Best Programming Languages for Data Science in 2026

The question "which language should I learn for data science?" has a clear answer for most people, and it is not one language but two: SQL and Python. The others matter for particular roles and teams. Here is what each is actually used for, how SQL and Python divide the work, and an order to learn them that matches the job you want.

SQL: where the data lives

Most organisational data sits in relational databases and warehouses, and SQL is how you ask it questions: filter, join, group and aggregate, close to the data and usually faster than pulling everything into memory (PostgreSQL tutorial: the SQL language). Nearly every data role uses SQL daily, and interviews for analyst and data science roles almost always include it. It is also the most transferable skill here: the syntax changes little between PostgreSQL, SQL Server, MySQL, BigQuery and Snowflake.

Python: everything after the query

Python is a general-purpose language (About Python), which is its strength in data science: the same language that cleans data with pandas also trains models with scikit-learn, calls APIs, schedules pipelines and serves models over HTTP. Its ecosystem of data libraries is the main reason it became the default language for data science and machine learning. Developer surveys such as the Stack Overflow Developer Survey consistently place Python and SQL among the most used languages (Stack Overflow Developer Survey).

How the two divide the work

The same question, total orders and revenue by city, in both languages:

import sqlite3
import pandas as pd

rows = [("Hyderabad", "Books", 1200), ("Pune", "Toys", 450), ("Hyderabad", "Toys", 800),
        ("Chennai", "Books", 999), ("Pune", "Books", 300)]

con = sqlite3.connect(":memory:")
con.execute("CREATE TABLE orders (city TEXT, category TEXT, amount INTEGER)")
con.executemany("INSERT INTO orders VALUES (?, ?, ?)", rows)
print(con.execute("""
    SELECT city, COUNT(*) AS orders, SUM(amount) AS revenue
    FROM orders GROUP BY city ORDER BY revenue DESC
""").fetchall())

df = pd.DataFrame(rows, columns=["city", "category", "amount"])
summary = (df.groupby("city").agg(orders=("amount", "size"), revenue=("amount", "sum"))
             .sort_values("revenue", ascending=False).reset_index())
print(summary.values.tolist())

[('Hyderabad', 2, 2000), ('Chennai', 1, 999), ('Pune', 2, 750)]
[['Hyderabad', 2, 2000], ['Chennai', 1, 999], ['Pune', 2, 750]]

Identical answers. In practice, you do the heavy filtering and aggregation in SQL, where the data is, and pull the smaller result into Python for what SQL does poorly: statistical tests, machine learning, custom logic and charts. Knowing both lets you put each step where it runs best. Pandas vs SQL for data analysis goes deeper into that choice.

R: statistics and research

R is a language built for statistical computing and graphics (The R Project). It remains strong in academic research, biostatistics, epidemiology and some finance and pharmaceutical teams, with a deep catalogue of statistical packages and excellent plotting. If you join a team that works in R, learning it is straightforward once you know Python; otherwise, it is optional for most industry data science roles.

Scala and Java: around Spark and the JVM

Apache Spark, the most common engine for processing data across a cluster, is written in Scala and offers APIs in Scala, Java and Python, plus SQL (Apache Spark documentation). Most data scientists use Spark through Python (PySpark) or SQL. Scala and Java matter mainly for data engineers maintaining Spark applications or other JVM-based systems.

Julia, JavaScript and the shell

• Julia is designed for high-performance numerical and scientific computing (The Julia Programming Language). It is valued in some scientific and optimisation work, but its job market is small compared with Python's.
• JavaScript is useful for custom interactive visualisations on the web; most data scientists never need it, because notebook and BI tools cover charts.
• Bash and the command line are not a language choice so much as a daily tool: moving files, running scripts and working on remote servers.

What to learn, by role

For almost everyone, the order is the same: SQL and Python first, to a level where you can load, clean, query and summarise real data without looking everything up. Only then add what your target role or team uses. Learning a third language early usually slows down mastery of the two that every job needs. The data science career guide describes the roles in more detail.

Practising the two together

The most efficient way to learn SQL and Python is on the same project, because that is how they are used at work. Load a public dataset into SQLite or PostgreSQL, answer questions with SQL first, then pull the results into Python for charts, statistical tests or a simple model. You will quickly notice which questions are easy in each: a three-table join with a filter is a few lines of SQL and awkward in pandas, while a rolling statistic with custom logic is the reverse. Keeping both open side by side builds the judgement employers look for: not whether you know a language, but whether you know where each step belongs.

How much of each is enough to start

For SQL: joins of several tables, grouping with HAVING, subqueries, and window functions such as ROW_NUMBER and running totals. For Python: data structures, functions, reading files, and pandas for cleaning and aggregation. The SQL for data science article lists the queries worth practising.

SQL and Python are the two languages at the core of the Data Science course, from its Python for data science module to SQL for data science.

Sources

• Stack Overflow Developer Survey — Stack Overflow
• About Python — Python Software Foundation
• The SQL Language (PostgreSQL tutorial) — PostgreSQL documentation
• The R Project for Statistical Computing — The R Foundation
• Apache Spark documentation — Apache Spark documentation
• The Julia Programming Language — The Julia Project