Data Transformation

This path is focused on developing expert level skills in data cleaning, data transformation and data modeling. You will learn to maximize the capabilities of the Power Query UI, and also write M code.

This path is designed to help you build expert level skills in the implementation, monitoring and governance of Power BI, including making optimal use of the Service through dataflows, datasets, thin reports, etc.

This Learning Path dives into SharePoint and Power Apps, focusing on leveraging Power Automate for task automation and improved user interaction. 

This Learning Path offers a comprehensive journey through data visualization and analysis techniques, with a special emphasis on Power BI.

This path is similar to the Data Analyst learning path above, but is geared to building a strong and broad foundation of data analysis skills, as compared to being more specifically focused on passing the PL 300 exam.

This Learning Path delves into data analysis techniques, focusing on Power BI functions. It guides learners to enhance their data insight extraction skills.

This Learning Path offers a comprehensive journey through advanced data analysis and modeling techniques all illustrated through practical scenarios.

This path is designed to build the broad range of expert-level knowledge necessary to confidently pass the PL-300 Data Analyst certification exam (replacement for DA-100).

Get familiar with structured values in the M language and discover how to interact with them.

The second part of the preparation course for the Microsoft's DP-500 Exam Certification

Acquire an extensive knowledge and understanding of the Power Query and M language to help you easily transform your data from multiple sources into Power BI

Unlock the power of data analysis and visualization with PandasAI, and transform raw data into actionable insights that drive decision-making and growth.

Discover how to utilize ChatGPT within Power BI for enhanced data analysis, reporting, and interactive dashboards. Learn how to integrate ChatGPT into Power BI to create dynamic and conversational visualizations.

Expand Power BI's capabilities in accessing a wide range of data using APIs

Learn how to clean, optimize and model data tables to develop effective, insightful and actionable Power BI reports

Deepen your understanding of the Power Query/M concepts through practical application

Create advanced analytical models by learning how to harness the power of DAX calculations for use within Power BI

Know how to get DAX Query data from Power BI Desktop without having to write it yourself

Find out how Sidekick can help to review, optimize & cleanup your dataset and reports

Create external tool to generate the Enterprise DNA extended date table (Holidays, Some Date Intelligence Measures, Set Sort By)

Learning how to use SELECT, FROM, WHERE, GROUP BY, HAVING, ORDER BY

Learn how to use other data sources to filter and/or colorize individual objects of a 3D model in Power BI

Scale up your customer service management to drive transformation of data into compelling reports with Power BI

Discover the key points around OEE and producing a technically sound report

Take on the challenge of creating an analysis report on hotels revenue management data.

Implement your analytical skills in creating a report of the entire business operation from production to forecasting.

Challenge your data skills by creating a report on transport and shipping data 

Work on one of the most significant measures for manufacturing productivity - OEE

Create an analysis report on environmental data and potentially impact how this data is visualized and recorded

Produce insights for management level that can highlight any issues and prepare them for the audit

Analyze the data of a large insurance company who are due to undergo an internal audit with regard to its complaint’s procedure

Deep dive into the world of F1 and create an analysis of the drivers' stats

This SQL cheat sheet gives you the most common SQL objects and commands, including data types, DML and DDL statements, aggregate functions, string and date functions, and subqueries.

This offers techniques to retrieve and manipulate data based on specific row indexes or key matches, enhancing the flexibility and precision of data analysis.

When programming in Python, having a reliable cheat sheet by your side can be a game-changer. Python may be known for its simplicity and readability, but there’s no denying that its extensive range of features and functionalities is too much to memorize!