Master real-world data science challenges and learn to manage data analysis teams effectively. Perfect for executives and managers.
Master real-world data science challenges and learn to manage data analysis teams effectively. Perfect for executives and managers.
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.
4.5
(2,357 ratings)
51,982 already enrolled
Instructors:
English
پښتو, বাংলা, اردو, 3 more
What you'll learn
Identify strengths and weaknesses in experimental designs
Learn novel solutions for managing data pulls
Describe common pitfalls in communicating data analyses
Understand a typical day in the life of a data analysis manager
Evaluate statistical modeling assumptions effectively
Navigate real-world data science challenges
Skills you'll gain
This course includes:
2.65 Hours PreRecorded video
6 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
Created by
Provided by

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 practical aspects of managing data science projects and teams. It contrasts ideal scenarios with real-world challenges in data analysis, covering experimental design, data quality management, statistical modeling, and communication strategies. The curriculum is designed for executives and managers, emphasizing conceptual understanding over technical details, and includes topics like A/B testing, causal inference, and the differences between machine learning and classical statistical approaches.
Introduction, the perfect data science experience
Module 1 · 7 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.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.