Comparative Analysis of Data Analysis/Visualization Tools

Comparative Analysis of Data Analysis/Visualization Tools

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.

Python is an open source programming language, which can support different styles of programming. Being fast, powerful and portable, it can be used by developers for scripting a website or other applications. Python includes libraries that enhance its functionality (like NumPy, Pandas, SciPy). Python is known to be an efficient tool for machine learning (using scikit-learn).
SPSS is a versatile package that allows different types of data analysis and data transformations on all sorts of data. It is user friendly, and offers various statistical tests, regression frameworks and correlations. Additionally, the data and output can be saved in a wide variety of file formats.
SAS has strong handling capabilities and is well tested. It contains an inbuilt library with all the necessary packages required for analysis and reporting of data. It supports different data formats and has the ability to read data for any type of file and format. As it is a commercial software, it is expensive and is not always enriched with latest statistical functions.

Comparison Table of tools used in data analysis

FeatureRPythonSPSSSAS
Ease of learningGetting familiar with the syntax can take timeReadable and clean syntaxEasy to learn and useDifferent, but easy to learn
CostFree of costFree of costFairly expensive to use commerciallyExpensive
Data HandlingCan handle big data, but powerful hardware is required when working with large datasets.Good data handling capabilityNot advisable for big dataGood data handling capability
VisualizationAdvanced graphical capability, with different packages for visualization.Advanced graphical capability, with different packages for visualization.Comparatively low graphical capabilityGood functional graphical capability. Customization of plots are complex.
Customer SupportNo customer service team, but massive community is available for helpHas a large community for customer help (comparatively smaller than that of R)Fairly good customer supportHas a dedicated customer support team
Data Visualization translates information to visual context, thereby making data less complex to understand. Visualizations help identify patterns, outliers or trends in data. It is usually used during the end process of data analytics, to better explain and represent insights obtained.Some data visualization tools that can be used for data analysis are mentioned below in detail.

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

powervis

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

FeatureTableauPower BIQlikView
Ease of learningEasy 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
CostExpensiveLess expensive as compared to TableauFairly expensive
Data InterpretationExtremely convenient for Data interpretation and analyticsFairly good for Data interpretation and analyticsCan improve
Customer SupportFairly large customer communityLarge Customer communitySmaller customer community

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