Certiport ITS Data Analytics資料分析核心能力國際認證應考攻略 (1版)

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【簡介】 Information Technology Specialist（ITS）是由Pearson VUE／Certiport推出符合產業趨勢的資訊科技認證，涵蓋IT資訊技術、資料庫、軟體研發、新興科技四大領域，透過 ITS 各項認證指標訓練，可驗證考生是否確實掌握業界所需與具備雇主所需的 IT 技能，幫助考生為未來職涯做好準備。 本書整理了「ITS Data Analytics 資料分析」認證考科綱要所涵蓋的資訊技術與電腦技能，考生可透過精進學習本書各章節重點內容，迅速掌握應考方向。 【目錄】 CH01 資料基礎 Data Basics CH02 資料操作 Data Manipulat CH03 資料分析 Data Analysis CH04 資料視覺化與溝通 Data Visualization and Communication CH05 負責任的分析實踐 Responsible Analytics Practices CH06 模擬試題 CH07 ITS 資訊科技專家國際認證原廠認證應考資訊

Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making (1版)

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【簡介】 Using real-world data case studies, this innovative and accessible textbook introduces an actionable framework for conducting trustworthy data science.Most textbooks present data science as a linear analytic process involving a set of statistical and computational techniques without accounting for the challenges intrinsic to real-world applications. Veridical Data Science, by contrast, embraces the reality that most projects begin with an ambiguous domain question and messy data; it acknowledges that datasets are mere approximations of reality while analyses are mental constructs. Bin Yu and Rebecca Barter employ the innovative Predictability, Computability, and Stability (PCS) framework to assess the trustworthiness and relevance of data-driven results relative to three sources of uncertainty that arise throughout the data science life cycle: the human decisions and judgment calls made during data collection, cleaning, and modeling. By providing real-world data case studies, intuitive explanations of common statistical and machine learning techniques, and supplementary R and Python code, Veridical Data Science offers a clear and actionable guide for conducting responsible data science. Requiring little background knowledge, this lucid, self-contained textbook provides a solid foundation and principled framework for future study of advanced methods in machine learning, statistics, and data science. Presents the Predictability, Computability, and Stability (PCS) methodology for producing trustworthy data-driven resultsTeaches how a data science project should be conducted from beginning to end, including extensive discussion of the data scientist’s decision-making processCultivates critical thinking throughout the entire data science life cycleProvides practical examples and illuminating case studies of real-world data analysis problems with associated code, exercises, and solutionsSuitable for advanced undergraduate and graduate students, domain scientists, and practitioners