On a certain occasion, if you got stuck at one of the most crucial steps of a research-information analysis, then your proper knowledge about such steps and some specific tools can pave your research path.
What is Data Analysis?
Data analysis is a critical step in research that includes data collection and analysis to draw a useful conclusion out of it. Hence, to deal with a large variety of data, a researcher must go through different methods and techniques to perform the analysis as per the specific requirements. Thus, the specific requirements depend on the industry, discipline, and objectives of a given research. For a better understanding of the analysis process, it is important to highlight that all methods of information analysis are based on two core areas: qualitative or quantitative analysis. Hence, this article will discuss a variety of different analysis methods and some open-source data analysis tools. It will also suggest which tool is the most effective for the data analysis as a whole.
What are the methods of data analysis?
The quality of research is based on techniques and methods for the collection or analysis of quantitative or qualitative data. For the collection of both types of data, there are three most common techniques: primary data collection, secondary data collection and mixed data collection methods. Once you collect the data for analysis, the next step is to sort it for creating a meaningful story.
The number of data analysis methods is even more than the number of data collection methods in research. You can get dissertation help service in case of any issue. However, for the researcher’s convenience, we broadly divide the analysis process into two types: quantitative analysis and qualitative analysis. Let’s discuss them one by one.
Qualitative data analysis methods
A qualitative data analysis method analyses facts present in the form of words, text, observations, symbols, and images. There are many analysis methods that can sort the textual data:
- Content Analysis: A method that deals with behavioural or verbal data.
- Grounded Theory: A method that uses casual explanation from a past event.
- Narrative Analysis: A method effective to analyse the statements or claims.
Quantitative data analysis methods
The other name of the quantitative analysis method is the statistical method of analysis. The reason is that it deals with the data in the form of figures, digits or numbers. Such statistical tools and techniques are necessary to analyse this type of data. There are many methods in data analysis that help researchers draw a meaningful story from the statistical data as well. A few of them are:
- Descriptive statistical analysis
- Dispersion statistical analysis
- Discriminant statistical analysis
- Factor statistical analysis
- Regression statistical analysis
- Time series statistical analysis
What are open-source data analysis tools
If you want help in the data analysis process, there are many open-source data analysis tools that can make it easier. A common misconception about open-source tools is that they are free to use. Actually, most ofthe open-source tools are free to use, but this is not true for all. By definition, the open-source tools are those whose source code is available and editable for the users. Hence, for analysis, there is a number of open-source software available. The analyst just needs to wisely select the most appropriate one. Let’s discuss the features of the three most popular data analysis tools:
It is an open-source data analysis platform that allows you to track and monitor the metrics of various databases. You will receive alerts informing you about certain events, along with real-time information about external systems. With Grafana, you can view your data with geomaps, histograms, heatmaps, and graphs to make your data easier to understand. Further, you can collect your data to improve the context and set up seamless alerts when it makes sense. In short, Grafana is an effective open-source analysis tool even for commercial level research.
The KNIME Analytics platform was first launched in 2006 and has been rapidly accepted by the open-source community since then. A number of companies and software vendors use it to create information sciences. The open and advanced software of KNIME makes analysis easy to understand for all. You can create a visual workflow by dragging the graphical user interface, planning analytical steps while managing your data flow and making sure your work is up-to-date. Also, you can combine tools into a single workflow with KNIME native nodes across different domains. You can use and download AWS S3, Salesforce, Azure, and more for better dataanalysis.
RapidMiner brings AI (artificial intelligence) to the enterprise through an open-scale data science platform. Designed for the analytics team, RapidMiner integrates the entire data science cycle from data preparation to machine learning. Further, if you want to get deeper information, then RapidMiner’s interactive visualisations, for example, graphs and charts, can make it easier. Other exciting features of RapidMiner include panning, zoomingand other drill-down capabilities. Here, you can analyse more than 40-types of data including videos, text, audio, images.and NoSQL.
The lists of the open-source data analysis tools never end on above three toolss; there are a number of others as well—for example, Apache Spark, Redash, RStudio, BIRT, and Pentaho
Which tool is best for data analysis?
In light of approximately dozens of open-source data analysis tools, the most time-consuming part is to decide which one is the best for your analysis part. In general, there is no hard and fast rule for information analysis tools’ selection. Even you can randomly select any of your choices. But user reviews in particular can make the decision easier. Reading reviews explain that out of 5 stars, KNIME and RapidMiner get 4.6, but Grafana gets 4.5 stars.
So, RapidMiner and KNIME are the two best open sources analysis tools. But if you have to select anyone between RapidMiner and KNIME, certain features can help in the assessment. Like, KNIME has more than 1,000 standard operating options, and RapidMiner has more than 1,500 options. But KNIME gives a slight advantage as it allows for more flexibility. Furthermore, due to its integration with other software, KNIME offers more advanced features for analysis to its users. As a big picture, you can use KNIME for better analysis options.
In short, data analysis is a systematic organisation of both textual and statistical data for creating a meaningful scientific story. But to deal with different types of data, the analysis methods vary from conceptual analysis to statistical analysis. Also, to get an advantage from the technology, the open-source information analysis tools are effective to perform data analysis for you. All you need to do is just select a perfect one according to your aims.