Overview
Modern analytics rarely lives in a single spreadsheet. Data sits in SaaS tools, cloud databases, and internal systems and APIs are how you move it reliably into reports, models, and automations. In this track I’ll tutor you on how REST and platform APIs work in practice, including the Google Sheets API and typical database-backed sources, and how to implement and consume them inside Power BI, Tableau, and Python. We can go deep on data-engineering patterns and on automating repetitive extract–transform–load style tasks so your pipelines run with less manual work.
Who this is for
Analysts and developers who want to stop copy-pasting exports; BI builders who need live or scheduled data; and anyone preparing to work with cloud tools where “API-first” is the norm. You might be self-taught in Excel or Power BI and ready to level up, or a student working on a project that requires programmatic data access.
Curriculum
Connecting systems with confidence
Your stack is unique. We focus on the threads that matter—whether that is reading API docs without guesswork, fixing a broken refresh, or building a small pipeline you can run again next week.
- REST and HTTP, explained clearly. We walk through how data moves on the web: requests and responses, methods, endpoints, headers, JSON payloads, status codes, and pagination. You also learn how real teams handle authentication API keys and OAuth so you can follow documentation and troubleshoot with confidence.
- Using Google Sheets between systems. You practice reading and writing ranges, batch updates, and sensible patterns for when a spreadsheet is the right link between tools—and when you should reach for a database instead.
- Blending API data with databases in reporting. We look at how dashboards combine live or scheduled API feeds with SQL-backed sources, and how naming and modelling keep mixed pipelines honest and easy to explain to others.
- Power BI as a consumer of APIs. Web connectors, parameters, refresh schedules, and turning awkward nested JSON into clean tables inside Power Query.
- Tableau and web-sourced data. Connecting to web data and, where it suits your process, bringing API-driven extracts together with your existing connections.
- Python for glue code and integrations. Calling HTTP endpoints with a standard client library, parsing JSON, handling errors gracefully, saving outputs, and running jobs on a simple schedule or from a notebook when that fits your workflow.
- Automation you can maintain. Designing repeatable pulls, logging failures in a useful way, and building habits so your integrations survive the next vendor API update.
Outcomes you can aim for
By the end of a focused series of sessions, you should be able to read API documentation with confidence, prototype a connection in your tool of choice, debug common failures, and design a small automation that fits your real workload whether that’s refreshing a dashboard or feeding a model training pipeline.
How sessions work
Content is driven by your goals: we can follow a structured syllabus or jump straight into a dataset or API you already use at work or in school. Bring your own examples when possible — that’s when learning sticks fastest.