Which language is best for data science?

[Starlight-143]

Which language is best for data science?

[bhaumikmaan]

The best language for data science are:

1. Python - Undoubtedly the best in the game. Tons of libraries, easy to learn and huge community support
2. R – For being a statistical sowerhouse i.e. excellent for data visualization
3. Scala - Not much used but has strong support for distributed computing and good for big data

Hope that gives some clarity. Do mark as answered and completed

[Kurvaeshwar99]

Great question! The best language for data science depends a bit on your specific goals, but here are the top contenders:
Go with Python if you’re just starting or want all-around versatility.

Learn R if you’re deep into statistics or working in academia.

Know SQL no matter what — it’s super useful for any data science job.

[AmanShukla18]

Python is the best language for data science because it’s easy to learn and use. It has many helpful tools like pandas, NumPy, and scikit-learn that make working with data, building models, and creating charts simple. Python is also very popular, so there’s a big community and lots of support. This makes it a great choice for both beginners and experts in data science.

[4rshxnth]

Python is the most widely used language in data science due to its simplicity, vast library ecosystem, and strong community support for machine learning, data analysis, and visualization. It is often used alongside SQL for data querying and R for advanced statistical modeling.

[vipinwap07]

The best language for data science depends a bit on your goals and context, but here’s the quick breakdown:

✅ Python → #1 choice overall
• Huge ecosystem (pandas, NumPy, scikit-learn, TensorFlow, PyTorch, etc.)
• Tons of tutorials, community support, and real-world use
• Great for prototyping, visualizations (matplotlib, seaborn, plotly), and machine learning
• Easy to integrate with production systems

✅ R → Best for statistics-heavy work and academia
• Built for statisticians, with amazing packages like ggplot2, dplyr, caret
• Strong in bioinformatics, epidemiology, social sciences
• More niche in production ML/AI, but excellent for data analysis and reports

✅ SQL → Essential for data extraction and wrangling
• Not a standalone data science language, but critical for querying databases
• Most data science jobs expect the at least intermediate SQL

✅ Julia → Up-and-coming, niche
• Fast, great for numerical computing
• Still smaller ecosystem, but growing in areas like scientific computing and optimization

✅ Scala / Java → Big Data / Spark ecosystems
• If you’re working with huge datasets in distributed systems (Spark, Hadoop), Scala or Java comes in

⸻

💡 TL;DR recommendation:
If you’re starting or want maximum versatility → learn Python.
If you’re coming from a stats-heavy or academic background → add R.
Always sprinkle in SQL.

[codewithdhruba01]

Best Language for Data Science

Python is widely considered the best programming language for data science due to its:

• 🛠️ Rich ecosystem of libraries (e.g., pandas, NumPy, scikit-learn, TensorFlow, PyTorch)
• 👨‍🔬 Strong community support and vast number of tutorials
• 📊 Powerful tools for data visualization (matplotlib, seaborn, Plotly)
• Easy integration with machine learning and deep learning frameworks
• 💬 Readable, flexible, and beginner-friendly syntax

Other notable languages:

• R: Excellent for statistical analysis and visualization
• SQL: Essential for database querying and data manipulation
• Julia: Gaining popularity for high-performance numerical computing

For most data science projects, Python is the go-to choice.

[rajkhatri398]

Start with Python (best balance of ease, libraries, and job demand).

Learn R if in academia/statistics.

Use SQL for database work.

Consider Julia/Scala for niche cases (speed/big data).

[Chirag037]

Hi everyone! 👋

I'm learning data science and wanted to ask:
What programming languages do you use (or recommend) for data science and why?

I’ve done a bit of research and found that several languages are commonly used, each with its own strengths. Here's a quick list I came across:

🐍 Python – Most popular choice. Easy to learn and has tons of libraries like pandas, NumPy, scikit-learn, matplotlib, and TensorFlow.

📊 R – Great for statistical analysis and advanced data visualization. Used a lot in academia and research.

💾 SQL – Not a full programming language, but essential for querying and managing data from databases.

⚡ Julia – High-performance language built for numerical computing and large datasets. Still growing in popularity.

☕ Java – Used in big data frameworks like Hadoop. Strong performance and portability in production systems.

🔥 Scala – Works well with Apache Spark for big data processing. Functional + object-oriented features.

💡 MATLAB – Used in academic and engineering circles for numerical computing and simulations.

[SundarBau]

1. Python

Why it’s best for data science:

Easy to learn: Simple syntax, readable code—great for beginners.

Powerful libraries:

Data manipulation: pandas, NumPy

Visualization: Matplotlib, Seaborn, Plotly

Machine learning & AI: scikit-learn, TensorFlow, PyTorch, Keras

Versatile: Can be used for web apps, automation, cloud deployment, and data pipelines.

Community support: Tons of tutorials, forums, and prebuilt solutions.

2. R

Why it’s useful:

Specialized in statistics and data visualization.

Libraries like ggplot2 and shiny make charts and dashboards easy.

Preferred in academic research and bioinformatics.

Limitations:

Not as flexible as Python for general programming or deploying ML models in production.

3. SQL

Essential for working with databases.

Allows for efficient data extraction, aggregation, and filtering.

4. Julia

Fast and efficient for numerical computing and large-scale data processing.

Growing in popularity, but with a smaller community than Python.

5. Scala (with Apache Spark)

Good for big data processing.

Used in data pipelines and distributed computing projects.

[Gupta-02]

If you’re starting out in data science, Python is the best language to go with because it has a huge ecosystem of libraries like Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch, making everything from data cleaning to machine learning super smooth. It’s beginner-friendly, widely used in the industry, and has tons of community support, so you’ll never feel stuck. Alongside Python, knowing SQL is almost a must because most real-world data lives in databases. If you’re more into hardcore statistics or research, R is also great, and for speed in heavy computations, some people explore Julia, though it’s still growing. But honestly, Python + SQL will cover 90% of what you’ll need in real-world data science.

[Sebastianrix]

Python, with pandas lib. It allows data-frames. Very useful. Also everything machine-learning / Deep-learning is working with Python TesnorFlow/PyTouch and Cuda.

Python is the way

[Jundel-Malazarte]

Python is the most versatile and popular for data science due to its simplicity and vast libraries (NumPy, Pandas, TensorFlow) for ML, AI, and general analysis.