Shulou(Shulou.com)06/01 Report--

In this issue, the editor will bring you about what are the free exploratory data analysis tools in big data. The article is rich in content and analyzes and narrates it from a professional point of view. I hope you can get something after reading this article.

Here are a variety of free tools to help you learn about exploratory data analysis. Nowadays, many free and interesting tools can be found in the market to help us with our work. These tools don't require you to write code more accurately and carefully, just a click of a mouse to get the job done.

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

1 Excel / Spreadsheet

Whether you are about to enter the field of data science or have made some achievements in this field, you will know that over the years, excel has been an indispensable part of the field of data analysis (one of the most commonly used tools). Even today, a large number of projects that require data analysis rely on excel to complete. With more and more help from the community, tutorials, and free resources, learning excel has become easier and easier.

Excel basically supports the most commonly used data analysis functions: summarizing (summarizing) data characteristics, data visualization, transforming data (removing noise data) to get new data sets for analysis, etc. These tools are powerful enough to allow us to re-examine the data in many ways. 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 its alternatives, such as open office, google docs!

2 Trifacta

Trifacta's Wrangler tools are challenging traditional data cleaning and manipulation methods. Because excel has limitations on data size, but this tool does not have such limitations, you can safely use it to work with big data sets. This tool has incredible features such as chart recommendations, built-in algorithms, and analytical insight that you can use to generate reports at any time. This is an intelligent tool that focuses on solving business problems faster, thus making us more efficient in data-related exercises.

The provision of these open source tools makes us feel more confident and supportive, and there are excellent people around the world who are working hard to make our lives better.

3 Rapid Miner

This tool appears on 2016Gartner Magic Quadrant as a leader in advanced analytics. Yes, it's more than just a data cleaning tool. It is professional in building machine learning models. Yes, it contains all the ML algorithms that we often use. Not only GUI, it also provides support for people who build models using Python&R.

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

4 Rattle GUI

If you try to use R but can't get the hang of it, Rattle should be your first choice. This GUI is built on the R language and can be started by typing the installation package ("rattle") into the R language, then typing the library (rattle), and then typing RTACK (). Therefore, to use Rattle, you must install the R language. It is not 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 and so on.

Now it has been widely used. According to Krahn, rattle is installed 10000 times a month. It provides enough options to explore, transform, and model data, but few people click on it. However, it has fewer choices in statistical analysis than SPSS. However, SPSS is a paid tool.

5 Qlikview

QlikView is one of the more popular tools in the global business intelligence industry. What this tool does is gain business insight and present it in a very attractive way. With its more advanced visualization features, you will be surprised at the amount of control you get when processing data. It has a built-in recommendation engine that updates information about better visualization from time to time.

However, this is not a statistical software. QlikView is incredible in exploring data, trends, and insights, but it doesn't prove anything statistically. In this case, you may need to look at other software.

6 Weka

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

It was originally designed for research purposes at Wakato University, but has since been accepted by more and more people around the world. However, I haven't seen a weka communication community as enthusiastic as R and Python in such a long time. The tutorials listed below will help you more.

7 KNIME

Like RapidMiner, KNIME provides an open source platform for analyzing data that can later be deployed using other products that support KNIME. The tool has rich features in data fusion, visualization and advanced machine learning algorithms. Yes, you can also use this tool to build models. Although there is not enough discussion about this tool, given its design technology, I think it will soon attract people's attention.

In addition, there are quick training courses on their website that allow you to start using the tool now.

8 Orange

As cool as it sounds, this tool is designed to generate interactive data visualization and data mining tasks. There are enough tutorials on YouTube to learn about this tool. It has an extensive database of data mining tasks, including all classification, regression and clustering methods. At the same time, the multi-functional visualization formed in the process of 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 learn more about the exact process of the data mining task.

9 Tableau Public

Tableau is a data visualization software. We can say that Tableau and QlikView are the most powerful sharks in the ocean 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 the data types, the better methods available, and so on.

If you want to understand the data in real time, tableau can do the job. In a sense, tableau gives us a rich and colorful data life that allows 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 to BI and he or she has no idea what to do, consider choosing this software. Visual bucket consists of line chart, bar chart, column chart, pie chart, superimposed bar chart and map. So it's a basic piece of software that can't be compared with giants like Tableau and QlikView. This tool enables the browser and does not require any software installation.

11 Data Science Studio (DSS)

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 software package for any organization that aims to develop, build, deploy and expand 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 the project. The most interesting part of all the features is that you can reproduce your work in DSS because every operation in the system is versioned through an integrated GIT repository.

12 OpenRefine

It started with Google's Excelsior, but Google seems to have scaled back the project for unknown reasons. However, the tool is still available and is renamed Open Refine. Among the many open source tools, Open Refine specializes in messy data; it cleans up, transforms, and shapes data for predictive modeling purposes. Interestingly, during the modeling process, analysts spend 80% of their time on data cleaning. It's not that pleasant, but it's true. Using Open Refine for improvement, analysts can not only save time, but also apply it to production work.

13 Talend

Today, decisions are mainly data-driven. Managers and professionals no longer make intuitive decisions. They need a tool that can help them quickly. Talend can help them explore data and support them in making decisions. Specifically, it is a data collaboration tool that can clean, transform, and visualize data.

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

14 Data Preparator

This tool is built on Java and can help us develop, clean up, and analyze data. It includes various built-in packages for discretization, numbers, scaling, attribute selection, missing values, outliers, statistics, visualization, balance, sampling, row selection, and several other tasks. Its GUI can be understood intuitively and simply. Once you start using this, I'm sure you won't spend much time figuring out how to use it.

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

15 DataCracker

This is a data analysis software specializing in the study of survey data. Many companies do conduct surveys, but it is difficult for them to conduct statistical analysis. The survey data are never clear. It contains a lot of missing and inappropriate content. This tool reduces our pain and enhances our experience of dealing with messy data. The tool is designed to load data from all major Internet survey programs such as surveymonkey, survey gizmo, etc. There are several interactive features that contribute to a better understanding of data.

16 Data Applied

This powerful interactive tool is designed to build, share, and design data analysis reports. Creating visualization on large datasets can be cumbersome. But this tool is powerful in using tree maps to visualize large amounts of data. Like all other tools above, it has the functions of data conversion, statistical analysis, anomaly detection and so on. In short, it is a multi-purpose data mining tool that can automatically extract valuable knowledge (signals) from the original data. You will be surprised to find that this non-programming tool is not inferior to R or Python in data analysis.

17 Tanagra Project

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

In addition to supervised learning algorithms, it also has examples of 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 cleaning, interactive utilization and so on.

18 H2o

H2O is one of the most popular software in the analysis industry. In just a few years, the organization has successfully spread among analysts around the world. This open source software brings the experience of rapid analysis of lighting, which is a further extension of the API programming language. Not only is it data analysis, but you can build advanced machine learning models at any time. Based on strong community support, learning this tool is not a concern.

These are the free exploratory data analysis tools shared by big data. If you happen to have similar doubts, you might as well refer to the above analysis to understand. If you want to know more about it, you are welcome to follow the industry information channel.

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