Data analysis is crucial to deep dive into problems, and to explore data in meaningful ways. It organizes, interprets, structures and presents data into useful information that provides business context, which further enhances productivity in a business.
In data analysis, evaluation of data can be done using analytical and statistical tools to discover useful information. The results obtained from these tools are then interpreted to make decisions.
Some analytical tools that can be used for data analysis are mentioned below in detail.
R is an open source software programming language for statistical computing, reporting, data analysis and visualization. It consists of freely available packages (like dplyr, stringr), which can be used for data mining, machine learning and other statistical techniques. It has an effective data handling facility, and offers a variety of operators for calculations on arrays, lists, vectors and matrices.
Comparison Table of tools used in data analysis
|Ease of learning||Getting familiar with the syntax can take time||Readable and clean syntax||Easy to learn and use||Different, but easy to learn|
|Cost||Free of cost||Free of cost||Fairly expensive to use commercially||Expensive|
|Data Handling||Can handle big data, but powerful hardware is required when working with large datasets.||Good data handling capability||Not advisable for big data||Good data handling capability|
|Visualization||Advanced graphical capability, with different packages for visualization.||Advanced graphical capability, with different packages for visualization.||Comparatively low graphical capability||Good functional graphical capability. Customization of plots are complex.|
|Customer Support||No customer service team, but massive community is available for help||Has a large community for customer help (comparatively smaller than that of R)||Fairly good customer support||Has a dedicated customer support team|
Tableau is a visualization software used across industries, with a highly interactive interface. It has a simple drag-and-drop functionality that results in an efficient visualization process. It can easily handle large and fast changing datasets, and easily integrates with Hadoop, AWS, MySQL, etc. Tableau can be used with C, C++, Java and Python
Microsoft Power BI contains a collection of business intelligence tools. It contains dashboard reports and data models (available in Content Packs). Power Bi includes a huge arrangement of on-premise and cloud data sources. Using Power BI, one can get informative reports that show complete presentation of data, thereby revealing important insights.
QlikView has a highly customizable interface, with a variety of features for business intelligence and reporting. It can be used with C++, SQL Server, VBscripting, etc. QlikView ensures easy implementation, flexibility and high scalability. It provides a unique cloud-based data service (Qlik Data Market), that enables the user to fetch datasets from different public and syndicated data sources. QlikView offers sharing and collaboration, data security provisions, guided analytics and integrated framework for the efficient working of an enterprise.
Comparison Table of tools used in data visualization
|Ease of learning||Easy drag and drop functionality. Includes some advanced features.||Easy to learn as it is similar to Microsoft Excel and Microsoft Access.||Easy to learn and fast to deploy|
|Cost||Expensive||Less expensive as compared to Tableau||Fairly expensive|
|Data Interpretation||Extremely convenient for Data interpretation and analytics||Fairly good for Data interpretation and analytics||Can improve|
|Customer Support||Fairly large customer community||Large Customer community||Smaller customer community|