|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/dp-100-practice-exam-actual-practice-questions/
课程评论：没有评论
课程名称：DP-100 实践考试 - 实际与练习题 课程概述：本课程旨在帮助学员准备DP-100考试，主题为设计与实施Azure上的数据科学解决方案。课程于2024年3月最新更新，提供150多道经过精心挑选的考试问题，确保学员能够首次成功通过考试。课程将持续增加更多问题。 技能评估： - 设置Azure机器学习工作区（占30-35%） - 运行实验和训练模型（占25-30%） - 优化和管理模型（占20-25%） - 部署和使用模型（占20-25%） 详细技能： - 定义和准备开发环境（占15-20%） - 选择开发环境，评估部署环境约束，分析并推荐符合系统需求的工具。 - 设置开发环境并配置数据科学工作环境。 - 定义商业问题并量化技术成功指标，量化风险。 - 为建模准备数据（占25-30%） - 转换数据至可用数据集，开发数据结构，设计数据采样策略，设计数据准备流程。 - 执行探索性数据分析（EDA），识别数据模式并确定后续步骤，同时识别异常值和数据不一致性，创建描述性统计。 - 特征工程（占15-20%） - 执行特征提取与选择，定义最优标准并应用对应算法。 - 开发模型（占40-45%） - 选择算法，确定性能指标，实施适当算法，考虑特定算法的数据准备步骤。 - 划分数据集并检测数据不平衡，实施重采样和惩罚措施，培训模型并评估其性能。 数据科学家是当今最受欢迎的职业，获得认证的数据科学家比未认证者有更高的就业机会。 此课程适合希望提升数据科学能力及通过DP-100考试的学员。
Exam DP-100: Designing and Implementing a Data Science Solution on AzureLatest Update on March 2024. Exam questions that have been carefully selected to ensure your success on the first try. 150+ different questions. More questions will be added soon...Skills measuredSet up an Azure Machine Learning workspace (30-35%)Run experiments and train models (25-30%)Optimize and manage models (20-25%)Deploy and consume models (20-25%)Detail SkillsDefine and prepare the development environment (15-20%)Select development environmentassess the deployment environment constraintsanalyze and recommend tools that meet system requirementsselect the development environmentSet up development environmentcreate an Azure data science environmentconfigure data science work environmentsQuantify the business problemdefine technical success metricsquantify risksPrepare data for modeling (25-30%)Transform data into usable datasetsdevelop data structuresdesign a data sampling strategydesign the data preparation flowPerform Exploratory Data Analysis (EDA)review visual analytics data to discover patterns and determine next stepsidentify anomalies, outliers, and other data inconsistenciescreate descriptive statistics for a datasetCleanse and transform dataresolve anomalies, outliers, and other data inconsistenciesstandardize data formatsset the granularity for dataPerform feature engineering (15-20%)Perform feature extractionperform feature extraction algorithms on numerical dataperform feature extraction algorithms on non-numerical datascale featuresPerform feature selectiondefine the optimality criteriaapply feature selection algorithmsDevelop models (40-45%)Select an algorithmic approachdetermine appropriate performance metricsimplement appropriate algorithmsconsider data preparation steps that are specific to the selected algorithmsSplit datasetsdetermine ideal split based on the nature of the datadetermine number of splitsdetermine relative size of splitsensure splits are balancedIdentify data imbalancesresample a dataset to impose balanceadjust performance metric to resolve imbalancesimplement penalizationTrain the modelselect early stopping criteriatune hyper-parametersEvaluate model performancescore models against evaluation metricsimplement cross-validationidentify and address overfittingidentify root cause of performance resultsData Scientist is most demanded skill of this era. Certified Data Scientist get more chance to get hired than non-certified candidate.
