You’re a remote data visualization professional. What technical skills do you need to succeed?

3 most important technical skills you need on the long run are: 1. Programming Proficiency: You need to have a strong understanding of python especially libraries like seaborn, matplotlib, pandas, etc. 2. Database and Query Skills: You need to also understand how to use SQL to interact with databases for retrieval purposes especially. Your DQL (Data query Language is important) 3. Business Intelligence Tools: Experience with BI tools like Tableau or Power BI for creating interactive visualizations. Ability to translate complex data insights into accessible visual format.

Here are some key technical skills: [1] Data Analysis and Manipulation: You should be proficient in handling data using tools like Python or R. This includes cleaning, transforming, and aggregating data to prepare it for visualization. [2] Data Visualization Libraries: Mastery of data visualization libraries such as Matplotlib, Seaborn, Plotly (in Python), ggplot2 (in R) etc. is essential. Understanding how to create various types of charts, graphs, and interactive visualizations is crucial. [3] Dashboard Creation: Knowledge of dashboard creation tools like Tableau, Power BI, or Dash (for Python) is important. You should be able to design and build interactive dashboards that provide insights into complex datasets.

As a remote data visualization professional, having strong skills in data analytics, visualization tools, programming languages, design principles, and storytelling is crucial for success. Proficiency in data manipulation, visualization tools like Tableau and programming languages like Python, R, and SQL are essential for creating informative visualizations and interactive dashboards. Understanding data structures, algorithms, and web development technologies can also be helpful. Having a good eye for design and storytelling, as well as staying up-to-date with the latest trends, are important for effectively communicating complex data insights to stakeholders and continuously improving in the field.

Para prosperar en la visualización remota de datos, es esencial dominar herramientas como Tableau y Power BI para crear visualizaciones efectivas. También debes tener sólidos conocimientos en la limpieza y manipulación de datos utilizando lenguajes como SQL y Python, y habilidades en dise?o gráfico para garantizar la estética y comprensión de las visualizaciones. Además, debes ser capaz de trabajar con grandes conjuntos de datos, crear paneles interactivos para la exploración de datos y adaptar las visualizaciones para su uso en entornos web y móviles. Interpretar las necesidades de los usuarios y mantenerse al día con las últimas tendencias y tecnologías en visualización de datos también son cruciales para el éxito en este campo.

Tools for data visualization include: Tableau Microsoft Power BI Google Data Studio Python libraries like matplotlib, seaborn, and Plotly R Excel QlikView

Data visualization is done at many levels/layers. Some platforms offer in-site visualizations with customization such as Google Analytics or Salesforce. No coding is needed. This is used by managers and users of said platform After the data is extracted you can also visualize it in your landing data warehouse/lakehouse, Databricks and Snowflake offer dashboards for this purpose. Coding will be needed. This is used by Data professionals mostly for diagnostics and prototypes. Finally you can visualize it a BI platform like PowerBI, Tableau or Qlik. This reports are usually intended for directors and managers, but can really be tailored towards everyone in the company.

Use of correct data visualization is extremely important based on client needs/cost/usability you can choose: Tableau Power BI Excel Google Data Studio/Looker studio Python R/Rshiny Qlik etc.

In my experience, the most useful language for Data practitioners nowadays is Python for data visualization, data manipulation, automations, scripting and even APIs. SQL is also instrumental. R is popular but not strictly needed if you already know Python. HTML is seldom used, mainly for web-scraping, RPAs that gather information from webpages. JavaScript is rarely used, I had to use it once for a custom QlikSense extension. Most visualizations are done within BI platforms. In my experience if something needs web-development it's best to externalize the service than to learn it yourself for a single task.

Data visualization is where the data practitioner has to learn web development like skills. Once I had to develop a custom extension for QlikSense using JavaScript, although I'm not an expert, my background in other languages allowed me to work with JS. In static reports HTML, CSS styles and other similar languages are also used, for example in Qlik Nprinting, although you can use a graphical interface for your report, everything is still web-page like code in the backend, and getting some things done might require you to work with this code directly.

Always avoid redundancy. Try to keep a database/sheet with a list of the dashboards in your company's environment and the basic descrption of every visualization. For example if it's a graph: "Dashboard: General Sales Graph 1: Monthly Sales vs Period - 6 Months" You will be surprised on how many redundant visualizations and sometimes entire reports that are being mantained and could save precious time and resources. Some services like Microsoft Fabric are attempting to make things easier for us in this regard.