Microsoft Excel: Excel is one of the most widely used tools for data visualization. It allows users to easily create basic charts and plots like bar charts, pie charts, line graphs, scatter plots, histograms etc. using the built-in charting functionalities. Excel supports a variety of chart types that help identify patterns, trends and relationships during the initial exploration of data. Some key advantages of using Excel include its ease of use, compatibility with other Office tools and the ability to quickly generate preliminary visualizations for small to moderate sized datasets.
Tableau: Tableau is a powerful and popular business intelligence and data visualization tool. It allows users to connect to a variety of data sources, perform calculations, and generate highly customized and interactive visualizations. Tableau supports various chart types including bar charts, line charts, scatter plots, maps, tree maps, heat maps etc. Additional features like filters, calculated fields, pop ups, dashboards etc. help perform in-depth analysis of data. Tableau also enables easy sharing of dashboards and stories. While it has a learning curve, Tableau is extremely valuable for detailed exploratory analysis of large and complex datasets across multiple dimensions.
Power BI: Power BI is a data analytics and visualization tool from Microsoft similar to Tableau. It enables interactive reporting and dashboards along with advanced data transformations and modeling capabilities. Power BI connects to numerous data sources and helps create intuitive reports, charts, KPIs visually explore relationships in the data. Some unique features include Q&A natural language queries, AI visuals and ArcGIS Maps integration. Power BI is best suited for enterprise business intelligence use cases involving large datasets from varied sources. Its integration with Office 365 and ability to publish reports online make it a powerful tool for collaborative analysis.
Python (Matplotlib, Seaborn, Bokeh): Python has emerged as one of the most popular languages for data science and analysis tasks. Key Python libraries like Matplotlib, Seaborn and Bokeh provide functionalities to create a variety of publication-quality charts, plots and graphics. These help gain insights through visual exploration of relationships, trends and anomalies in datasets during EDA. Python libraries enable higher level of customizations compared to Excel or Tableau. They also have extensive documentation and an active developer community supporting advanced use cases. Jupyter Notebook further enhances Python’s capabilities for iterative and collaborative data analysis workflows.
R: Similar to Python, R is an extremely powerful and versatile programming language tailored for statistical computing and graphics. Base plotting functions and various contributed packages like ggplot2, lattice, shiny etc. in R enables sophisticated and publication-ready data visualization. R supports a wide range of static and interactive plots including histograms, scatter plots, box plots, density plots, maps, networks etc. It is especially useful for statistical and computational exploratory analysis involving modeling, forecasting and other predictive analytics tasks. R is a popular choice in academic research due to its statistical capabilities.
Qlik: Qlik is a business intelligence platform to explore, visualize and analyze enterprise data. Its associative data model engine allows users to intuitively interact with data using selections, filters and motions across multiple associated analyses. Qlik supports creating dashboards, apps and stories to visually represent key metrics, relationships and patterns in the data. Key features like expressions, flows and multi-dimensional analysis make Qlik extremely powerful for comprehensively exploring large datasets. Its ease of use, security and deployment models position it well for self-service analytics and governed data discovery in organizations.
So Excel, Tableau, Power BI, Python/R, and Qlik are some of the most common tools utilized by data scientists and analysts for the initial exploratory data analysis and hypothesis generation stage of a project. They enable visual data profiling through charts, graphs and dashboards to understand trends, outliers and statistical relationships present in datasets. The right choice often depends on factors like dataset size, required functionality, collaboration needs, existing tool expertise and deployment scenarios. A mix of these tools is also embraced in modern analytics workflows for seamless data exploration.