[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["缺少我需要的資訊","missingTheInformationINeed","thumb-down"],["過於複雜/步驟過多","tooComplicatedTooManySteps","thumb-down"],["過時","outOfDate","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["示例/程式碼問題","samplesCodeIssue","thumb-down"],["其他","otherDown","thumb-down"]],["上次更新時間：2024-07-26 (世界標準時間)。"],[[["\u003cp\u003eThis module teaches you to identify potential issues in datasets, including biases and invalid inferences, ultimately helping you build better ML models.\u003c/p\u003e\n"],["\u003cp\u003eUnderstanding data limitations and collection conditions is crucial to avoid "data cascades" that lead to poor model performance and wasted resources.\u003c/p\u003e\n"],["\u003cp\u003eThe module explores common data analysis pitfalls, such as mistaking correlation for causation, and emphasizes the importance of proper data exploration and preprocessing in machine learning workflows.\u003c/p\u003e\n"],["\u003cp\u003eBy recognizing common problems in charts and data visualizations, you'll be able to avoid misperceptions and ensure accurate data representation.\u003c/p\u003e\n"]]],[],null,["\u003cbr /\u003e\n\n| **Estimated time:** 1.5 hours\n\nLearning objectives\n\nIn this module, you will learn to:\n\n- Investigate potential issues underlying raw or processed datasets, including collection and quality issues.\n- Identify biases, invalid inferences, and rationalizations.\n- Find common issues in data analysis, including correlation, relatedness, and irrelevance.\n- Examine a chart for common problems, misperceptions, and misleading display and design choices.\n\nML motivation\n\nWhile not as glamorous as model architectures and other downstream model work,\ndata exploration, documentation, and preprocessing are critical to\nML work. ML practitioners can fall into what Nithya Sambasivan et al. called\n[data cascades](https://research.google/blog/data-cascades-in-machine-learning/)\nin their [2021 ACM paper](https://dl.acm.org/doi/10.1145/3411764.3445518)\nif they do not deeply understand:\n\n- the conditions under which their data is collected\n- the quality, characteristics, and limitations of the data\n- what the data can and can't show\n\nIt's very expensive to train models on bad data and\nonly find out at the point of low-quality outputs that there were problems\nwith the data. Likewise, a failure to grasp the limitations of data, human\nbiases in collecting data, or mistaking correlation for causation,\ncan result in over-promising and under-delivering results, which can lead to a\nloss of trust.\n\nThis course walks through common but subtle data traps that ML and data\npractitioners may encounter in their work."]]
