Lawrence Cormack cormack@utexas.edu | Franco Pestilli pestilli@utexas.edu
Canvas: https://utexas.instructure.com/courses/1331982
Course Page: https://utdirect.utexas.edu/apps/registrar/course_schedule/20222/42035/
Course tutorials: https://github.com/psychdatascience/FDS-CourseOne
This course lays the foundation for data science education targeting psychological and brain science students. No previous coding experience is required. The students will be introduced to basic concepts and tools for data analysis. The focus is on hands-on practice and enjoyable learning. The course will use python as the programming language, and Jupyter Notebooks as the development environment (our “home base”) for the examples, tutorials, and assignments. We use Jupyterlab Notebooks because they are both the industry standard and a nice way to load, visualize, and analyze data as well as describe our findings in one environment. We will also learn GitHub to document changes and backup our work and, eventually, for use as a collaboration tool.
The course is flipped, meaning that the students will be required to follow short pre-recorded video lectures and or written tutorials, and then we’ll spend the class time actually writing code and playing with data. Hands-on data analysis, final projects and associated presentations will be mandatory for the completion of the course. The final outcome for the class is that each student will have a GitHub repository with all of their work (Jupyter notebooks, data, etc.), including a final project that will be presented to the class.
Specific topics to be covered include: GitHub, Jupyter Notebooks, Python Programming, Data Visualization, Simulation and Data analysis, Data Modelling, Ordinary Least-square regression and Generalized Linear Models.
Note: This is a brand new course for Spring 2022, so it will be a (fun) work in progress. Details of the course content might deviate from this syllabus as we go.
Course Prerequisites There are no prerequisites for the course.
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BYOL - Bring Your Own Laptop to class for hands on participation. The College or Department might have a loaner service for the duration of the course.
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Software installed on your laptop:
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Python installation via Anaconda: https://www.anaconda.com/products/individual.
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The git code management tool: https://git-scm.com/.
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Read Understand and Respect the class code of conduct (see below under POLICIES)
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Remember this class is about learning and having fun. We are here to provide you with the opportunity to learn something helpful for your future.
Classes will be held online or in-person depending on what is possible but your ‘presence’ will be required and your participation will be verified via submitted notebooks.
This class will comprise various learning activities. A. In-class coding exercises to walk through the coding and data analysis tools.
B. Papers, book chapters, videos, and notes will be required as reading in preparation for some classes.
C. Project-driven data analysis students will complete a series of reports to practice the coding and data concepts covered by the tutorials.