9 Top Programming Languages for Data Science

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Programming skills are critical whichever direction you go in data science. While languages like Python, R, and SQL act as foundations for many data science or analytics roles, others are useful for career paths in areas such as data systems development or better suited specifically for aspiring data scientists.

Use this post as a starting place to explore nine top programming languages and when they’re used in data science:

  1. Python
  2. R
  3. SQL
  4. Scala
  5. Julia
  6. Javascript
  7. Java
  8. C/C++
  10. Bonus: Excel

How Is Programming Used in Data Science?

The field of data science relies on programming across all job functions, from automating cleaning and organizing raw data sets to designing databases to fine-tuning machine learning algorithms.

What Programming Language Is Best for Data Science?

R and Python are popular, foundational programming languages in data science, but choosing the right language to learn depends on your level of experience, role, and/or project goals.

The Top 3 Data Science Programming Languages

1. Python

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

Python is an open source object-oriented programming language, grouping data and functions together for flexibility and composability. In data science, it’s commonly for data processing, implementing data analytics algorithms, and training machine learning and deep learning algorithms. Python supports multiple data structures and uses a plain English syntax, making it a great language for beginner programmers.

When to use Python in data science? Python is a great place to start if you’re learning to code for the first time, want something scalable, and/or are looking to keep your career options open.

2. R

While Python is general purpose, R is more specialized, suitable for statistical analysis and intuitive visualizations.
R is built to handle massive data sets and complex processing through RStudio. Its statistics-specific syntax is intuitive for researchers with statistics backgrounds, and powerful visualizations offer more intuitive communication of results.

When to use R in data science: Data scientists with some programming experience or beginning data scientists looking to make a mark in the research field should consider learning R. If you have experience as a statistician, you’ll also recognize the structure of R.

3. SQL

Learning SQL, or structured query language, is vital for manipulating structured data. Large-scale datasets can contain millions of rows, making it difficult to find precisely what data you need. SQL is a querying language, allowing you to adjust, locate, and check massive data sets. As a domain-specific language, it’s convenient to manage relational databases.

“Scripting with Python, fundamental statistics, and SQL are critically important regardless of which direction you go in data,” said Gwen Britton, Associate Vice President of Southern New Hampshire University (SNHU) Global Campus STEM & Business Programs and instructor for edX MicroBachelors programs in data management and business analytics.

When to use SQL in data science: If you’re using relational databases, you must learn SQL.

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