You belong in this course and in the field of data science!

We are excited to learn with each and every one of you. We are here to support your success. We have no doubt that you will do great things with the data science skills you learn in this course because of who you are as a person and the values you bring with you from your culture, family, and life experiences. We want to invite you to bring your whole self into our data science learning community. Each of you brings cultural assets and personal perspectives that will allow you to make unique contributions in science and make the world a better place. We encourage you to bring your personal perspectives and values to all you do in this course!

This book is adapted from Yaniv Brandvain’s Biostatistics course. We are deeply grateful to Yaniv for allowing us to adapt his book for our course. Please go to Yaniv’s book for extra biostatistics material

Course overview

You will be empowered to produce, present, and critically evaluate statistical evidence as applied to biological topics. You will think about how a biological question can be formulated as a statistical question, present graphs that show how data speak to this question, be aware of shortcomings of that model, and how statistical analysis of a dataset can be brought back into our biological discussion.

You will learn to work with the software R (no worries, we start from the very beginning!) so that you can analyze datasets. You will work collaboratively in teams to analyze data from SFSU labs.

Learning objectives

  • Learn how to think statistically. Students will recognize that data are observations that reflect chance sampling and be able to incorporate this idea of chance into our interpretation of observations.

  • Recognize how bias can influence our results. Not only are results influenced by chance, but factors outside of our focus can also drive results.

  • Gain familiarity with foundational statistical values and concepts. Students will understand the meaning of statistical words such as variance and p-value, read and interpret graphs, and translate statistical models into sentences.

  • Conduct the entire process of data analysis in R. Students will use the statistical language, R, to summarize, analyze, and combine real-world data from SFSU Biology Faculty to make appropriate visualizations and to conduct appropriate statistical tests.