M Code vs Tidyverse

Developers should learn M Code when working with Microsoft's data ecosystem, especially for ETL (Extract, Transform, Load) processes in Power BI or Excel, as it allows for precise control over data transformations beyond the standard GUI tools meets developers should learn tidyverse when working with data analysis, statistical modeling, or data visualization in r, as it offers a cohesive and user-friendly approach to common data science tasks. Here's our take.

M Code

Nice Pick

Developers should learn M Code when working with Microsoft's data ecosystem, especially for ETL (Extract, Transform, Load) processes in Power BI or Excel, as it allows for precise control over data transformations beyond the standard GUI tools

Pros

• It is essential for automating repetitive data cleaning tasks, integrating disparate data sources, and optimizing query performance in Power Query, making it valuable for roles focused on business intelligence, data analysis, or data engineering within Microsoft environments
• Related to: power-bi, excel-power-query

Cons

• Specific tradeoffs depend on your use case

Tidyverse

Developers should learn Tidyverse when working with data analysis, statistical modeling, or data visualization in R, as it offers a cohesive and user-friendly approach to common data science tasks

Pros

• It is particularly useful for data cleaning, transformation, and exploratory data analysis in fields like research, business analytics, and machine learning, where consistency and readability of code are priorities
• Related to: r-language, dplyr

Cons

• Specific tradeoffs depend on your use case

The Verdict

These tools serve different purposes. M Code is a language while Tidyverse is a library. We picked M Code based on overall popularity, but your choice depends on what you're building.

The Bottom Line

M Code wins

Based on overall popularity. M Code is more widely used, but Tidyverse excels in its own space.