Top 10 Data Science & ML Tools for Non-Programmers

It’s not a problem, as today I will share my top 10 excellent tools by dint of which you can take full advantage of huge data sets and extracting valuable information from it.

If you’re a newbie in machine learning and data science or just a non-technical with barely any knowledge or experience in programming, this article is for you.

Have a nice reading!……………………………………………………………………………………Disclaimer: To eliminate problems of different kinds, I want to alert you to the fact this article represent just my personal opinion I want to share, and you possess every right to disagree with it.

Instruments for Stats & Machine Learning:1.

DataRobotOne of the best platform to simplify the life of a dummy in machine learning and programming.

DataRobot automatically detects the best data processing and feature set by dint of the most essential data science processes like text mining, imputation, variable type detection, scaling, and transformation.

And without any exaggeration, all these will assist you in building intelligent analytics and get fruitful results.

More specifically, the platform automatically searches for the best features, selects the most appropriate algorithms, tests the models and provides an API for deploying the model.

It takes the best algorithms from R, Python, Spark, and other sources, and uses TextMining, variable type detection, encoding, scaling, transformation, and automatic generation of features.

Demo — https://www.


com/lp/request-a-demo/Automated Machine Learning — https://www.



RapidMinerA widely known tool on the market today that puts the power of Machine Learning in the hands of business analysts without programming skills.

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

Generally speaking, the platform offers more features than any other visual solution, plus it is open and extensible to support all the needs of scientific data.

In addition, RapidMiner accelerates the creation of complete analytical workflows — from data preparation to modeling to business deployment — all in one environment which significantly increasing efficiency and reducing the time required for data projects.

For non-technical users, RM is an absolute catch.

Here is implemented the principle of visual programming which means you do not need to write code as well as you do not need to carry out complex mathematical calculations.

You just need to drop the data onto the working field, and then simply drag the operators into the GUI.

What is more, RM comprises 4 components including:RapidMiner Studio: Easy to use visual environment software for building analytics processes (data preparation, visualization, and statistical modeling).

RapidMiner Server: A performance-optimized application server where you can schedule and run your analytic processes and quickly return your results.

Also, it is comfortable for working with teamwork, project management, and model deployment.

RapidMiner Radoop: Implements big-data analytics capabilities centered around Hadoop.

RapidMiner Cloud: A cloud-based repository which allows easy sharing of information among various devices.

Although a professional license is paid, you can start your free 30-day trial right now.

There are 10,000 columns in the standard AGPL license and one logical process limit by the way.


MLBaseMLbase is an open source platform developed by AMP (Algorithms Machines People) Lab at the University of California, Berkeley that aims at addressing two critical issues in data science — reducing the difficulties of implementing and applying Machine Learning to large scale problems.

It comprises 3 components-layers including:ML Optimizer: This layer aims to automate the task of ML pipeline construction.

The optimizer solves a search problem over feature extractors and ML algorithms included in MLI and MLlib.

The ML Optimizer is currently under active development.

MLlib: It works as the core distributed ML library in Apache Spark.

Initially developed as part of MLBase project, MLlib is now supported by the Spark community.

MLI: This is ‘an experimental API for feature extraction and algorithm development that introduces high-level ML programming abstractions’ (MLbase).

This interface can be used to build distributed implementations of a wide variety of common Machine Learning algorithms with minimal complexity and highly competitive performance and scalability.


Auto-WEKAThis is a data-mining software written in Java that performs combined algorithm selection and hyper-parameter optimization over the classification and regression algorithms that are being implemented in WEKA (abbreviation for Waikato Environment for Knowledge Analysis).

It is a GUI based tool which is very good for newbies in data science since its interface is intuitive enough for you to get the job done quickly.

It provides options for data pre-processing, classification, regression, clustering, association rules, and visualization.

The best part about it is that it is open-source and the developers have provided tutorials and papers to help you get started.

When a dataset is given, this tool explores the hyperparameter settings for several algorithms and recommends the most preferred one to the user that gives good generalization performance.

It is primarily used for educational and academic purposes for now.

SoftwareGithub project pageSample DatasetsAuto-WEKA 2.

6 (July 2017) WEKA package, should be installed through the WEKA package manager.

Manual (PDF)Old, stand-alone version: Auto-WEKA 0.

5 (November 2013) [source, executables, documentation, examples] (tar.



BigMLBigML is a machine-learning platform that takes the user through a step-by-step process.

The platform provides a good GUI which takes the user through 6 steps as following:Sources: use various sources of informationDatasets: use the defined sources to create a datasetModels: make predictive modelsPredictions: generate predictions based on the modelEnsembles: create an ensemble of various modelsEvaluation: very model against validation setsSolving machine-learning problems such as classification, regression, association, clustering, and discovery is no longer an issue, thanks to the wide range of BigML algorithms.

By the way, BigML offers several packages bundled together in monthly, quarterly and yearly subscriptions.

It even offers a free package but the size of the dataset you can upload is limited to 16MB.

You can get a feel of how the interface works through a YouTube channel created by Big ML’ developers.

Official website — https://bigml.

com/Instruments for Data Visualization and Reporting:6.

TableauWhen it comes to ML & Data Science, Tableau is one of the most popular platforms.

Apart from being a total must, this is an amazing tool for non-programmers and people without coding skills.

Tableau makes data visualization and reporting tasks to be done quickly and clearly.

Plus, it does not require expensive implementation.

With Tableau, you can create graphs, charts, maps, etc.

within a short span of time.

Tableau has tools for analyzing data.

These include the most basic types of analysis:regressions,prediction of time series with regard to seasonality (using the triple exponential smoothing algorithm),clustering (by the k-means method) — this tool is quite suitable for initial data analysis.

The Tableau Product Family:Tableau Server allows to place analytics on a browser and make it accessible to any user.

The solution unfolds in minutes, is simply supported and makes the joint analytical work quick and easy.

Tableau Public allows placing interactive graphics on your sites and blogs for free without programming.

Tableau Reader is a free product that allows viewing online working documents created in Tableau Desktop.

Tableau Desktop is a personal software that allows anyone to analyze any kind of data and create brilliant reporting in minutes.

Tableau probably would warrant consideration in the list (it is an enterprise data visualization tool), but it is probably cost prohibitive to use for basic data visualization purposes.

If you have it already, it’s a powerful tool (and they offer a free trial subscription).

Official website — https://www.



DatawrapperDatawrapper is a digital tool that makes creating interactive visuals of data a breeze.

Generating any type of visualization from your data is now possible with Datawrapper.

You can either represent your data in the form of a line graph, bar graph or interactive charts.

Many news channels and organizations to represent data in an interesting manner have used Datawrapper.

Uploading data can be done with a .

csv file or by pasting from the source.

Datawrapper is also a paid app.

Users must pay a monthly 39$ or annual fee 119$ to generate embedded graphs and download images of Datawrapper charts.

Despite its limitations, Datawrapper has the most intuitive interface of any of the tools I sampled.

Official website — https://www.



VisualrAlthough this tool is not widely popular, Visualr is no worse than its mainstream vis-à-vis Tableau.

It has got some powerful features at a very competitive cost.

It’s fast, reliable and economical.

It can be used by individuals as well as organizations with big teams.

It is very simple and intuitive to use.

It is one of the best tools to use as it makes complex visualizations simple.

You can simply upload your Excel, Access, CSV or even Flat files and start visualizing.

This saves you from the hassle of converting the files into a particular format.

Another huge is design extremely attractive dashboards at the snap of fingers.

This feature would be a lifesaver when you are short on time.

What is more, conveniently connect to almost all enterprise-level databases, including Oracle, MS SQL, MySQL, Excel, Access or even flat files like CSV.

Official website — https://visualrsoftware.


htmlInstruments for Cleaning/Transforming Data:9.

PaxataPaxata automates machine learning and data in a way that helps non-technical users work efficiently with data.

It is a powerful instrument that provides visual guidance, algorithmic intelligence, and smart suggestions, uses Spark for enterprise data volumes, automatic governance, etc.

The working process is manageable here like you can use ample sources to acquire data, performs data exploration using powerful visuals, performs data cleaning using normalization of similar values using natural language processing, etc.

Paxata platform follows the following process:Add Data: use a wide range of sources to acquire dataExplore: perform data exploration using supreme visuals allowing the user to easily identify gaps in dataClean+Change: perform data cleaning using actions like imputation, normalization of similar values using NLP, detecting duplicatesShape: make pivots on data, perform grouping and aggregationShare+Govern: allows sharing and collaborating across teams with strong authentication and authorization in placeCombine: a proprietary technology designated SmartFusion enables combining data frames with 1 click as it automatically detects the best combination possible; multiple data sets can be combined into a single AnswerSetBI Tools: provides easy visualization of the final AnswerSet in commonly used BI tools; also enables easy iterations between data preprocessing and visualizationConsequently, Praxata might be a good tool to use if your work requires extensive data cleaning.

Official website — https://www.



TrifactaLast but not least in this list, Trifacta is an awesome instrument for preparation, cleaning and transforming data without hiring a data scientist.

This is a free stand-alone software that offers an intuitive GUI for performing data cleaning.

What makes Trifacta be really good is that this software takes data as input and evaluates a summary with multiple statistics by column and for each column, it recommends some transformations automatically.

The data preparation can be done by various options present in the software like discovering, structure, cleaning, enriching, etc.

It is available in three popular versions including:Trifacta Wrangler — Wrangle files up to 100MB, step-by-step new user onboarding, download results for use in analytics or data visualizationWrangler Pro — 14-day free trial of Wrangler Pro, access for up to 5 users, no limits on data & processing within the trial termTrifacta Wrangler enterprise designed to help data analysts do the work associated with data preparation without having to manually write codeThis platform aims at solving Excel’s shortcomings when it comes to dealing with huge data size.

Boasting of a user-friendly GUI, it has a number of interesting features including advanced chart building, analysis insights as well as super quick report generation.

Instead of conclusionAs you may guess, there are ample tools available today that eliminate the need to hire a data scientist or ML-specialist.

Frankly speaking, this is a great opportunity, and I don’t think these tools may be viewed as a threat to the data scientists’ job.

It means only one thing — the market for technical data scientists will keep expanding.

I hope this post will help to compare and select the best solution for your needs.

If you have any questions or suggestions, leave it in the comment section below.

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Cheers!.. More details

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