Which Programming Language Should Data Scientists Learn First? (2024)

FAQs

Which Programming Language Should Data Scientists Learn First? ›

Due to its simple and readable syntax, Python is often referred to as one of the easiest programming languages to learn and use for beginners. If you are new in data science and don't know which language to learn first, Python is one of the best options.

1. Python. Python is a general purpose popular programming language. Learning Python can open up doors not only in data science, but also in web and software development.

The most popular coding language for data science nowadays is Python. This dynamic, all-purpose language is by nature object-oriented.

If this is your first foray into computer programming, you may find Python code easier to learn and more broadly applicable. However, if you already have some understanding of programming languages or have specific career goals centered on data analysis, R language may be more tailored to your needs.

Typically, SQL is a good programming language to learn first. As a tool, SQL is essential for retrieving content from relational databases. Compared to Python, SQL may be easier for some people to learn.

Performance: C++ is generally faster than Python, particularly for computationally intensive tasks. This can be an advantage in data science applications where speed is important.

Is Python Necessary in the data science field? It's possible to work as a data scientist using either Python or R. Each language has its strengths and weaknesses. Both are widely used in the industry.

The short answer is yes, coding is necessary to become a data scientist. Data science requires an understanding of programming languages such as Python and R, as well as some knowledge of statistics and mathematics.

If you want to work more with data scientists who don't use Python, maybe choose R. If you want a language made for working with data and also improving performance, maybe choose Julia or SQL. Actually, definitely learn SQL. It's a database query language every developer and data scientist should know.

Traditionally, data science roles do require coding skills, and most experienced data scientists working today still code.

Is Python replacing R? ›

Python has gained wide popularity because of its readable syntax, making it easy to learn under expert guidance. R is less popular when compared to python. However, the usage of this language is increasing exponentially for business applications. Mozilla uses Python programming to explore its broad code base.

The truth is, R is far from dead. While it's true that Python has gained significant traction in recent years, R remains a powerful language that offers unique benefits for data scientists. One of the critical advantages of R is its focus on statistics and data visualization.

If you're passionate about the statistical calculation and data visualization portions of data analysis, R could be a good fit for you. If, on the other hand, you're interested in becoming a data scientist and working with big data, artificial intelligence, and deep learning algorithms, Python would be the better fit.

Compared to Python, SQL is a much simpler language. It's also exclusively used for data. That means it's easier to learn, and it provides the quickest, most efficient means of performing simple data analyses.

Learning Curve

SQL is considered simpler to learn than Python since it only allows a limited number of operations; however, mastering its syntax and structures can take some time. On the other hand, Python has an extensive library, making it easier to code but it requires more time and effort to master than SQL.

Can Python Replace SQL? Python can replace some of the tasks that developers might otherwise use SQL for. However, Python can't completely replace SQL since each language serves different purposes.

1. Does Data Science Require Coding? Yes, data science needs coding because it uses languages like Python and R to create machine-learning models and deal with large datasets.