Summer Institute for Training in Biostatistics and Data Science at Columbia (SIBDS@Columbia)

总结/摘要 我们建议建立一个创新的夏季培训计划，介绍基本原则， 生物统计学和数据科学的有前途的本科生和开始研究生的多元化池， 作为定量科学家，专注于生物医学研究。这个为期七周的计划， 夏季研究所在生物统计学和数据科学培训在哥伦比亚（SIBDS@哥伦比亚），将是 由世界知名的教师与专业知识和广泛的资助研究在几个关键领域的池授课， 生物统计学和数据科学，来自不同背景，包括女性、非洲人、拉丁裔和第一批人 在他们的家庭获得大学教育，与学生的动手指导和多样性的热情， 劳动力学员将有机会与世界一流的专家在相关领域的互动， NHLBI和NIAID的使命，并将通过精心设计的项目，利用 从涉及心脏，肺，血液和睡眠的研究数据的程序获得的定量技能 疾病和传染病流行病学。该计划将被安置在生物统计学系 哥伦比亚大学在培养有前途的本科生方面有着悠久而成功的历史 通过其长期运行的BEST（生物统计学和流行病学暑期培训）， 多样性计划和哥伦比亚SIBS（CSIBS）计划的过去周期。根据拟议的培训方案， 我们将创建一个管道补充（和协同）现有的最好的计划，重点是国家， 最先进的数据科学技能，而不是最好的计划的重点是生物统计学和 流行病学还将有新的创新，以证明生物统计学和数据科学的价值， 跨学科的研究，也是一个新增加的重点研究传染病。我们特别 建议（一）确定和招聘一个多样化的和数量熟练的14个本科生和开始 每年夏天毕业的大学生;（ii）让受训人员沉浸在精心设计的课程中， 生物统计学和数据科学的基本概念，计算技能和动手生物医学数据分析; (iii)指导学员在生物统计学和数据科学研究生学习方面的专业发展， （iv）通过一个结构良好的学位追踪系统，确保学员取得成功 完成，追求研究生课程，以及随后的定量科学家职业生涯。鉴于哥伦比亚的 作为主要研究中心的声誉、NIH资助研究的广泛投资组合、专家教师和导师， 现有的BEST/CSIBS周期成功校友网络，作为学员的榜样， 我们位于种族多元化和文化丰富的纽约市，我们准备继续做出贡献 大幅扩大训练有素的生物统计学家和数据科学家的多样化人才库， 解决当今最紧迫的研究问题所需的复杂数据分析。

SUMMARY/ABSTRACT We propose to establish an innovative summer training program to introduce fundamental principles of biostatistics and data science to a diverse pool of promising undergraduate and beginning graduate students for careers as quantitative scientists with a focus on biomedical research. The proposed seven-week program, Summer Institute for Training in Biostatistics and Data Science at Columbia (SIBDS@Columbia), will be taught by a pool of world-renowned faculty with expertise and extensive funded research in several key areas of biostatistics and data science, hailing from diverse backgrounds and including women, African, Latino, and first in their family to attain a college education, with passion for hands-on mentoring of students and diversity in the workforce. Trainees will have the opportunity to interact with world-class experts in domain areas relevant to the missions of NHLBI and NIAID and will be immersed in research through carefully designed projects utilizing quantitative skills acquired from the program on data from studies involving heart, lung, blood, and sleep disorders and infectious disease epidemiology. The program will be housed within the Department of Biostatistics at Columbia University, which has a long and successful history of training promising undergraduate students from diverse backgrounds through its long-running BEST (Biostatistics and Epidemiology Summer Training) Diversity Program and past cycles of the Columbia SIBS (CSIBS) program. Under the proposed training program, we will create a pipeline complimentary to (and synergistic with) the existing BEST program by focusing on state- of-the-art data science skills, as opposed to the BEST program’s focus on the intersection of biostatistics and epidemiology. There will also be new innovations to demonstrate the value of biostatistics and data science in interdisciplinary research and also a new added focus on research of infectious diseases. Specifically, we propose to (i) identify and recruit a diverse and quantitatively skilled group of 14 undergraduate and beginning graduate college students every summer; (ii) immerse trainee cohorts in a carefully designed curriculum of fundamental concepts of biostatistics and data science, computing skills, and hands-on biomedical data analysis; (iii) mentor trainees on professional development toward graduate studies in biostatistics and data science and subsequent careers; and (iv) ensure success of trainees by a well-structured tracking system through degree completion, pursuit of graduate studies, and subsequent careers as quantitative scientists. Given Columbia’s reputation as a major research hub, its extensive portfolio in NIH-funded research, its expert faculty and mentors, an existing network of successful alumni from previous BEST/CSIBS cycles as models for trainees, and its location in ethnically diverse and culturally rich New York City, we are poised to continue to contribute substantially toward expanding a diverse pool of well-trained biostatisticians and data scientists that can handle the complex data analyses needed to address today’s most pressing research questions.