Data Intelligence Professional Foundation

"A short orientation before the tools. It sets out what data intelligence is -- turning data into reliable intelligence for decisions, systems and solutions -- and how that differs from data analytics. You leave knowing the shape of a data professional's work, how the three levels build on each other, and where the pathway leads."

"The gentlest on-ramp into structured data work, and a tool most analysts still use daily. You build real working capability in a spreadsheet -- formulas, lookups, pivot tables, clean data entry -- enough to answer everyday questions from a dataset without writing code. Tool-agnostic across Excel, Google Sheets and LibreOffice Calc. Stops at self-service data modelling (Power Query / Power Pivot / DAX), covered in the BI & Visualization specialisation."

"The essential SQL capability every data professional needs: reading, filtering, combining and summarising data from a relational database in standard ANSI SQL that works across MySQL, PostgreSQL, SQL Server and Oracle. You finish able to answer clear questions from a single database confidently. Stops where analytical querying begins -- subqueries, CTEs and window functions are SQL Applied."

"How the data a professional queries is actually structured, and how to design a structure that holds up. Keys, normalisation and entity-relationship modelling; you can read, critique and produce a sound relational schema for a transactional system. Dimensional / analytical modelling is awareness only -- its depth belongs in the Data Engineering and BI specialisations."

"The reasoning a data professional applies before and after the calculation. Data literacy -- questioning where a number came from and what it does and does not say -- plus the practical statistics to summarise data, describe uncertainty, and tell correlation from causation. Formal experiment design is out of scope here; it is owned by Business Analytics so it is not taught twice."

"Applied SQL for real analysis: composing multi-table queries, subqueries, CTEs and window functions to answer layered questions from messy, realistic datasets -- the queries an analyst actually writes on the job. Assumes SQL Fundamentals."

"Python as a data professional's second core tool alongside SQL: the language essentials, then the pandas / NumPy workflow for loading, shaping and analysing data programmatically. Deliberately overlaps SQL and the EDA course -- the same task done a second way builds real fluency. Advanced wrangling and production practices are out of scope here."

"The part of the job that is most of the job. You take raw, real-world data -- inconsistent, incomplete, duplicated -- and turn it into something analysis can trust, with steps that can be re-run when the data refreshes. Refocused from 'manipulation & analysis' onto preparation specifically."

"This course teaches systematic techniques to explore, understand, and extract insights from data before formal modeling or reporting."

"Two halves of one job: making data visible and making it land. Clear statistical and comparative visuals, dashboards and repeatable reports, then structuring a finding for a decision-maker without distorting it. Absorbs the former Business Communication & Storytelling course. Ends with your first real portfolio piece."

"Build a rigorous foundation in machine learning: training, evaluation, and the bias-variance tradeoff that governs model quality."

"Where you move from "I can work with data" to "I can understand a professional problem and determine what a data solution needs to do." Given a vague business ask, you learn to find the real problem underneath it, identify who and what constrains the solution, and produce clear, testable requirements a team could build from. This is the first place inside the Foundation program where solution-thinking is taught rather than assumed."

"SQL used the way a professional uses it: from a requirement, for performance, in a team, toward something that runs in production. You cover how a database executes a query, how to write queries that stay fast as data grows, and the practices that make SQL work maintainable by more than one person. Assumes SQL Applied."

"Key business metrics, customer and revenue analytics, and A/B testing -- building the bridge between raw analysis and business decision-making."

"Data privacy, bias and fairness, transparency, and real-world regulation (GDPR and beyond) -- culminating in an ethics review of a real or hypothetical data project."