Top 5 Reasons Why You Should Use No-Code Tools To Get Started With Data Science

Take it easy.

1. It’s easier to get started.

Data science does not have to mean months or years of work to get started. With a no-code platform you can go from literally zero coding or data science experience to running your first algorithm in minutes — literally!

2. It’s easier to visualize your results.

It’s hard to find the meaning in arrays of numbers, and visualizing your data is its own coding hurdle. No-code tools automatically visualize your results so that you can see the results of your work.

3. It’s easier to experiment with different methods.

Regressions, Neural Networks, Kalman Filters. If you’re learning to code with, for example, Python, you’re going to deal with an entirely new learning curve for each algorithm you try. This will make it hard to compare models and learn by these comparisons. No-code tools let you try everything instantly and focus on learning the science behind each method.

4. You get feedback loops between experimentation and real-world results.

The ultimate goal of 99% of data science projects is improving decision-making in some real-world setting, whether it’s choosing when to buy a particular stock or tagging which transactions are likely fraudulent. No-code tools let you quickly go from model to testing in a live application back to improving your model much more quickly. These feedback loops dramatically speed up your learning.

5. If the hard part of your job is typing, your job will eventually be automated.

Data scientists are hired for knowing when it’s appropriate to use a Neural Network over a Linear Regression — not how to manage a Pandas array from memory. The hours you spend debugging Python code are wasted. No-code tools handle the data science “plumbing” for you, so you can focus on learning what actually matters.

Monument’s no-code AI prepares you for the future.

Monument ( is a 100% no-code Artificial Intelligence and Machine Learning platform built to automate the tedious parts of data science. The platform features a wide array of algorithms for time-series prediction (stock price prediction, weather forecasting) and classification (fraud detection, predictive maintenance).

Predictions to keep you two steps ahead.

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