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19 Data Science Tools for people who aren’t so good at Programming

This list of Data Science tools for people who aren’t so good at Programming was compiled by Aarshay Jain, from Analytics Vidhya.

Programming is an integral part of data science. Among other things, it is considered that a mind which understands programming logic, loops, and functions has higher chances of becoming a successful data scientist. So, what about people who never studied programming subject in their school or college ?


The list of tools:

1. RapidMiner (RM)

RM covers the entire life-cycle of prediction modeling, starting from data preparation to model building and finally validation and deployment.

2. DataRobot (DR)

DataRobot (DR) is a highly automated machine-learning platform built by all time best Kagglers including Jeremy Achin, Thoman DeGodoy and Owen Zhang.

3. BigML

BigML is another platform with ~Mn USD in funding. It provides a good GUI which takes the user through 6 steps.

4. Google Cloud Prediction API

The Google Cloud Prediction API offers RESTful APIs for building machine learning models for android applications.

5. Paxata

Paxata is one of the few organizations which focus on data cleaning and preparation, NOT the machine learning or statistical modeling part.

6. Trifacta

Trifacta is another startup focused on data preparation.

It has 2 products offering:

  • Wrangler – a free stand-alone software
  • Wrangler Enterprise – licensed professional version

7. Narrative Science

Narrative Science is based on a unique idea in the sense that it generates automated reports using data.

8. MLBase

MLBase is an open-source project developed by AMP (Algorithms Machines People) Lab at University of California, Berkeley.


Weka is a data mining software written in Java, developed at the Machine Learning Group at University of Waikato, New Zealand.

10. Automatic Statistician

Automatic Statistician  is not a product per se but a research organization which is creating a data exploration and analysis tool.

Find more tools in the full article: If you want to learn more about the 19 tools quoted above click here. For other articles about Data Science tools click here.

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