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+  language: python
+  name: python3
+---
+
+# Acknowledgments
+
+We'd like to thank everyone that has contributed to the development of
+[*Data Science: A First Introduction*](https://datasciencebook.ca).
+This is an open source textbook that began as a collection of course readings
+for DSCI 100, a new introductory data science course
+at the University of British Columbia (UBC).
+Several faculty members in the UBC Department of Statistics
+were pivotal in shaping the direction of that course,
+and as such, contributed greatly to the broad structure and
+list of topics in this book. We would especially like to thank Matías
+Salibían-Barrera for his mentorship during the initial development and roll-out
+of both DSCI 100 and this book. His door was always open when
+we needed to chat about how to best introduce and teach data science to our first-year students.
+We would also like to thank Gabriela Cohen Freue for her DSCI 561 (Regression I) teaching materials
+from the UBC Master of Data Science program, as some of our linear regression figures were inspired from these.
+
+We would also like to thank all those who contributed to the process of
+publishing this book. In particular, we would like to thank all of our reviewers for their feedback and suggestions:
+Rohan Alexander, Isabella Ghement, Virgilio Gómez Rubio, Albert Kim, Adam Loy, Maria Prokofieva, Emily Riederer, and Greg Wilson.
+The book was improved substantially by their insights.
+We would like to give special thanks to Jim Zidek
+for his support and encouragement throughout the process, and to
+Roger Peng for graciously offering to write the Foreword.
+
+Finally, we owe a debt of gratitude to all of the students of DSCI 100 over the past
+few years. They provided invaluable feedback on the book and worksheets;
+they found bugs for us (and stood by very patiently in class while
+we frantically fixed those bugs); and they brought a level of enthusiasm to the class
+that sustained us during the hard work of creating a new course and writing a textbook.
+Our interactions with them taught us how to teach data science, and that learning
+is reflected in the content of this book.
+
+
+## Acknowledgments for the Python Edition
+
+We'd like to thank everyone that has contributed to the development of
+[*Data Science: A First Introduction with Python*](https://python.datasciencebook.ca).
+This is an open source Python translation of the original
+book, which focused on the R programming language. Both of these books are
+used to teach DSCI 100 at the University of British Columbia (UBC).
+We would like to give special thanks to Navya Dahiya and Gloria Ye
+for completing the first round of translation of the R material to Python,
+and to Philip Austin for his leadership and guidance throughout the translation process.
+We also gratefully acknowledge the UBC Open Educational Resources Fund, the UBC Department of Statistics,
+and the UBC Department of Earth, Ocean, and Atmospheric Sciences for supporting the translation of
+the original R textbook and exercises to the Python programming language.

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+  language: python
+  name: python3
+---
+
+# About the authors
+
+The original version of this textbook was developed by Tiffany Timbers, Trevor
+Campbell, and Melissa Lee for the R programming language. The content of the R
+textbook was adapted to Python by Trevor Campbell, Joel Ostblom, and Lindsey
+Heagy.
+
+**[Tiffany Timbers](https://www.tiffanytimbers.com/)** is an Associate Professor of Teaching in the Department of
+Statistics and Co-Director for the Master of Data Science program (Vancouver
+Option) at the University of British Columbia. In these roles she teaches and
+develops curriculum around the responsible application of Data Science to solve
+real-world problems. One of her favorite courses she teaches is a graduate
+course on collaborative software development, which focuses on teaching how to
+create R and Python packages using modern tools and workflows.
+
++++
+
+**[Trevor Campbell](https://trevorcampbell.me/)** is an Associate Professor in the Department of Statistics at
+the University of British Columbia. His research focuses on automated, scalable
+Bayesian inference algorithms, Bayesian nonparametrics, streaming data, and
+Bayesian theory. He was previously a postdoctoral associate advised by Tamara
+Broderick in the Computer Science and Artificial Intelligence Laboratory
+(CSAIL) and Institute for Data, Systems, and Society (IDSS) at MIT, a Ph.D.
+candidate under Jonathan How in the Laboratory for Information and Decision
+Systems (LIDS) at MIT, and before that he was in the Engineering Science
+program at the University of Toronto.
+
++++
+
+**[Melissa Lee](https://www.stat.ubc.ca/users/melissa-lee)** is an Assistant Professor of Teaching in the Department of
+Statistics at the University of British Columbia. She teaches and develops
+curriculum for undergraduate statistics and data science courses. Her work
+focuses on student-centered approaches to teaching, developing and assessing
+open educational resources, and promoting equity, diversity, and inclusion
+initiatives.
+
++++
+
+**[Joel Ostblom](https://joelostblom.com/)** is an Assistant Professor of Teaching in the Department of
+Statistics at the University of British Columbia.
+During his PhD, Joel developed a passion for data science and reproducibility
+through the development of quantitative image analysis pipelines for studying
+stem cell and developmental biology. He has since co-created or lead the
+development of several courses and workshops at the University of Toronto and
+is now an assistant professor of teaching in the statistics department at the
+University of British Columbia. Joel cares deeply about spreading data literacy
+and excitement over programmatic data analysis, which is reflected in his
+contributions to open source projects and data science learning resources.
+
++++
+
+**[Lindsey Heagy](https://lindseyjh.ca/)** is an Assistant Professor in the Department of Earth, Ocean, and Atmospheric
+Sciences and director of the Geophysical Inversion Facility at the University of British Columbia.
+Her research combines computational methods in numerical simulations, inversions, and machine
+learning to answer questions about the subsurface of the Earth. Primary applications include
+mineral exploration, carbon sequestration, groundwater and environmental studies. She
+completed her BSc at the University of Alberta, her PhD at the University of British Columbia,
+and held a Postdoctoral research position at the University of California Berkeley prior to
+starting her current position at UBC.
+