The first thing needed to become a Data Science professional is to know multiple programming languages and the faster you learn the better. Every Data Science enthusiast must be passionate about coding and coding experience is a must to become a Data Scientist. But before you jump on to writing piles of code you have to understand the specific domain you’ll be working on.
Domains of Data Science
Along with coding skills, a Data Scientist should also have analytical and problem-solving skills. A data scientist is a proficient specialist adept at employing mathematical and statistical methodologies to effectively process, scrutinize, and derive insights from data. This field encompasses various domains, including but not limited to machine learning, deep learning, network analysis, natural language processing, and geospatial analysis. The execution of data science tasks predominantly hinges on harnessing the computational capabilities of computers. Programming languages for Data Science stands as the pivotal methodology enabling data scientists to engage with and issue commands to computer systems.
Let’s talk about the most in-demand programming languages a Data Scientist must have in 2023 and beyond.
Programming Languages for Data Science
These are the coding languages that are required for a Data Science professional:
This is the maestro of all coding languages when it comes to Data Science and any Data Science task you can imagine can be executed using Python. Python possesses the capability to execute a broad spectrum of tasks, ranging from data preprocessing, visualization, and statistical analysis, to the deployment of machine learning and deep learning models. The syntax of Python is quite simple and easy to understand. Consequently, beginners in Data Science always start with learning Python, the best programming language for Data Science.
Another programming language that is explicitly designed for Data Science is R. This is an open-source domain-specific language that has gained popularity after Python in the Data Science realm. Therefore, learning Python or R or both is the best scenario in Data Science and is going to be a plus for you. Working directly with R is not a big deal but people commonly use Rstudio, a powerful third-party interface that integrates various capabilities, such as data editor, data viewer, and debugger.
Database management is one of the most fundamental jobs of a Data Scientist. SQL or Structured Query Language gives programmers the ability to communicate with, edit, and extract data from the database. The main funda is that by knowing SQL you can work with various databases where SQLite, MySQL, and PostgreSQL are the primarily used systems. SQL is a versatile language and the syntax is very easy to learn.
In the past decade, JAVA has dropped its rank below Python but has become highly effective when it comes to website development. Java Virtual Machine provides a solid and efficient framework for popular big data tools, such as Hadoop, Spark, and Scala. However, the JAVA ecosystem is quite a reliable platform where endless technologies, software applications, and websites collaborate.
Released in 2011, Julia is a star in the making in the Data Science profession and it has flabbergasted the entire Data Science community through its awesome numerical computing. It has a high speed, clear syntax, and versatility- the main reason behind its popularity. Though it has a smaller community without libraries as its main competitors, several organizations have shown trust in Julia.
Scala is a multi-paradigmatic language exclusively designed to be a clearer and less wordy alternative to Java. It was released in 2004 and now is one of the most in-demand programming languages for machine learning and big data. Scala, by its compatibility with the Java Virtual Machine, facilitates seamless integration with Java, rendering it an ideal language for complex, distributed big data initiatives. A notable illustration of this synergy is the utilization of Scala as the programming language of choice in the development of the Apache Spark cluster computing framework.
C and C++ outpace many programming languages in terms of speed, rendering them highly suitable for the development of big data and machine learning applications. Notably, some pivotal components of widely used machine learning libraries, such as PyTorch and TensorFlow, are coded in C++.
However, it’s important to acknowledge that C and C++ are inherently intricate due to their low-level nature. Consequently, while they may not be the initial choices for those venturing into the realm of data science, attaining proficiency in these languages can significantly enhance one’s skill set and career prospects, provided a solid grasp of fundamental programming concepts is established.
With the advancement of mobile applications and IoT, the need for more mobile-friendly applications has increased over the last decade. Apple conceived Swift as a tool aimed at simplifying app development to expand its app ecosystem and bolster customer engagement. Following its introduction in 2014, a collaborative effort between Apple and Google swiftly ensued, marking the pivotal role of Swift in bridging the realms of mobile technology and machine learning.
The good news is that Swift is no longer restricted to the iOS ecosystem but can operate quite independently on Linux. It is now compatible with TensorFlow and is interoperable with Python. Therefore, a mobile developer who has grown a ferocious interest in Data Science should learn Swift swiftly.
Google introduced Go in 2009 and ever since it has stolen the spotlight, especially for machine learning projects. Go is known to be the 21st-century C with C-like syntax and layouts. Data Science professionals are becoming fans of Go because it is a flexible and easy-to-understand language. Though the community is small Go is a good ally for machine learning tasks.
Now that you have a clear understanding of the programming languages a Data Science professional requires, fasten your seatbelt and start learning coding. Each language has its traits and does exceptionally well in different domains. However, many coding languages are versatile and can be used in multiple genres. Therefore, choose your Data Science wisely based on your coding experience plus a new programming language learning capability.