Power BI: tell the data story
Power BI training: prepare data with Power Query, build a data model, write DAX measures and publish reports your team understands.


Courses on data analytics, data engineering, Power BI, Tableau, Kafka and Jupyter. Compare topics, levels and role fit, then enquire about training.
Data science includes courses on analysing, preparing and visualising data. The topics suit analysts, controllers, data engineers and specialists who prepare data for decisions. If you build analyses, start with “Data analytics”. For pipelines and data flows, “Data engineering” fits. Reports and dashboards are covered by “Power BI: tell the data story”. Decide by focus: analyse, provide or visualise. There are also courses on Kafka, Jupyter and exam-related Microsoft topics; vendor names imply no partnership. Prerequisites, scope and dates are confirmed on enquiry.
Power BI training: prepare data with Power Query, build a data model, write DAX measures and publish reports your team understands.
Data analytics training: frame analytical questions, check data quality, analyse data with Python and pandas and present findings clearly to decision makers.
Data engineering training: design data pipelines with Airflow, Spark and dbt, model data cleanly and run and test pipelines so they stay reliable.
Tableau training: connect data, choose the right visualisation, build interactive dashboards and present results to colleagues and management.
Model an event-driven workflow and trace a message from producer to consumer. Focus on understandable events, handling repeated processing and the boundaries of ordering and delivery.
Combine data checks, code and explanations into a notebook another person can follow. Investigate hidden execution state and document which conclusions are actually supported by the data.
PL-300 training for Power BI: connect and clean data, build semantic models, calculate measures in DAX and publish reports with secure access.
DP-600 training on Microsoft Fabric: structure solutions, choose between lakehouse and warehouse, query data and tune semantic models for reporting.
Data engineering in Microsoft Fabric: load, transform, secure and monitor batch and streaming data, in preparation for DP-700.
Acquire, clean and statistically analyse data, then present it clearly: this course prepares you for the CompTIA Data+ DA0-002 exam.
Load, join, group and analyse data as time series using NumPy and pandas, then show your results in clear charts built with Matplotlib.
From your first data connection to an interactive dashboard: learn Tableau Desktop and prepare for the Foundations exam at the same time.
Joins, extracts, parameters and table calculations: build Tableau Desktop analyses that still hold up when the questions get harder.
Collect, explore and model data, then explain your results: practise the whole data science workflow and prepare for the DSP-210 exam.
AWS training on data platforms: define sources and quality, design storage and access, process data and document a repeatable workflow.
Data engineering training on Google Cloud: define sources and quality, design storage, process data and make the workflow repeatable.
In the SAP Analytics Cloud course you define data questions, model data, build reports and planning and check quality and access rights.
Databricks course on lakehouse data platforms: design the data model, prepare data, check quality and document a repeatable workflow.
Snowflake course on data platforms and analytics: design the data model, query and process data, check quality and security, and document the workflow.