Power Query and M Language the easy way

¡Hola! It seems like you're looking for a comprehensive list of topics related to Power Query, M Language, and Power BI, including best practices and DIAD (Data Insights and Analytics Division) resources. Let's break down the topics you mentioned and provide guidance on each one:

1. Statistics using Power Query and M Language: This involves using Power Query to perform statistical operations on your data set, such as calculating averages, medians, standard deviation, etc. M Language provides a variety of functions for statistics, like List.Stats.

2. Standard and Scientific using Power Query and M Language: These are mathematical operations you can perform in Power Query using M Language. You can convert numbers to scientific notation or back with functions like Number.ToScientific and Number.FromScientific.

3. Trigonometry - Rounding - Information using Power Query and M Language: Trigonometric functions (Sin, Cos, Tan, etc.) are available in M Language, as well as rounding functions (Round, IntegerPart, etc.), and information functions like List.LookUp.

4. Date Transformation using Power Query and M Language-0/1: This includes all the operations you can perform on dates and times in Power Query, such as adding or subtracting days, extracting parts of a date, formatting dates, etc.

5. Time-Duration Transformation using Power Query and M Language: You can manipulate time and duration columns, convert between time units, and add durations to dates.

6. Column From Example using Power Query and M Language: This is a feature in Power Query that allows you to create a new column based on examples provided. It uses pattern recognition to apply the transformation to the entire dataset.

7. Custom Column using Power Query and M Language-0/1: You can define custom columns by writing M Language formulas. This can be done step by step or as a single operation.

8. Invoke Custom Column using Power Query and M Language-0/1: This involves calling custom columns that you've defined earlier in the query editing process.

9. Conditional Column using Power Query and M Language: You can create columns based on conditional logic, such as if statements or List.SelectRows, which filters rows based on certain conditions.

10. Index Column using Power Query and M Language-0/1: This is about creating new columns from existing ones by referencing their index, which is useful when you want to extract individual components of a column.

11. Duplicate and Merge Column using Power Query and M Language: You can identify duplicate rows, remove them, or merge columns that contain duplicate values.

12. Format - Trim - clean -Lower - Upper Case using Power Query: These are text transformation operations you can perform in Power Query to clean up text data.

13. Extract using Power Query and M Language: Extracting data from a column, like extracting the first letter of each item, or more complex extractions involving XML or JSON data.

14. Parse XML Data using Power Query and M Language: Power Query can parse XML files and pull out relevant data for use in Power BI models.

15. Parse JSON Data using Power Query and M Language: Similar to XML, Power Query provides functions to parse JSON data into a structured format that you can model.

16. Statistics using Power Query and M Language: This is another mention of statistics but with the context of Power BI models, which can utilize these stats for visualizations or DAX calculations.

17. Standard and Scientific using Power Query and M Language: Again, this refers to the mathematical operations available in M Language for standard and scientific computations.

18. Trigonometry - Rounding - Information using Power Query and M Language: This is a repeat of the earlier point but serves as a reminder of the capabilities within Power Query and M Language for these types of operations.

19. Date Transformation using Power Query and M Language-0/1: A reiteration of the date transformation capabilities, with "-0" possibly indicating more basic transformations and "-1" indicating more advanced or complex ones.

20. Time-Duration Transformation using Power Query and M Language: Another repeat, emphasizing the importance and versatility of working with time and duration in Power Query.

21. Column From Example using Power Query and M Language: This is mentioned again as a key feature for deriving column transformations from examples.

22. Custom Column using Power Query and M Language-0/1: This is further clarified as a feature where you can define custom columns dynamically or with a step-by-step approach.

23. Invoke Custom Column using Power Query and M Language-0/1: This emphasizes the ability to use the custom columns you've defined later in your data transformation process.

24. Conditional Column using Power Query and M Language: This is highlighted again as a powerful way to manipulate data based on conditions.

25. Index Column using Power Query and M Language-0/1: This is repeated with an emphasis on referencing columns by their index for specific transformations.

26. Duplicate and Merge Column using Power Query and M Language: This capability is crucial when dealing with datasets that have duplicate values or need to merge data from multiple columns.

27. Format - Trim - clean -Lower - Upper Case using Power Query: This is a reminder of the text manipulation capabilities within Power Query.

For best practices, you should look into official Microsoft documentation and resources specific to the DIAD (Data Insights and Analytics Division), which often provide industry-specific guides and best practices for using Power BI and its associated tools like Power Query and M Language.

As for DIAD resources, these are typically found on the Microsoft Power Platform documentation site or through learning paths and courses offered by Microsoft, partners, or community experts. These resources will guide you through implementing solutions that cover all aspects of your data analytics lifecycle, from data ingestion and transformation to visualization and sharing of insights.