2024暑期iHUB·上海：商業(yè)分析專題： 數(shù)據(jù)分析與統(tǒng)計方法在流程優(yōu)化及供應需求中的應用

專業(yè)：商業(yè)

項目類型：海外導師線下項目

開始時間：2024年07月20日

是否可加論文：是

項目周期：1周在線科研+14天面授科研+5周在線論文指導

語言：英文

有無剩余名額：名額充足

建議學生年級：大學生 高中生

是否必需面試：否

適合專業(yè)：商業(yè)分析金融學財務管理數(shù)據(jù)分析創(chuàng)業(yè)創(chuàng)新風險管理數(shù)學商業(yè)統(tǒng)計公司管理商業(yè)決策

地點：上海圣華紫竹學院

建議選修：高等數(shù)學微積分與應用

建議具備的基礎：商業(yè)分析、風險管理、管理學、統(tǒng)計學，應用數(shù)學等專業(yè)或者希望修讀相關(guān)專業(yè)的學生；具有代數(shù)及微積分基礎的學生優(yōu)先

產(chǎn)出：1周在線科研+14天面授科研+5周在線論文指導 項目報告 優(yōu)秀學員獲主導師Reference Letter EI/CPCI/Scopus/ProQuest/Crossref/EBSCO或同等級別索引國際會議全文投遞與發(fā)表指導（可用于申請） 結(jié)業(yè)證書 成績單

項目背景：數(shù)據(jù)具有固有的不確定性，例如：人的感情；天氣形勢；可再生資源；以及未來預測。盡管存在不確定性，數(shù)據(jù)仍然包含寶貴的信息。從本質(zhì)來講，人類不喜歡不確定性，但簡單地忽略這一點可能產(chǎn)生比不確定性本身更多的問題。 在大數(shù)據(jù)時代，高管需要以不同的方式處理不確定性的各個維度。他們需要承認、接受這一點，并確定如何充分利用不確定的數(shù)據(jù)。大數(shù)據(jù)的重要作用之一便是可以作為客戶和企業(yè)之間的雙向通道。例如，特斯拉電動車在駕駛和停車時產(chǎn)生大量數(shù)據(jù)。在行駛中，司機持續(xù)地更新車輛的加速度、剎車、電池充電和位置信息。數(shù)據(jù)也傳回工程師以了解客戶的駕駛習慣，用于優(yōu)化汽車性能。本項目旨在探索如果獲取更多的不同種類的數(shù)據(jù)，以及培養(yǎng)數(shù)據(jù)分析能力，包括軟件工具和使用這些數(shù)據(jù)分析工具的必備技能。 Managers encounter data daily and regularly base their decisions on it. In the published book, “Competing on Analytics: The New Science of Winning”by Harvard Business School Press, Thomas H. Davenport and Jeanne G. Harris reveal how organizations such as Amazon.com, Wal-Mart, Netflix, Capital One, and others use analytics as a tool for competitive differentiation and advantage. Business analytics is the sensible use of data and quantitative models for informing decisions and actions. Business Analytic can help companies make better decisions by showing present and historical data within their business context. Analysts can leverage business analytic to provide performance and competitor benchmarks to make the organization run smoother and more efficiently. Analysts can also more easily spot market trends to increase sales or revenue. Used effectively, the right data can help with anything from compliance to hiring efforts.

項目介紹：商業(yè)數(shù)據(jù)分析是企業(yè)運營中高效管理的重要技能。通過對企業(yè)的銷售、利潤和其他關(guān)鍵指標的變化趨勢建模，可以對這些指標的未來進行有效的科學預測。通過數(shù)據(jù)分析和建模了解可能發(fā)生的季節(jié)性、年度或任何規(guī)模的變化，可以讓企業(yè)經(jīng)營有備無患。該項目內(nèi)容為商業(yè)分析核心知識與技能，包括統(tǒng)計分析、概率分布、決策分析、抽樣分布、置信區(qū)間、假設檢驗、回歸模型等。其中，概率模型側(cè)重不確定性和風險處理；統(tǒng)計分析側(cè)重數(shù)據(jù)呈現(xiàn)以及如何通過數(shù)據(jù)獲取有用信息和有效推論；優(yōu)化模型和決策分析側(cè)重運用數(shù)據(jù)進行決策。學生將在項目中運用Excel或Mintab進行商業(yè)數(shù)據(jù)分析，在項目結(jié)束時提交報告，進行成果展示。

Business Analytics and modeling are important skills for effective managerial decision-making in business and industry. Advances in technology (computers, scanners, cell phones) have made a significant amount of data available to managers. Furthermore, business analytics provides a way for businesses to plan for the future. By modeling the trends in a business's sales, profits, and other key metrics, these indicators can be projected into the future. Understanding the changes that are likely to occur seasonally, annually, or on any scale allows businesses to better prepare. The techniques learned in this program will help students infer data and as such make better-informed decisions. The program covers statistical analysis, probability distributions, sampling distributions, confidence intervals, hypothesis testing, and regression models. Probability models provide tools to handle uncertainty and risk. Statistical analysis focuses on the presentation of data and techniques to draw useful and valid inferences from data.

項目大綱：描述性統(tǒng)計與離散概率分布 Descriptive statistics; discrete probability distributions 離散與連續(xù)概率分布；回報/風險分析 Discrete and continuous probability distributions; return/risk analysis 抽樣分布與置信區(qū)間估計 Sampling distributions; confidence interval estimation 假設檢驗 Hypothesis testing about population mean and proportion 簡單回歸模型與多元回歸模型 Simple regression models; multiple regression models 案例分析：供應鏈優(yōu)化及戰(zhàn)略制定 Case Study 項目回顧與成果展示 Program Review and Presentation 論文輔導 Project Deliverables Tutoring