18 FREE EXPLORATORY DATA ANALYSIS TOOLS FOR PEOPLE WHO DON’T CODE SO WELL

All of us are born with special talents. It’s just a matter of time until we discover it and start believing in ourselves.

Some people struggle when they start coding in R. Sometimes a lot more can be done than one can ever think! Some people have never ever coded, not even <Hello World> in their entire life.  Below are the several non-coding tools available for data analysis.

 List of Non Programming Tools

1. Excel / Spreadsheet

If anyone is transitioning into data science or have already survived for years, they would know, excel remains an indispensable part of analytics industry. Even today, most of the problems faced in analytics projects are solved using this software. It supports all the important features like summarizing data, visualizing data, data wrangling etc. which are powerful enough to inspect data from all possible angles. No matter how many tools a person knows, excel must feature in their armory. Though, Microsoft excel is paid but they can still try various other spreadsheet tools like open office, google docs, which are certainly worth a try!

  1. Trifacta   

Trifacta’s Wrangler tool is challenging the traditional methods of data cleaning and manipulation. Since, excel possess limitations on data size, this tool has no such boundaries and everyone can securely work on big data sets. This tool has incredible features such as chart recommendations, inbuilt algorithms, analysis insights using which anyone can generate reports in no time. It’s an intelligent tool focused on solving business problems faster, thereby allowing us to be more productive at data related exercises.

  1. Rapid Miner   

This tool emerged as a leader in 2016 Gartner Magic Quadrant for Advanced Analytics. It’s more than a data cleaning tool. It extends its expertise in building machine learning models. It comprises all the ML algorithms which used frequently. Not just a GUI, it also extends support to people using Python & R for model building. In short, it’s a complete tool for any business which requires performing all tasks from data loading to model deployment.

  1. Rattle GUI   

If anyone has tried using R, but couldn’t get a knack of what’s going in, Rattle should be their first choice. This GUI is built on R and gets launched by typing install.packages(“rattle”) followed by library(rattle) then rattle() in R. Therefore, to use rattle it’s a must to install R. It’s also more than just data mining tool. Rattle supports various ML algorithms such as Tree, SVM, Boosting, Neural Net, Survival, Linear models etc.

  1. Qlikview   

Qlikview is one of the most popular tools in business intelligence industry around the world. This tool derives business insights and presents it in an awesome manner. With its art visualization capabilities, it gives tremendous amount of control while working on data. It has an inbuilt recommendation engine updated from time to time about best visualization methods while working on data sets.

  1. Weka   

An advantage of using Weka is that it is easy to learn. Being a machine learning tool, its interface is intuitive enough to get the job done quickly. It provides options for data pre-processing, classification, regression, clustering, association rules and visualization. Most of the steps while model building can be achieved using Weka. It’s built on Java.

7. KNIME

Similar to RapidMiner, KNIME offers an open source analytics platform for analyzing data, which can later be deployed, scaled using other supportive KNIME products. This tool has abundance of features on data blending, visualization and advanced machine learning algorithms. By using this tool one can build models also.

  1. Orange   

As cool as its sounds, this tool is designed to produce interactive data visualizations and data mining tasks. There are enough youtube tutorial to learn this tool. It has an extensive library of data mining tasks which includes all classification, regression, clustering methods. Along with, the versatile visualizations which get formed during data analysis allowing to understand the data more closely.

 

  1. Tableau Public 

Tableau is a data visualization software. We can say, tableau and qlikview are the most powerful sharks in business intelligence ocean. The comparison of superiority is never ending. It’s a fast visualization software which allows exploring of data, every observation using various possible charts. It’s intelligent algorithms figure out by self about the type of data, best method available etc. For understanding data in real time, tableau can get the job done. In a way, tableau imparts a colorful life to data and allows sharing work with others.

  1. Data Wrapper   

It’s a lightning fast visualization software. When someone gets assigned BI work, and the person has no clue what to do, this software is a considerable option. It’s visualization bucket comprises of line chart, bar chart, column chart, pie chart, stacked bar chart and maps. So, it’s a basic software and can’t be compared with giants like tableau and qlikview. This tools is browser enabled and doesn’t require any software installation.

  1. Data Science Studio (DSS) 

It is a powerful tool designed to connect technology, business and data. It is available in two segments: Coding & Non-Coding. It’s a complete package for any organization which aims to develop, build, deploy and scale models on network. DSS is also powerful enough to create smart data applications to solve real world problems. It comprises of features which facilitates team integration on projects. Among all features, the most interesting part is that work can be reproduced in DSS as every action in the system is versioned through an integrated GIT repository.

12. OpenRefine 

It started as Google Refine but looks like google plummeted this project due to unclear reasons. However, this tool is still available renamed as Open Refine. Among the generous list of open source tools, openrefine specializes in data cleaning, transforming and shaping it for predictive modeling purposes. As an interesting fact, during model building, 80% time of an analyst is spent in data cleaning. Not so pleasant, but it’s the fact. Using openrefine, analysts can not only save their time, but also put it to use for productive work.

  1. Talend 

Decision making these days is largely driven by data. Managers & professionals no longer make gut-based decision. They require a tool which can help them quickly. Talend can help them to explore data and support their decision making. Precisely, it’s a data collaboration tool capable of clean, transform and visualize data. Moreover, it also offers an interesting automation feature where a person can save and redo their previous task on a new data set. This feature is unique and hasn’t been found in many tools. Also, it makes auto discovery, provides smart suggestion to the user for enhanced data analysis.

  1. Data Preparator   

This tool is built on Java to assist in data exploration, cleaning and analysis. It includes various inbuilt packages for discretization, numeration, scaling, attribute selection, missing values, outliers, statistics, visualization, balancing, sampling, row selection, and several other tasks. It’s GUI is intuitive and simple to understand. Once someone starts working on it, it wouldn’t take lot of time to figure out how to work. A unique advantage of this tool is, the data set used for analysis doesn’t get stored in computer memory. This means it’s possible to work on large data sets without having any speed or memory troubles.

  1. DataCracker   

It’s a data analysis software which specializes on survey data. Many companies do surveys but they struggle to analyze it statistically. Survey data’s are never clean. It comprises of lot of missing & inappropriate value. This tool reduces agony and enhances experience of working on messy data. This tool is designed such that it can load data from all major internet survey programs like survey monkey, survey gizmo etc.

16. Data Applied

This powerful interactive tool is designed to build, share, design data analysis reports. Creating visualization on large data sets can sometimes be troublesome. But this tool is robust in visualizing large amounts of data using tree maps. Like all other tools above, it has feature for data transformation, statistical analysis, detecting anomalies etc.

17.  Tanagra Project

This tool is old fashioned UI, but this free data mining software is designed to build machine learning models. Tanagra project started as free software for academic and research purposes. Being an open source project, it provides enough space to devise its own algorithm and contribute.

  1. H2o 

H2o is one of the most popular software in analytics industry today. In few years, this organization has succeeded in evangelizing the analytics community around the world. With this open source software, they bring lighting fast analytics experience, which is further extended using API for programming languages. Not just data analysis, but allows for building advanced machine learning models in no time.

Bonus Additions:

In addition to the awesome tools above, below are some more tools which might be interesting to look at. However, these tools aren’t free but available for trial:

  1. Data Kleenr
  2. Data Ladder
  3. Data Cleaner
  4. WinPure

 End Notes

Once a person starts working on these tools they would understand that knowing programming for predictive modeling isn’t much advantageous. They can accomplish the same thing with these open source tools. Therefore, until now, if anyone was disappointed at their lack of non-coding, now is the time you channelize their enthusiasm on these tools.

The only limitation with these tools (some of them) is, lack of community support. Except few tools, several of them don’t have a community to seek help and suggestions. Still, it’s worth a try!

PS: All the above are personal perspective on the basis of exposure to information provided by Analytics Vidhya

Source : https://www.analyticsvidhya.com/blog/2016/09/18-free-exploratory-data-analysis-tools-for-people-who-dont-code-so-well/