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Back in the late nineties, when statisticians apparently decided coding was cool, R emerged as a sort of open-source rebellion against the expensive, proprietary statistical packages dominating the landscape. It promised a flexible environment for data analysis, visualization, and, let’s be honest, a frankly alarming degree of customizability. The reality? It’s a bit like building a car from spare parts – incredibly powerful if you really know what you’re doing, but prone to unexpected breakdowns and requiring constant tinkering.

Compared to Python, which has become the darling of the machine learning crowd, R remains stubbornly focused on statistical computing. While Python strives for general-purpose dominance, R happily wallows in its niche, beloved by academics, biostatisticians, and anyone who enjoys wrestling with package dependencies. It’s still surprisingly relevant, powering much of the statistical rigor behind everything from clinical trials to financial modeling. Though one suspects many users are still running code written on floppy disks.

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analytics engineer

Senior Data Management Professional - Data Quality - Equity Corporate Actions @ bloomberg

GB | 2025-12-25
This role involves managing data quality within Bloomberg’s Equity Corporate Actions team, emphasizing standards, validation, and client relationships. The differentiator is the focus on... read more »
Analytics, Data Quality, Data Management, ETL/ELT, Computer Science, Python, Agile/Scrum, Management, SQL, R
analytics engineer

​​Global MEL ​Senior Data Analysis Officer​​ @ better-cotton-initiative

GB | 2025-12-24 | GBP 37771 / year
The Senior Data Analysis Officer supports farm-level data management and analysis to enhance cotton sustainability metrics, with a key differentiator being its focus on diverse country support and... read more »
Management, Data Quality, Data Collection, Data Analytics, DataViz, Power BI, VBA, SQL, BI, Analytics, Python, R, AI/ML