密集項目：生物統(tǒng)計學(xué)專題：R語言統(tǒng)計建模在流行病機制剖析與傳染趨勢預(yù)測等領(lǐng)域中的應(yīng)用

專業(yè)：自然科學(xué)

項目類型：國外小組科研

開始時間：2024年11月09日

是否可加論文：是

項目周期：4周在線小組科研學(xué)習(xí)+2周不限時論文指導(dǎo)學(xué)習(xí)

語言：英文

有無剩余名額：名額充足

建議學(xué)生年級：大學(xué)生 高中生

是否必需面試：否

適合專業(yè)：生物學(xué)抗擊冠狀病毒統(tǒng)計學(xué)生物統(tǒng)計學(xué)公共衛(wèi)生學(xué)生物醫(yī)學(xué)統(tǒng)計生物醫(yī)學(xué)生物統(tǒng)計流行病學(xué)生命科學(xué)公共衛(wèi)生

地點：無

建議選修：定量研究分析方法

建議具備的基礎(chǔ)：生物統(tǒng)計、生物醫(yī)學(xué)、公共衛(wèi)生學(xué)等專業(yè)學(xué)生；對數(shù)據(jù)科學(xué)與統(tǒng)計在公共衛(wèi)生與生物醫(yī)學(xué)中的應(yīng)用感興趣的學(xué)生； 學(xué)生需要具備數(shù)理統(tǒng)計、R語言和一些常用庫的使用及數(shù)據(jù)操作基礎(chǔ)

產(chǎn)出：4周在線小組科研學(xué)習(xí)+2周不限時論文指導(dǎo)學(xué)習(xí) 共125課時 項目報告 優(yōu)秀學(xué)員獲主導(dǎo)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(dǎo)（可用于申請） 結(jié)業(yè)證書 成績單

項目背景：生物統(tǒng)計旨在運用數(shù)理原理和方法，分析與闡釋生物數(shù)據(jù)和現(xiàn)象，力圖把握本質(zhì)規(guī)律，解決生物、醫(yī)學(xué)、公共衛(wèi)生問題。數(shù)據(jù)科學(xué)的蓬勃發(fā)展及其在金融等諸多領(lǐng)域的落地為生物醫(yī)學(xué)和公共衛(wèi)生統(tǒng)計分析提供了新方法。目前，R語言、Matlab、SPSS都是全球范圍內(nèi)較為普及的生物信息統(tǒng)計分析工具。項目將廣泛介紹統(tǒng)計數(shù)據(jù)科學(xué)在公共衛(wèi)生和生物醫(yī)學(xué)中的前沿應(yīng)用，指導(dǎo)學(xué)生使用技術(shù)和軟件完成探索性和更高級的回歸分析，幫助學(xué)生將技巧應(yīng)用到解決實際問題中，直接體驗數(shù)據(jù)科學(xué)統(tǒng)計技巧對生物醫(yī)學(xué)領(lǐng)域的潛在和重要影響。

項目介紹：本項目將深入探討R語言在公共衛(wèi)生領(lǐng)域的廣泛應(yīng)用，為學(xué)生提供全面的統(tǒng)計學(xué)知識和實踐技能。學(xué)生將學(xué)習(xí)如何使用R語言進行數(shù)據(jù)處理、分析和可視化，強調(diào)在公共衛(wèi)生研究中的具體應(yīng)用。項目內(nèi)容包括R語言的基礎(chǔ)語法和數(shù)據(jù)結(jié)構(gòu)，以及如何運用R進行常見的統(tǒng)計方法，如回歸分析、方差分析、生存分析等，使用R語言包包括tidyverse、dplyr、ggplot等。通過理論教學(xué)和實際案例，學(xué)生將掌握R語言的高級編程技巧，有效處理衛(wèi)生領(lǐng)域的大型數(shù)據(jù)集。特別強調(diào)課程將關(guān)注R語言在流行病學(xué)研究、健康數(shù)據(jù)分析、臨床試驗設(shè)計等方面的應(yīng)用。學(xué)生將通過實際項目和案例研究，培養(yǎng)對真實衛(wèi)生數(shù)據(jù)的處理和解釋能力，從而更好地理解和應(yīng)用統(tǒng)計學(xué)方法。無論是對于初學(xué)者還是有一定統(tǒng)計學(xué)基礎(chǔ)的學(xué)生，本課程都將為其提供一個全面的R語言統(tǒng)計學(xué)培訓(xùn)，使他們能夠在未來的公共衛(wèi)生研究和實踐中靈活應(yīng)用統(tǒng)計學(xué)方法。在項目結(jié)束時，提交項目報告，進行成果展示。This program will explore in depth the wide application of R in the field of public health, providing students with comprehensive statistical knowledge and practical skills. Students will learn how to use R for data processing, analysis, and visualization, emphasizing specific applications in public health research. The content of the project includes the basic syntax and data structure of R language, and how to use R to carry out common statistical methods, such as regression analysis, analysis of variance, survival analysis, etc., the use of the R language package including tidyverse, dplyr, ggplot, etc. Through theoretical instruction and practical cases, students will acquire advanced programming skills in the R language to effectively handle large data sets in the health field. In particular, the course will focus on the application of R language in epidemiological research, health data analysis, clinical trial design, etc. Students will develop the ability to process and interpret real health data through practical projects and case studies to better understand and apply statistical methods. For both beginners and students with a background in statistics, this course will provide them with comprehensive R language statistics training that will enable them to flexibly apply statistical methods in future public health research and practice. At the end of the project, submit the project report and present the results.

個性化研究課題參考：傳染病預(yù)測預(yù)警；生物統(tǒng)計模型在PAHs致人群健康損害危險度評價中的應(yīng)用研究；生物統(tǒng)計學(xué)在降血糖新藥療效評估中的應(yīng)用
Suggested Future Research Fields: Infectious disease prediction and warning；Research on the application of the biostatistics model in the evaluation of the risk of population health damage caused by PAHs；Application of Biostatistics in Evaluating the Efficacy of New Drugs for Lowering Blood Sugar

項目大綱：統(tǒng)計數(shù)據(jù)科學(xué)概論、數(shù)據(jù)科學(xué)對公共衛(wèi)生與生物醫(yī)學(xué)的應(yīng)用；R語言介紹、RStudio和tidyverse的介紹 Introduction to statistical data science; applications in public health and biomedicine. Introduction to R, RStudio, and the tidyverse. R語言真實數(shù)據(jù)實操演示 Further practice with R and RStudio, illustration using example real-life data. 數(shù)據(jù)的讀取和操作、R語言之dplyr、R語言之ggplot數(shù)據(jù)圖形化和探索性分析、預(yù)測模型-線性回歸 Reading data, data manipulation with dplyr, exploratory data analysis with ggplot2 數(shù)據(jù)操作實踐，圖形化數(shù)據(jù)摘要案例學(xué)習(xí)Practice with data manipulation, further examples of graphical data summaries 線性回歸、邏輯回歸、機器學(xué)習(xí)導(dǎo)論Linear regression modeling, logistic regression modeling, introduction to machine learning 回歸分析數(shù)據(jù)案例學(xué)習(xí)，以診斷圖為例 Practical application of regression techniques to real-life data examples, some diagnostic plots. 監(jiān)督和無監(jiān)督學(xué)習(xí)算法，決策樹算法，聚類算法 Supervised and unsupervised learning algorithms, tree-based methods, clustering, and other approaches. Validation of methods 使用樣本數(shù)據(jù)進行算法實踐 Application of the algorithms discussed in lecture to sample data, illustration of validation analysis. 項目回顧與成果展示 Program Review and Presentation 論文輔導(dǎo) Project Deliverables Tutoring