Data Professional Types and Skills
↳ 𝗪𝗵𝗮𝘁 𝗧𝗵𝗲𝘆 𝗗𝗼:
→ Query databases with SQL.
→ Build dashboards using Tableau or Power BI.
→ Present findings to non-technical stakeholders.
𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁
→ 𝗙𝗼𝗰𝘂𝘀: Solving complex problems with data.
↳ 𝗪𝗵𝗮𝘁 𝗧𝗵𝗲𝘆 𝗗𝗼:
→ Develop machine learning models.
→ Perform predictive analysis.
→ Clean and preprocess large datasets.
𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿
→ 𝗙𝗼𝗰𝘂𝘀: Building and maintaining data pipelines.
↳ 𝗪𝗵𝗮𝘁 𝗧𝗵𝗲𝘆 𝗗𝗼:
→ Design robust ETL (Extract, Transform, Load) processes.
→ Ensure databases are scalable and reliable.
→ Work closely with analysts and scientists to make data accessible.
𝗗𝗮𝘁𝗮 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁
→ 𝗙𝗼𝗰𝘂𝘀: Creating the big-picture infrastructure for data flow.
↳ 𝗪𝗵𝗮𝘁 𝗧𝗵𝗲𝘆 𝗗𝗼:
→ Design and structure data systems.
→ Establish data governance practices.
→ Optimize storage and retrieval processes.
𝗠𝗟 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿
→ 𝗙𝗼𝗰𝘂𝘀: Deploying machine learning solutions.
↳ 𝗪𝗵𝗮𝘁 𝗧𝗵𝗲𝘆 𝗗𝗼:
→ Integrate models into production environments.
→ Optimize models for scalability.
→ Monitor performance and address drift issues.
𝗕𝗶 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿
→ 𝗙𝗼𝗰𝘂𝘀: Visualizing and delivering insights.
↳ 𝗪𝗵𝗮𝘁 𝗧𝗵𝗲𝘆 𝗗𝗼:
→ Create interactive dashboards and reports.
→ Develop data visualization strategies for decision-makers.
→ Work with stakeholders to understand reporting needs.
𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗼𝗻𝘀𝘂𝗹𝘁𝗮𝗻𝘁
→ 𝗙𝗼𝗰𝘂𝘀: Advising organizations on data strategies.
↳ 𝗪𝗵𝗮𝘁 𝗧𝗵𝗲𝘆 𝗗𝗼:
→ Analyze business requirements.
→ Propose tools and technologies for data analysis.
→ Provide actionable recommendations for data-driven growth.
𝗗𝗮𝘁𝗮 𝗘𝘁𝗵𝗶𝗰𝗶𝘀𝘁
→ 𝗙𝗼𝗰𝘂𝘀: Ensuring ethical use of data.
↳ 𝗪𝗵𝗮𝘁 𝗧𝗵𝗲𝘆 𝗗𝗼:
→ Define ethical guidelines for data collection and use.
→ Monitor compliance with regulations like GDPR.
→ Address biases in datasets and models.
𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗮𝗴𝗲𝗿
→ 𝗙𝗼𝗰𝘂𝘀: Overseeing data teams and strategies.
↳ 𝗪𝗵𝗮𝘁 𝗧𝗵𝗲𝘆 𝗗𝗼:
→ Manage data team projects and goals.
→ Establish data policies and standards.
→ Ensure alignment of data activities with business objectives.
𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗘𝘅𝗽𝗲𝗿𝘁
→ 𝗙𝗼𝗰𝘂𝘀: Crafting stories through data visuals.
↳ 𝗪𝗵𝗮𝘁 𝗧𝗵𝗲𝘆 𝗗𝗼:
→ Create charts, graphs, and dashboards.
→ Optimize visuals for ease of interpretation.
→ Combine creativity with technical tools like Tableau, Power BI, or D3.js.
Tags:
#DataScience, #DataAnalytics, #DataEngineering, #DataVisualization
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