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所在平台: Udemy |
课程主页: https://www.udemy.com/course/predictive-prescriptive-analytics-for-decision-making/
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**课程名称：** 预测与指示性分析在业务决策中的应用 **课程概述：** 本课程教授如何利用数值数据构建预测性与指示性模型。指示性分析能够帮助企业应对不确定性，预防欺诈，降低风险，提高效率，达成业务目标，并增强客户忠诚度。本课程将深入探讨指示性分析的定义，它如何综合信息（如情景、资源、历史及当前表现）来提出行动建议或策略，并适用于各种时间维度（短期至长期）的决策。 **您将学到：** * 理解横断面数据与纵向数据之间的区别。 * 区分预测性问题和预测（Forecasting）问题，并将这些概念应用于数据驱动的决策。 * 掌握参数化和非参数化建模方法，并理解它们在预测准确性与模型可解释性之间的权衡。 * 利用线性规划（LPP）构建多种“假设情景”（What-if Scenarios），这在业务决策中至关重要。 * 理解梯度下降算法，这是许多常用机器学习算法的基础。 **核心技能：** * 利用数值数据构建预测与指示性模型。 * 时间序列预测。 * 通过线性规划实现优化。 * 梯度下降及其在机器学习中的应用。 * 构建适用于业务决策的分析框架。 **适合人群：** * 拥有1-8年工作经验的专业人士。 * 工程、数学/统计学、编程背景优先。 * 目标职业包括：领域专家、工程师、软件与IT专业人士、项目经理、业务分析师、顾问、创业者。 * 拥有5年以上工程师经验者。
PREDICTIVE, PRESCRIPTIVE ANALYTICS FOR BUSINESS DECISION MAKINGLEARN HOW TO BUILD PREDICTIVE AND PRESCRIPTIVE MODELS USING NUMERICAL DATAPrescriptive analytics can cut through the clutter of immediate uncertainty and changing conditions. It can help prevent fraud, limit risk, increase efficiency, meet business goals, and create more loyal customers.Prescriptive analytics is a type of data analytics-the use of technology to help businesses make better decisions through the analysis of raw data. Specifically, prescriptive analytics factors information about possible situations or scenarios, available resources, past performance, and current performance, and suggests a course of action or strategy. It can be used to make decisions on any time horizon, from immediate to long term.What will you Learn?Understand the difference between Cross sectional and Longitudinal data.Differentiate between a prediction and forecasting problem scenario and apply these concepts towards data led decision making.Understand Parametric and Non Parametric modelling approach towards addressing the key tradeoff between Predictive accuracy and Explain- ability of models.Use LPP towards building multiple "What if " scenarios which are widely used in business decision making.Conceptualize Gradient Descent Algorithm which is a key foundation for most of the widely used Machine learning algorithms to be introduced subsequently.Top skills you will learnDevelop predictive and prescriptive models using numerical dataTime-series ForecastingOptimization through Linear ProgrammingGradient Descent and it's applicability in Machine LearningFramework towards business decisionsIdeal For1 - 8 yrs work experience.- Engineering, Math/Statistics/Programming background preferredTypical roles: Domain experts, Engineers, Software and IT Professionals, ProjectManagers, Business Analysts, Consultants, Entrepreneurs.Engineers with over 5 years of experience
