The No-code Data Science Tool Stack

I am a big fan of no-code tools. They are extremely useful in eliminating repetitive code across projects—thereby boosting productivity.

The below visual depicts 8 powerful (and my favorite) no-code tools for data science tasks:

They automate many redundant steps in data science projects and help you perform data science tasks without any code.

Let’s discuss them one by one.

• Gigasheet:

  • Browser-based no-code tool to analyze data at scale.

  • Use AI to conduct data analysis

  • It’s like a combination of Excel + Pandas with no scale limitations.

  • You can analyze datasets as large as 1B rows.

  • Get started: Gigasheet.

• Mito:

  • Create a spreadsheet interface in Jupyter Notebook.

  • Use Mito AI to conduct data analysis.

  • Automatically generates Python code for each analysis

  • Get started: Mitosheet.

• PivotTableJS:

  • Create Pivot tables, aggregations, and charts using drag-and-drop.

  • Add heatmaps to tables.

  • Works within Jupyter Notebook.

  • Get started: PivotTableJS.

• Drawdata:

  • Draw any 2D scatter dataset by dragging the mouse.

  • Export the data as DataFrame, CSV, or JSON.

  • Create a histogram and line plot by dragging the mouse.

  • Get started: Drawdata.

• PyGWalker:

  • Open a tableau-style interface in Jupyter notebook

  • Analyze a DataFrame as you would in Tableau.

  • Get started: PyGWalker.

• Visual Python:

  • A GUI-based Python code generator.

  • Import libraries, perform data I/O, create plots, write code for ML models, etc. by clicking buttons.

  • Get started: Visual Python.

• Tensorflow Playground:

  • Provides an elegant UI to build, train, and visualize neural networks.

  • Browser-based tool.

  • Change data, model architecture, hyperparameters, etc., by clicking buttons.

  • Get started: Tensorflow Playground.

• ydata-profiling:

  • Generate a standardized EDA report for your dataset.

  • Works in a Jupyter notebook

  • Covers info about missing values, data statistics, correlation, data interactions, etc.

  • Get started: ydata-profiling.

👉 Over to you: Have I missed any other cool data science no-code tools?

Whenever you are ready, here’s one more way I can help you:

Every week, I publish 1-2 in-depth deep dives (typically 20+ mins long). Here are some of the latest ones that you will surely like:

• [FREE] A Beginner-friendly and Comprehensive Deep Dive on Vector Databases.

• Augmenting LLMs: Fine-Tuning or RAG?

• Implementing LoRA From Scratch for Fine-tuning LLMs.

• You Are Probably Building Inconsistent Classification Models Without Even Realizing

• Why Sklearn’s Logistic Regression Has no Learning Rate Hyperparameter?

• PyTorch Models Are Not Deployment-Friendly! Supercharge Them With TorchScript.

• DBSCAN++: The Faster and Scalable Alternative to DBSCAN Clustering.

• Federated Learning: A Critical Step Towards Privacy-Preserving Machine Learning.

• You Cannot Build Large Data Projects Until You Learn Data Version Control!

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