BE/Bi 103 a: Introduction to Data Analysis in the Biological Sciences ===================================================================== Modern biology is a quantitative science, and biological scientists need to be equipped with tools to analyze quantitative data. This course takes a hands-on approach to developing these tools. Together, we will analyze real data. We will learn how to organize, preserve, and share data sets, create informative interactive graphical displays of data, process images to extract actionable data, and perform basic resampling-based statistical inferences. Importantly, biological data is often "messy" and there is no one right way to perform an analysis or make a plot. As we work with data, we will discuss various approaches to get a feel for the art of biological data analysis. The sequel to this course goes deeper into statistical modeling, mostly from a Bayesian perspective. This course is foundational for that and further studies in analysis of biological data. If you are enrolled in the course, please read the :ref:`Course policies`. We will not go over them in detail in class, and it is your responsibility to understand them. Useful links ------------- - `Ed `_ (used for course communications) - `Canvas `_ (used for assignment submission/return) - `Homework solutions `_ (password protected) People ------ - Instructor + `Justin Bois `_ (`bois at caltech dot edu`) - TAs + Danny Collinson (``dccollin AT caltech DOT edu``) + Madison Dunitz (``mdunitz AT caltech DOT edu``) + Kayla Jackson (``kaylajac AT caltech DOT edu``) + Kevin Mei (``kmei AT caltech DOT edu``) + Anastasiya Oguienko (``oguienko AT caltech DOT edu``) + Arjuna Subramanian (``amsubram AT caltech DOT edu``) .. toctree:: :maxdepth: 1 :caption: Lessons lessons/00/index lessons/01/cycle_of_science lessons/02/index lessons/03/index lessons/04/style.ipynb lessons/05/index lessons/06/index lesson_exercises/exercise_01.ipynb lessons/07/index lessons/08/file_formats.ipynb lessons/09/data_storage_and_sharing lessons/10/index lesson_exercises/exercise_02.ipynb lessons/11/index lessons/12/random_number_generation.ipynb lessons/13/prob_dists.rst lesson_exercises/exercise_03.ipynb lessons/14/index lessons/15/index lesson_exercises/exercise_04.ipynb lessons/16/index lessons/17/hacker_nhst.ipynb lesson_exercises/exercise_05.ipynb lessons/18/index lessons/19/index lesson_exercises/exercise_06.ipynb lessons/20/index lessons/21/index lesson_exercises/exercise_07.ipynb lessons/22/variate_covariate_implementation.ipynb lesson_exercises/exercise_08.ipynb lessons/23/mixture_models.ipynb lessons/24/index lessons/25/index lesson_exercises/exercise_09.ipynb lessons/26/index lessons/27/reproducible_workflows.rst lessons/28/paper_of_the_future.rst .. toctree:: :maxdepth: 1 :caption: Recitations recitations/01/index recitations/02/index recitations/03/manipulating_dfs.ipynb recitations/04/probability_review.ipynb recitations/05/overplotting.ipynb recitations/06/index recitations/07/mle_review.rst recitations/08/topics_in_bootstrapping.ipynb .. toctree:: :maxdepth: 1 :caption: Homework homework/01/index homework/02/index homework/03/index homework/04/index homework/05/index homework/06/index homework/07/index homework/08/index homework/09/index 10. Course feedback .. toctree:: :maxdepth: 1 :caption: Schedule schedule .. toctree:: :maxdepth: 1 :caption: Policies policies Previous editions of the course ------------------------------- - `Fall 2022 `_ - `Fall 2021 `_ - `Fall 2020 `_ - `Fall 2019 `_