Key Languages Used in Data Science

The language you choose to learn will depend on the things you need to accomplish and the problems you need to solve. There are many roles available for people who are interested in getting involved in data science,

Business Analyst, Database Engineer, Data Analyst, Data Engineer, Data Scientist, Research Scientist, Software Engineer, Statistician, Product Manager, Project Manager

Let's mainly focus on main 3 languages:

Python

If you already know how to program, then Python is great for you because it uses clear and readable syntax. You can create the same programs from other languages with less code using Python.

Python is super handy in lots of areas, including data science, AI and machine learning, web development, and even Internet of Things (IoT) devices like the Raspberry Pi.

It has a large, standard library that provides tools suited to many different tasks including but not limited to Databases, Automation, Web scraping, Text processing, Image processing, Machine learning, and Data analytics.

• Data Science: Pandas, Numpy, SciPy, Matplotlib

• Artificial Intelligence: TensorFlow, PyTorch, Keras, and Scikit-learn

• Natural Language Processing: Natural Language Toolkit (NLTK).

R

R is another language backed by a large global community of folks who love using it to tackle big problems. Statisticians, mathematicians, and data miners use R to develop statistical software, graphing, and data analysis.

R Language's array-oriented syntax makes it easier to translate from math to code for learners with no or minimal programming background. R has become the world’s largest repository of statistical knowledge.

R integrates well with other computer languages like C++, Java, C, .Net, and Python. Using R, you can get instant results for common math operations like matrix multiplication.

SQL

It's scope is limited to querying and managing data. While it is not a “Data Science” language, data scientists regularly use it because it is simple and powerful!

This language is great for handling structured data, which includes relationships between entities and variables. SQL was created specifically for managing data in relational databases.

SQL interfaces have also been developed for many NoSQL and big data repositories. The SQL language is subdivided into several language elements, including: Clauses, Expressions, Predicates, Queries, and Statements.

When performing operations with SQL, the data is accessed directly, without needing to copy the data separately, which can considerably speed up workflow executions. SQL behaves like an interpreter between you and the database. SQL is different from other software development languages because it is a non-procedural language.

This article explores various programming languages and their applications in data science and related fields. It highlights the versatility and readability of Python, the statistical prowess of R, and the data management capabilities of SQL. Each language's strengths and common use cases are discussed, providing guidance on choosing the right language based on specific needs and problems to solve.