Building a Data Science Team
Learn essential strategies for recruiting, organizing, and managing successful data science teams in this executive-focused course.
Learn essential strategies for recruiting, organizing, and managing successful data science teams in this executive-focused course.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Executive Data Science Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Identify essential roles in a data science team
Develop effective hiring and interviewing strategies
Manage data science team onboarding processes
Implement successful team management practices
Foster cross-functional collaboration
Skills you'll gain
This course includes:
1.4 Hours PreRecorded video
6 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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Get a Completion Certificate
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Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There is 1 module in this course
This course focuses on the essential aspects of building and managing successful data science teams. Students learn about different roles within a data science team, including data scientists and engineers, how to recruit qualified candidates, and effective team management strategies. The curriculum covers team organization, onboarding processes, cross-functional collaboration, and common challenges in data science team management.
Building a Data Science Team
Module 1 · 5 Hours to complete
Fee Structure
Instructors
Chief Data Officer and J Orin Edson Foundation Chair at Fred Hutchinson Cancer Center
Dr. Jeff Leek serves as the Chief Data Officer, Vice President, and J Orin Edson Foundation Chair of Biostatistics in Public Health Sciences at the Fred Hutchinson Cancer Center. Previously, he was a professor of Biostatistics and Oncology at the Johns Hopkins Bloomberg School of Public Health and co-director of the Johns Hopkins Data Science Lab. He earned his PhD in Biostatistics from the University of Washington and is known for his significant contributions to genomic data analysis and statistical methods for personalized medicine. His research has advanced our understanding of molecular mechanisms related to brain development, stem cell self-renewal, and immune responses to trauma, with findings published in top scientific journals such as Nature and Proceedings of the National Academy of Sciences. Dr. Leek developed a highly acclaimed Data Analysis course for Biostatistics students at Johns Hopkins, which has consistently received teaching excellence awards. He is also recognized for his efforts in creating educational initiatives that leverage data science for public health and economic development, including massive open online courses that have engaged millions worldwide.
Professor of Biostatistics at Johns Hopkins University
Dr. Roger D. Peng is a Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health and serves as a Co-Editor of the Simply Statistics blog. He earned his PhD in Statistics from the University of California, Los Angeles, and is recognized for his research in air pollution, health risk assessment, and statistical methods for environmental data. In 2016, he received the Mortimer Spiegelman Award from the American Public Health Association, honoring his significant contributions to health statistics. Dr. Peng developed the Statistical Programming course at Johns Hopkins to equip students with essential computational tools for data analysis. Additionally, he is a national leader in promoting reproducible research practices and serves as the Reproducible Research editor for the journal Biostatistics. His interdisciplinary research has been published in prestigious journals, including the Journal of the American Medical Association and the Journal of the Royal Statistical Society. He has authored over a dozen software packages that implement statistical methods for environmental studies and reproducible research, and he regularly conducts workshops and tutorials on statistical computing and data analysis.
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