Data Science is one of the most popular skills in digital era. This is due to the development of data industry which driven by the emergence of various new technologies. In order to be able to compete in the digital era, a data scientist needs to learn how to apply fundamental programming language to solve real-world data science problems. Five Recommended Programming Languages for Data ScientistA programming language is a set of instructions that produce various kinds of output. It has multiple applications and has been used to implement algorithms. Nowadays, there are many programming languages available such as Python, R, SQL, Java, and C++ which can help data scientists in doing their projects or tasks. First, Python is one of the most widely used programming language. It could accommodate data manipulation and data modelling as well as integrates other programming languages such as SQL. Next, R is a programming language that deals with the statistical and graphic sides of data science. It provides some statistical computing and analysis such as time series analysis, clustering, statistical tests, linear and non-linear modelling. Continue to the next programming languages, SQL or Structured Query Language helps data scientists to deal with database management systems. It is extremely convenient for storing, manipulating, and retrieving data in relational databases. Then, Java is a programming language that used to create backend systems and desktop, mobile, or web applications. However, in data science, Java is used to perform tasks involved in data analysis, deep learning, natural language processing, data mining, and others. Lastly, C++ is one of the earliest programming languages, although it could be complicated to learn, C++ can be an efficient and effective option as it has very rapid processing capabilities. Choose the Right Programming Language for Your Data Science ProjectProgramming language and framework are constantly evolving, makes it harder for developers, analysts, and researchers to choose the best language for their projects or tasks. Before using particular programming language for projects or tasks, data scientists should consider several things in advance.First, data scientists should look at their industry type and determine which language that commonly used by their peers and competitors, as well as look at their project scope and consider whether the language could meet the need of the project or not. After that, experiences in the world of data science and experiences in delivering projects could also influences the selection process of programming language. For example, beginner data scientists might choose python since it lets them code the fragments of an algorithm with ease. ConclusionIn doing projects or assignments, a data scientist can use several popular and recommended programming languages such as Python, R, SQL, Java, and C++. In addition, there are several things that need to be considered by a data scientist when choosing a programming language such as industry type, scope of the project, experiences in the world of&nbsp;<a href="https://multimatics.co.id/academy/program/certified-data-science-practitioner-cdsp" rel="noopener noreferrer" target="_blank" style="color: rgb(54, 110, 168); font-family: Poppins; font-size: 18px;">data science</a>, and experiences in delivering project. Reference:Sharma., R. 2021. upGrad blog. Top 6 Data Science Programming Languages 2021 [Hand-Picked]. Retrieved From: https://www.upgrad.com/blog/data-science-programming-languages/Tyagi., H. 2020. FreeCodeCamp. How to Choose the Best Programming Language for your Data Science Project. Retrieved From: https://www.freecodecamp.org/news/how-to-choose-the-best-programming-language-for-your-data-science-project/Van Der Aalst, W. (2016). Data science in action. In Process mining (pp. 3-23). Springer, Berlin, Heidelberg.

Data Science is one of the most popular skills in digital era. This is due to the development of data industry which driven by the emergence of various new technologies.

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Understand Programming Languages to Dive Deeper into Data Science