A Data Science Tech Stack is the collection of tools, software, and platforms used to collect, clean, analyze, and visualize data.
It includes everything from programming languages and databases to machine learning frameworks and dashboards.
Think of it as the “toolbox” for data scientists.
Quick Snapshot: What’s In & Out in 2025
| Category | In (2025) | Out (Fading) |
| Programming Language | Python, Julia | R (declining use) |
| IDEs & Notebooks | JupyterLab, Deepnote | Classic Jupyter Notebook |
| Data Storage | Snowflake, Delta Lake | Hadoop (legacy systems) |
| ML Tools | PyTorch, Hugging Face | TensorFlow 1.x |
| Workflow Orchestration | Prefect, Dagster | Airflow (still used, but aging) |
| BI/Visualization | Streamlit, Observable | Tableau (still popular, but aging) |
| Data Cleaning | Pandas, Polars | Excel-heavy workflows |
Programming Languages: Python Reigns, Julia Gains
Python is still the go-to for data science. Its simplicity and massive library support keep it on top.
But in 2025, Julia is gaining real traction—especially in academic and high-performance computing circles.
What’s losing ground?
R is still used in some niches, but many teams are shifting to Python or Julia for more flexible workflows.
Example:
A financial analytics firm now uses Julia for real-time risk modeling due to its speed, while Python handles ETL and visualization tasks.
IDEs and Notebooks: Time for JupyterLab and Deepnote
The old Jupyter Notebook is slowly being replaced by JupyterLab, which offers tabs, extensions, and better file handling.
Deepnote, a collaborative notebook tool, is also rising—great for remote teams and cloud-based data science.
Why it matters:
You can now code, write, and present—all in one space. No need for switching tools.
Data Storage & Lakehouses: Snowflake and Delta Lake Dominate
Traditional systems like Hadoop are fading in favor of modern, cloud-based storage platforms.
What’s In:
- Snowflake: Scalable, serverless, and works with most cloud services.
- Delta Lake: Adds structure to your data lakes for better performance and reliability.
💡 These systems simplify storage and make it easier to build real-time pipelines.
Machine Learning: PyTorch and Hugging Face Lead the Pack
PyTorch is now the top choice for deep learning. It’s easier to debug and better for research.
Hugging Face is exploding in popularity. Their pre-trained models make NLP tasks faster and easier.
On the decline:
TensorFlow 1.x—it’s considered outdated. Many are moving to PyTorch or TensorFlow 2.x with Keras-style APIs.
Workflow Orchestration: Prefect and Dagster Rise
Managing data pipelines is key in any tech stack. In 2025, tools like Prefect and Dagster are preferred over older systems.
Why?
They’re easier to use, offer better error handling, and integrate well with modern cloud platforms.
Apache Airflow is still around but showing its age.
BI and Visualization: Streamlit Is the New Favorite
Streamlit is a lightweight way to build dashboards with Python—without needing to be a web developer.
Observable is also gaining fans with its reactive JavaScript notebooks.
Fading:
Tableau is still used widely but feels heavy and slow for fast, iterative data projects.
Data Cleaning: Pandas Rules, Polars Rising
Pandas remains the top choice for cleaning and manipulating data in Python.
But now Polars is emerging as a faster, more memory-efficient option—especially for larger datasets.
Excel-based workflows are becoming rare in serious data science teams.
Bonus Tools on the Rise in 2025
- dbt (Data Build Tool) – For transforming data in warehouses.
- LangChain – For building LLM-powered applications.
- MLflow – For tracking ML experiments and deployments.
- DVC (Data Version Control) – For managing data alongside code.
Tools That Are Slowly Fading Away
Some tools aren’t dead yet, but they’re slowly being replaced:
- SAS – Still used in some industries, but losing ground to open-source options.
- SPSS – Once popular in research, now seen as outdated.
- Excel-only setups – Great for beginners, but too limited for most teams.
Infographic: The 2025 Data Science Tech Stack
Real-World Example: A Startup’s Stack in 2025
A startup in e-commerce uses:
- Python for data wrangling
- JupyterLab + Streamlit for quick reporting
- Snowflake as their data warehouse
- Prefect to manage pipelines
- Hugging Face for customer sentiment analysis
- MLflow for model tracking
This modern stack helps them move fast, test ideas, and scale efficiently.
Final Thoughts: Build a Stack That Grows With You
In 2025, the Data Science Tech Stack is all about speed, collaboration, and flexibility.
Before choosing your tools, ask:
- Does it scale?
- Is it easy to learn and use?
- Can it integrate with the rest of your stack?
Trends will keep shifting, but the goal stays the same: turn data into insight quickly and clearly.
Frequently Asked Questions (FAQ)
1. What is a Data Science Tech Stack?
Answer:
A Data Science Tech Stack is the full set of tools a data scientist uses—from collecting and storing data to analyzing and visualizing it. It includes programming languages, storage platforms, ML frameworks, and reporting tools.
2. What are the most popular programming languages in 2025 for data science?
Answer:
Python is still the top choice thanks to its simplicity and huge library support. Julia is also gaining popularity, especially for high-speed computations. R is still used but slowly fading in most mainstream projects.
3. Is R completely outdated now?
Answer:
Not entirely. R is still valuable in specific fields like bioinformatics and academia. However, many companies are moving toward Python and Julia for broader functionality and better integration with modern tools.
4. What happened to Hadoop? Why is it considered “out”?
Answer:
Hadoop was once a big deal in big data. But it’s complex, expensive to maintain, and slower compared to modern cloud solutions like Snowflake and Delta Lake, which are faster and easier to scale.
5. What are some modern alternatives to Jupyter Notebook?
Answer:
JupyterLab offers better file management, extensions, and a tabbed interface. Deepnote is also trending, especially for teams that need real-time collaboration in notebooks.
6. Which ML frameworks are dominating in 2025?
Answer:
PyTorch is now the leader for deep learning projects. It’s flexible and easier to debug. Hugging Face is the go-to for NLP and pre-trained models. Older versions of TensorFlow (like 1.x) are falling out of favor.
7. What’s new in data pipeline orchestration tools?
Answer:
Tools like Prefect and Dagster are replacing older platforms like Airflow. They’re easier to set up, handle errors better, and offer modern features suited for cloud-native data pipelines.
8. Is Tableau still a good tool for data visualization?
Answer:
Tableau is still widely used, especially in enterprise settings. But lighter, code-friendly tools like Streamlit and Observable are gaining attention because they’re faster and easier to customize for data scientists.
9. Why is Polars becoming popular alongside Pandas?
Answer:
Polars is faster and more memory-efficient, especially for large datasets. While Pandas is still the standard, Polars is great for handling big data without slowing down your system.
10. Is Excel still used in data science?
Answer:
Excel is good for small, simple tasks, but it’s too limited for serious data science projects. Tools like Pandas, Polars, and modern BI dashboards are much more powerful and scalable.