark, Hadoop, Hive, and Flink are written in Java. It comes with a significant number of libraries and tools to develop your coding techniques. Java's application is extensive, and many large enterprises look for developers with Java expertise. In the current market there are many courses available to complete your Masters in Data Science online training.
Python
Python comes with a bundle of benefits, and for this reason, it has a special place in programming languages. It is an open-source, object-oriented and flexible language; It is easy to learn. The programming language provides the most important benefit of ease of coding; Python's programming syntax is similar to English. As a result, you do not need to focus on language; Instead, you can focus on logic. Python should be a weapon of data science if you want to upgrade your skills.
SQL
When it comes to relational database management systems, you cannot just ignore SQL or structured query language. Since the role of a data scientist is to convert raw data into useful insights for decision making, SQL plays an essential role in the data retrieval process. To develop your career as a data scientist, one must know how to extract data from various databases and play with it.
MATLAB
MATLAB is the best option for handling data files such as images, videos, sensors, binaries, telemetry and other real-time formats. It is available with a full set of machine learning and statistics. Additionally, it includes advanced methods such as system identification, nonlinear optimization, and prebuilt algorithms to process video, picture, and control system design and financial modeling.
Scala
This popular language is an extension of Java and is also known as a scalable language. It is compatible with JVM or Java virtual machines, and is the primary tool for handling huge amounts of big data. Its data should be a tool for scientists because it supports both higher-order tasks and anonymous tasks.
R
R is a unique programming language, and brings many unique features that are not present in any other language. Therefore, if you want to move forward as a data scientist, learning R is essential. It is a vector language, and it can perform many tasks at a given time. Single functions can be used without including functions available in R in a loop. The use of R is gradually becoming famous, and therefore it is necessary to learn if you want to get a professional certificate in data science.
C (C ++)
C ++ has a permanent place in any data scientific arsenal. C ++ education is necessary, as well as more accessible. It is simple to use and is one of the fastest programming languages in the IT industry. Since it is a low-level language, it offers the data scientist to place extensive commands on applications.
JavaScript
When it comes to data visualization, JavaScript is the champion among other tools. The data visualization task becomes more straightforward with the huge library JS comes with. The Tensorflow.js release, JS, can bring machine learning into play and allow developers to reclaim the benefits from it.
Julia
It is another open-source programming language, and is simple to use. It is intuitive and comes with high efficiency as it exceeds the speed of Python and R. Therefore it is necessary to have a weapon for a data scientist in their arsenal.
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