You're a data scientist who needs to visualize complex data. What's the best tool for the job?

My personal favorite is Tableau: Tableau is a widely used data visualization tool that offers a user-friendly interface for creating interactive dashboards and visualizations. It supports a variety of data sources and allows users to easily explore and analyze data. Ultimately, the best tool for data visualization will depend on factors such as the complexity of the data, the desired level of interactivity, the programming languages or software already being used, and the specific requirements of the project or analysis. Many data scientists use a combination of different tools to effectively visualize information and communicate insights from their data.

Tratando-se de dados complexos: Em geral eu recomendaria o plotly como a biblioteca adequada para fazer essas análises, visto que as fun??es de hover, zoom e crop s?o muito úteis durante análises, especialmente tratando-se de séries temporais. Porém, como o trabalho de ciência de dados muitas vezes exige amostragem e testes rápidos de hipóteses, pode ser mais rápido visualizar os dados em matplotlib e seaborn, visto que basta alterar as células do notebook para torná-las dinamicas e construir fun??es de plotagem que podem ser úteis nas tomadas de decis?o. Em resumo, quer ver panorama geral? Plotly. Precisa validar alguma premissa ou hipótese, monte um bom teste em pandas e exiba o resultado rapidamente em matplotlib ou seaborn.

To data visualization we can use matplotlib, seaborn and plotly, the most popular libraries to graph on python. These libraries support all kind of graphs since a simple scatter plot to maps. There is a library with different types os graphs named D3.js also supported on python.

As a Business Intelligence expert who has utilized both Power BI and Tableau, I find Power BI to be the preferred tool for visualizing complex data. Power BI's seamless integration with Microsoft products proves invaluable. This facilitates quick adoption and efficient data consolidation, particularly beneficial in diverse environments like healthcare and banking. Power BI's user-friendly interface and robust DAX capabilities enable comprehensive insights into market development, patient care, and banking analytics. While Tableau offers advanced visualization features, Power BI's versatility and familiarity within Microsoft ecosystems make it my top choice for effective data visualization across different sectors

For visualizing complex data, Tableau is an excellent choice. Its intuitive interface and powerful features allow data scientists to create interactive and insightful visualizations. With diverse chart options, real-time updates, and seamless integration with various data sources, Tableau enables effective communication of complex findings to both technical and non-technical stakeholders.

When it comes to working with R, ggplot2 and its extension packages are my preferred tools for data visualization, particularly for genomic data. However, my approach always begins with pencil and paper to sketch out the plot structure, identify key aesthetics, and ensure that the final visualization effectively communicates the story I want to tell. This pre-planning step helps me to work more efficiently and produce visualizations that are both aesthetically pleasing and informative.

Seguiria a mesma lógica das bibliotecas python. Shiny seria bom para panoramas gerais ou para compartilhar visualiza??es com stakeholders. ggplot2 é maravilhoso para visualiza??es feitas para testar premissas e hipóteses. Recomendo estudar um pouco sobre sintaxe de gráficos, já que é o modo de constru??o de visualiza??es adotado pelo ggplot2. Parece complicado no início, mas simplifica ao aprender e é muito útil no caminho analítico.

Aqui, acredito que a melhor ferramenta seja a adotada pela organiza??o/institui??o/setor dos stakeholders. A melhor ferramenta é aquela que o visualizador já está acostumado a usar nesses casos.

O melhor conselho que eu posso dar acredito que seja pensar bem na história que será contada, para isso, foque no público-alvo. Entenda as competências de dados desse público, que tipo de informa??o eles est?o acostumados a consumir e visualizar. Monte sua história e produza as dataviz que contem esse roteiro. Ferramenta é isso: ferramenta.

Hi Selection Of tool depend on volume of data as well as complexity of data. During selection we should consider Capability for logic calculation.

Choosing the perfect tool pass through a good storytelling, which story you want to tell with this graph? Stay in mind that you need to show as clear as possible the main message with this data. There is a good book that helps you to build a pragmatism when building graphs named " Storytelling with data" from Cole Nussbaumer Knaflic.

Keep in mind: "My data visualization needs to be a brainless activity to my final user", which means less is more. Focus in the main message of your visualization and discard all extra stuff.

N?o exiba informa??o desnecessária. Dê todos os insumos para interpreta??o (título, nomes e unidades dos eixos e legenda para categóricas). Menos é mais aqui. Foque na história a ser contada e remova qualquer atrito que possa ficar no caminho. O olho humano (ocidental) costuma ler informa??es da esquerda para direita, de cima para baixo. Tenha isso em mente e aproveite esses locais de alto destaque para informa??es relevantes ou contextuais.

We need to identify requirements of end user that means the decision maker who will get the best insights from the data that would be added value for the company or firm. Then clean data and prepare data for visualization with any tools but mostly I preferred Power BI then Excel.

For complex data you can use a reduction of dimensionality techincs such t-sne or umap, can be useful to extract some insights from your data.