Dry goods: 18 free exploratory data analysis tools

Each of us has our own gifts, and it was only a matter of time before we discovered them and began to believe in ourselves. We all have limitations, but should we stop there? The answer is no.

When I started programming in R, I struggled. Sometimes more than one person has thought that. Because I’ve never coded in my life. My situation is like that of a man who has never learned to swim who has been forcibly kicked into the deep ocean and tried to keep himself afloat but drank a lot of salty water.

Now, when I look back, I smile. Do you know why? Because I could have chosen data analysis tools that I didn’t need to be able to program and avoided the pain.

Data mining is an indispensable part of predictive modeling. You can’t predict anything unless you know what happened in the past. The most important skill to master in data mining is curiosity, which is free but not available to everyone.

I wrote this article to help you understand the various free tools available for exploratory data analysis. There are so many free and fun tools on the market these days to help us do our jobs. These tools don’t require you to be precise and careful in writing code, they just need you to click a mouse to get the job done.

Tools/software that can be used for data analysis without programming

1 Excel / Spreadsheet

http://www.openoffice.org/download/

Whether you’re on your way into data science or are already established in the field, you know that Excel has been an integral part of the data analytics landscape (one of the most commonly used tools) for many years. Even today, a large proportion of projects requiring data analysis rely on Excel to complete them. Learning Excel has become easier thanks to the growing help from the community, tutorials, and free resources.

Excel basically supports the most common data analysis functions: overview (summarization) of data characteristics, data visualization, data transformation (removing noisy data) to create new data sets for analysis, and so on. These tools are powerful enough to allow us to reexamine data from multiple perspectives. No matter how many other data analysis tools you know, you must learn to use Excel. Although Microsoft Excel is a paid software, you can use alternatives such as Open Office, Google Docs!

2 Trifacta

https://www.trifacta.com/start-wrangling/

Trifacta’s Wrangler tool is challenging traditional approaches to data cleansing and manipulation. Because Excel has limitations on data size, this tool has no such limitations, and you can safely work with large data sets. This tool has incredible features such as chart recommendations, built-in algorithms, and analytical insights that you can use to generate reports in any given time. It’s an intelligent tool that focuses on solving business problems faster, thus making us more efficient in data-related exercises.

The availability of these open source tools makes us feel more confident and supported, and there are good people around the world working hard to make our lives better.

3 Rapid Miner

Home

This tool appeared in Gartner Magic Quadrant 2016 as a leader in advanced analytics. Yes, it’s more than just a data cleansing tool. It is specialized in building machine learning models. Yes, it contains all the ML algorithms that we use all the time. Not only GUI, it also provides support for people building models using Python&R.

It continues to fascinate people all over the world with its extraordinary ability. Most importantly, it provides analytical experience at a lightning-fast level. They have several products built for big data, visualization, model deployment on their product line, some of which (enterprise) include subscription fees. In short, we can say that it is a complete tool for any business requiring AI operations, from data loading to model deployment.

4 Rattle GUI

https://cran.r-project.org/bin/windows/base/

If you’re trying to use R and can’t get the hang of it, Rattle should be your first choice. This GUI is built in R and can be launched by typing the installation package (“rattle”) into R, then entering the library (rattle), then RTACK(). Therefore, to use Rattle, you must have the R language installed. Nor is it just a data mining tool. Rattle supports a variety of ML algorithms, such as tree algorithm, support vector machine algorithm, Booting algorithm, neural network algorithm, survival algorithm linear model algorithm, etc.

Now it is widely used. Rattle is installed 10,000 times a month, according to Mr Krahn. It provides enough options to explore, transform, and model data, but few people click on it. However, it has fewer options for statistical analysis than SPSS. However, SPSS is a paid tool.

5 Qlikview

http://global.qlik.com/us/landing/go-sm/qlikview/download-qlikview

QlikView is one of the more popular tools for the global business intelligence industry. What this tool does is take business insights and present them in a compelling way. With its more advanced visualizations, you’ll be surprised at the amount of control you get when working with your data. It has a built-in recommendation engine that can be updated from time to time with information about better visualizations.

However, this is not a statistical software. QlikView is fantastic at exploring data, trends, insights, but it doesn’t prove anything statistically. In this case, you may want to look at other software.

6 Weka

https://www.analyticsvidhya.com/learning-paths-data-science-business-analytics-business-intelligence-big-data/weka-gui-l earn-machine-learning/

One advantage of using Weka is that it is easy to learn. As a machine learning tool, the interface is intuitive enough that you can get things done quickly. It provides options for data preprocessing, classification, regression, clustering, association rules, and visualization. Most of the steps you might think of in the modeling process can be done using Weka. It’s built on Java.

It was originally designed for research purposes at the University of Wakato, but has since been accepted by more and more people around the world. However, it’s been a long time since I’ve seen a weKA community as enthusiastic as R and Python. The tutorials listed below will help you more.

7 KNIME

https://www.knime.org/knime-analytics-platform

Like RapidMiner, KNIME provides an open source platform for analyzing data that can later be deployed with other Knime-enabled products. The tool has rich features in data fusion, visualization, and advanced machine learning algorithms. Yes, you can also use this tool to build models. This tool hasn’t been talked about enough yet, but given its design technique, I think it will soon be noticed.

Plus, there are quick training sessions on their website that will get you started using the tool now.

8 Orange

http://orange.biolab.si/

As cool as it sounds, this tool is designed to produce interactive data visualization and data mining tasks. There are enough tutorials on YouTube to learn the tool. It has an extensive library of data mining tasks, including all classification, regression, clustering methods. At the same time, the multi-functional visualization developed during the data analysis enables us to understand the data more closely.

To build any model, you will need to create a flowchart. This is interesting because it will help us further understand the exact process of data mining tasks.

9 Tableau Public

https://public.tableau.com/s/

Tableau is a data visualization software. Tableau and QlikView are arguably the most powerful sharks in the sea of business intelligence. The comparison of advantages is endless. This is a visualization software that allows us to quickly explore the data, using a variety of possible charts for each observation. It is an intelligent algorithm that calculates its own data types, better methods available, and so on.

If you want to understand data in real time, Tableau can do the job. In a sense, Tableau gives us a colorful life of data, allowing us to share our work with others.

10 Data Wrapper

This is a lightning-fast visualization software. The next time someone on your team is assigned a BI job and he or she has no idea what to do, consider this software. The visualization bucket consists of line charts, bar charts, column charts, pie charts, overlay bar charts and maps. As such, it’s a basic piece of software, not comparable to giants like Tableau and QlikView. This tool is browser-enabled and does not require any software installation.

11 Data Science Studio (DSS)

http://www.dataiku.com/dss/trynow/

It is a powerful tool designed to connect technology, business and data. It can be divided into two parts: coding and non-coding. It is a complete package for any organization that aims to develop, build, deploy, and extend models on the network. DSS is also powerful enough to create smart data applications to solve real-world problems. It contains features that facilitate team integration on projects. Of all the features, the most interesting part is that you can reproduce your work in the DSS, because every operation in the system is versioned through an integrated GIT repository.

12 OpenRefine

http://openrefine.org/download.html

It started with Google’s excelsior, but it seems that Google has drastically scaled back the project for unclear reasons. However, this tool is still available under the name Open Refine. Among the many Open source tools, Open Refine specializes in messy data; Clean up, transform, and shape data for predictive modeling purposes. Interestingly, analysts spend 80% of their time cleaning up data during modeling. It’s not pleasant, but it’s true. By making improvements with Open Refine, analysts can not only save time but also use it for production work.

13 Talend

http://openrefine.org/download.html

Today, decisions are largely driven by data. Managers and professionals no longer make gut based decisions. They need a tool that can help them quickly. Talend can help them explore the data and support their decision making. Rather, it is a data collaboration tool that cleans, transforms, and visualizes data.

In addition, it provides an interesting automation feature that lets you save and redo previous tasks on new data sets. This feature is unique and not yet found in many tools. Moreover, it can automatically discover and provide users with intelligent suggestions to enhance data analysis.

14 Data Preparator

http://www.datapreparator.com/downloads.html

This tool, built on Java, helps us with data development, cleansing, and analysis. It includes various built-in packages for discretization, numbers, scaling, property selection, missing values, outliers, statistics, visualization, balancing, sampling, row selection, and several other tasks. Its GUI is intuitive and simple to understand. Once you start using this, I’m sure you won’t spend a lot of time figuring out how to use it.

A unique advantage of this tool is that the data set used for analysis is not stored in computer memory. This means you can work on large data sets without any speed or memory problems.

15 DataCracker

https://www.datacracker.com/Plans

This is a data analysis software that specializes in research data. Many companies do conduct surveys, but they struggle to statistically analyze them. Survey data are never clear. It contains a great deal of missing and inappropriate content. This tool reduces our pain and enhances our experience with messy data. The tool is designed so that it can load data from all the major Internet survey programs (surveyMonkey, Survey Gizmo, etc.). There are several interactive features that help you better understand the data.

16 Data Applied

http://www.data-applied.com/Web/TryNow/Overview.aspx

This powerful interactive tool is designed to build, share, and design data analysis reports. Creating visualizations on large data sets can sometimes be cumbersome. But this tool is powerful for visualizing large amounts of data using tree maps. Like all the other tools above, it has data transformation, statistical analysis, anomaly detection, and more. In short, it is a versatile data mining tool that automatically extracts valuable knowledge (signals) from raw data. You’ll be surprised to find that this non-programming tool is no worse at data analysis than R or Python.

17 Tanagra Project

http://eric.univ-lyon2.fr/~ricco/tanagra/en/tanagra.html

You may not like it for its old-fashioned UI, but this free data mining software is designed to build machine learning models. The Tanagra project was started as free software for academic research. As an open source project, it gives you plenty of room to design your own algorithms and contributions.

In addition to supervised learning algorithms, it also has paradigms such as clustering, factorial analysis, parametric and nonparametric statistics, association rules, feature selection and construction. Some of its limitations include lack of access to a wide range of data sources, direct access to data warehouses and databases, data cleansing, interactive utilization, and so on.

18 H2o

http://www.h2o.ai/download/h2o/choose

H2O is one of the most popular software in the analytics industry today. In just a few years, the organization has successfully spread to the analytical community around the world. This open source software brings lighting fast analysis experience, which is a further extended use of the API programming language. Not only data analysis, but you can build advanced machine learning models at any time. With strong community support, learning this tool is not a worry.

In addition to the great tools above, I found a few that I think might be of interest to you. However, these tools are not free, but you can still try them out:

• Data Kleenr http://chi2innovations.com/datakleenr/

• Data Ladder http://dataladder.com/

• Data Cleaner https://datacleaner.org/

• WinPure http://www.winpure.com/cleanmatch.html

Once you start using these tools (of your choice), you will understand that it is not a good idea to know predictive modeling programming. You can use these open source tools to accomplish the same tasks. So if you’ve been disappointed by your lack of non-coding until now, it’s time to put your passion into these tools.

The limitation I have observed with these tools (some of them) is the lack of community support. With few tools, several of them don’t have a community to turn to for help and advice. Still, it’s worth a try!

【 Pegasus Club 】

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