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
Use AI coding assistants to generate Python code to perform basic data science tasks
Critically evaluate, test, and debug code generated by AI coding assistants
Use online tools for version control, collaborative software development, and project management
Read and write data files in common formats such as CSV and Excel
Organize and manipulate data structures such as lists, arrays, and data frames
Work with continuous, discrete, and factorial data, and understand distinctions between them
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/4D images
Understand basic concepts and common tools used in machine learning
Extend your skills using online resources
Soft Skills¶
Demonstrate a professional work ethic
Be effective and productive in a hybrid/remote working environment
Work collaboratively, effectively, and productively in a distributed team
Peer-review the work of other team members
Teach others skills and solutions you discover, and communicate your approach to discovering these
Articulate your strengths and weaknesses as a data scientist working in a team, and identify ways to improve your abilities
Demonstrate skills you have developed using a portfolio of work, to potential supervisors/employers
Use, and communicate the value of, open and reproducible code and data