Learning Objectives for this Course

Learning Objectives for this Course#

The learning outcomes for this class can be characterized as a mix of “hard” and “soft” skills; both are equally valuable to your intellectual and personal development. At the end of this class, you should be able to:

Hard Skills#

  • Extract meaning from data, but also articulate the limitations of the conclusions you can draw from it

  • Write functional and efficient code in Python to perform basic data science tasks

  • Read and write data files in common formats such as CSV and Excel

  • Organize and manipulate data structures

  • Work with continuous, discrete, and factorial data

  • Visualize data in a variety of graphical formats

  • Perform exploratory data analysis using graphical and basic statistical operations

  • Perform basic signal processing on data, such as filtering in temporal and spatial dimensions

  • Build and run data processing pipelines on various types of neuroscientific data, including single unit recordings, time series, and 2D/3D images

  • Extend your Python skills using online resources

Soft Skills#

  • Demonstrate a professional work ethic by managing your time well, coming to class prepared, and engaging with your instructors and classmates

  • Peer-review the work of other classmates

  • Teach others skills and solutions you discover, and communicate your approach to discovering these

  • Articulate your strengths and weaknesses as a data scientist, and identify ways to improve your abilities

  • Use, and communicate the value of, open and reproducible code and data

This section was adapted from Aaron J. Newman’s Data Science for Psychology and Neuroscience - in Python.