Descriptive Data Analysis From Databases

I am sitting on several relational databases and I need a clear descriptive view of what the data is actually telling me. Your task is to connect directly to the databases, explore the tables, clean anything obviously out of place, and then surface the key patterns, distributions and summaries in an easy-to-digest format.

Here is the flow I imagine:

• Securely connect to the databases (I can provide read-only credentials).
• Run the necessary SQL queries or use Python/R data-wrangling tools to pull the relevant fields.
• Produce descriptive statistics, frequency counts, and cross-tabulations that highlight trends and anomalies.
• Create intuitive visualisations—simple dashboards, charts or notebooks are fine—as long as they make the findings immediately clear to a non-technical stakeholder.
• Deliver a concise written report (or a well-annotated notebook) that walks through methodology, key observations and next-step recommendations.

I have no preference between SQL, Python (Pandas, Matplotlib, Seaborn), or R (dplyr, ggplot2); use whichever stack lets you work fastest and keeps the analysis reproducible.

Acceptance criteria:
1. All code/notebooks run end-to-end against the provided databases without manual tweaks.
2. Visuals and summary tables match the numbers in the underlying data.
3. The final report answers the basic “who, what, when, where” questions of the dataset in plain language.

If this sounds straightforward to you, let me know what toolset you plan to use and an outline of your approach, and we can get started right away.