In the field of supply chain optimization, data science plays a crucial role. A staggering 70% of effective supply chain management depends on data science techniques. To excel in this area, professionals must master several key skills. Proficiency in statistical analysis is essential, as it forms the backbone of data-driven decision-making. Knowledge of machine learning algorithms is also vital, enabling predictive analytics that can forecast demand and optimize inventory levels. Additionally, understanding data visualization tools is important for communicating complex data insights clearly. Familiarity with programming languages such as Python or R is necessary for implementing these techniques. With 70% of the job hinging on data science, continuous learning and adaptation to new technologies are imperative for success in supply chain optimization.
Hamish Ivison
@hamishivi
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Mar 4 How well do data-selection methods work for instruction-tuning at scale?Turns out, when you look at large, varied data pools, lots of recent methods lag behind simple baselines, and a simple embedding-based method (RDS) does best!More below (1/8)
Carolina Rudinschi, PhD
@CRudinschi
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Apr 4 AI in manufacturing isn’t just about models—it’s about data readiness. John Harrington explains how #DataOps ensures that #AI has the right data to optimize yield, quality, and maintenance. https://buff.ly/WVNVnwM#sponsored #highbyte_iiot #HM25
@HighByteInc
@GregorianCT1
ChainStats
@ChainStatsPro
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Apr 8 In today’s markets, traders are stuck with delayed, centralized, and incomplete data—leaving them steps behind.At ChainStats, we bridge the gap between human intuition and algorithmic execution with real-time on-chain intelligence.As AI agents and automated strategies rise,
Math Cafe
@Riazi_Cafe_en
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Mar 2 EPFL’s “Optimization for Data Science”PDF: https://n.ethz.ch/~jiaxie/graduate_projs/notes_eth.pdf…
Blythe (Brumleve) Milligan
@blythebrum
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Apr 6 If your supply chain is still running on spreadsheets and shared folders, it’s time for a serious upgrade.Adam Anderson, Account Management Lead at Crisp, said the real key to unlocking AI’s value isn’t just collecting more data—it’s making sure that data is reliable,
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