Python: With libraries like Pandas (data manipulation), NumPy (numerical operations), SciPy (scientific computing), Matplotlib/Seaborn (visualization), and Statsmodels/Scikit-learn (statistical modeling and machine learning). Jupyter Notebooks or VS Code are common environments.
R: A language and environment specifically designed for statistical computing and graphics. RStudio is its popular IDE.
SAS: A powerful suite of analytics software widely used in certain industries (e.g., finance, pharma).
SPSS: Another popular statistical software package, particularly in social sciences and market research.
Data Warehousing Platforms:
Cloud-Based: Amazon Redshift, Google whatsapp number list BigQuery, Snowflake, Azure Synapse Analytics. BI Analysts need to understand how to connect to and query these systems.
Collaboration and Project Management Tools:
Jira, Confluence: For tracking projects, tasks, and documentation.
Slack, Microsoft Teams: For communication and collaboration.
Git/GitHub/GitLab: For version control of code (e.g., SQL scripts, Python/R code).
The specific toolset will vary, but proficiency in SQL, a leading visualization tool (like Tableau or Power BI), and Excel is typically non-negotiable.
Chapter 6: The BI Analyst's Workflow – From Question to Insight
A typical project or analytical task for a BI Analyst often follows a structured workflow:
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