Introduction to Free Data Analysis Tools
Free data analysis tools are used to analyze data and create meaningful insights out of the data set. These are a set of tools that helps businesses to create a data-driven decision-making process. Some of the industry-known tools that are very popular tools such as, Microsoft excel, tableau public, KNIME, Rattle GUI for R, Talend, H2O, Trifacta, Orange, RapidMiner, Qlikview. These tools are supported with several out-of-the-box features that help in the data analysis process. These data analysis tools are easy to learn and develop the analysis solution very quickly compared to standard programming for data analysis.
Data Analysis Tools
Below are the different tools of Data Analysis.
Excel still attracts people to do data analysis and yes it is indispensable still as an analytics tool. There are many free online tutorials available that teach about Excel and VBA through which you can master excel. All the features such as exploring data, summarizing data, and visualizing data through various graphical tools are done in excel.
It is very easy to learn and master excel. Excel is still a basic tool in data science and analytics. Knowledge of excel will help you in your data science career. Though Microsoft Excel is not free, there are similar tools like spreadsheets, open offices and may others in the market which provides the same features as excel. One small drawback of excel is that it can’t be used for very large datasets.
- Tableau is a free tool for data visualization from simple data to complex data. It is kind of interactive and we can suggest labels, tools, size of the column, and almost anything we can customize. The drag and drop interface is really helpful in this software and calculations can also be done in Tableau. Anyone who doesn’t have any idea of analytics can see and understand data from the Tableau platform.
- Dashboards and worksheets are created in Tableau for data analysis and visualization. Tableau helps see data from a different perspective through its dashboards. One can easily enter into the world of data science through Tableau. Also, Tableau integrates with Python and R programming language.
Trifacta is an open-source tool for data wrangling which makes data preparation easy for data analysis. Trifacta helps to transform, explore and analyze data from raw data format to clean, arranged format. It uses machine learning techniques to help users in data analysis and exploration. The other name of Trifacta is Data Wrangler which makes it clear that it is most useful in data cleaning.
It was developed in 2012 by Joe Hellerstein, Jeffrey Heer, and Sean Kandel. Trifacta works with the cloud and is collaborated with AWS. It has bagged an award for machine learning deployment from AWS. Trifacta helps you to work with large datasets, unlike Excel. Also, text editing suggestions are incredible in Trifacta.
RapidMiner is an integration tool for data preparation, machine learning, deep learning, and other data analysis techniques. The workflow is called process and the output of one process becomes the input of others. This can be extended via either programming languages or their own plugins. Some versions of RapidMiner are free.
The products of RapidMiner include RapidMiner Studio, RapidMiner Auto Model, RapidMiner Turbo Prep, RapidMiner Server, and RapidMiner Radoop. We can inspect data by loading data into RapidMiner and do calculations or sort the data inside the tool. RapidMiner is mainly designed for non-programmers. RapidMiner also helps in data cleaning and preparing charts.
Talend is an open-source tool for data integration with the help of the cloud. Talend helps to import data and move it to the data warehouse as quickly as possible. Talend has a unified platform. Also, the community of Talend is powerful that you will never know that the person on the other side comes from which background.
Talend Platforms, Talend enterprise, and Talend Open Studio helps in almost everything related to data that you may not look for another tool once you start working with Talend. Among the three, the most used is Talend Open Studio. The collaboration and management of Talend are commendable as with their data integration.
Qlikview is recommended as the best tool for data visualization. It is faster, easy, and unique in nature. There is a community in QlikView which has discussion forums, blogs, and a library. Community helps to solve most of your queries. It shows the relationship between data using different colors. Qlikview helps users to make the right decisions from their different approaches to data visualization.
If you are interested in layout designing, Qlikview is your way to go. It is good to have knowledge of data modeling and SQL basics to be proficient in Qlikview.
The orange toolkit can be used as simple data visualization to complicated machine learning algorithms provided it is open source. It can also be used with the Python library. It is like a canvas where the user places the widgets and workflow is created. All the data functionalities are done in widgets canvas. Users can explore various visualization techniques available in the tool.
There are many add-ons for the Orange tool as it is used in the machine learning algorithm as well. Data mining can also be done in this tool.
H2O helps in finding patterns of data. Its applications are mostly in machine learning and artificial intelligence but it provides really good insights about data. H2O has a built-in function to guess the structure of the incoming data set.
There are also other tools like OpenRefine for sorting and filtering data, Fusion Tables for charts and visualization, Microsoft power BI for data visualization and data wrangling, Google Dashboards to create reports, Plotly for statistical analysis, Gephi for statistical visualization and the tools are many.
Data analysis can be done easily with a bit of practice. All the tools will not help equally. It is good to select one tool and become a master in that tool. Understanding data is essential to know where we really are in terms of data analysis. Programming is not really important in visualizing and analyzing data. But some tools make you closer to programming.
This is a guide to Free Data Analysis Tools. Here we discuss the basic meaning and different data analysis tools in detail. You can also go through our other suggested articles to learn more –