The course continues building programming fundamentals in Python programming and data analysis. You will learn how to make use of complex data structures (Dictionaries), iterate repeated tasks that scales naturally (For-Loops, List Comprehensions), and create your own functions (Functions, Anonymous Functions). You will apply these skills to develop a custom data analysis for analyzing text data.
The course is intended for researchers who want to continue learning the fundamentals of Python programming and how to write custom data analysis functions when dealing with messy datasets. The audience should know how to work with Lists and Pandas Dataframes and conduct basic data analysis, and/or have taken our Intro to Python course.
Understand and distinguish the use cases of common data structures, such as Lists, Dictionaries, and Series.
Recognize and describe common iteration patterns on common data structures, such as counting and searching.
Implement “iterable” operations, including For-Loops, List Comprehensions on data structures that can be iterated (Lists, Dictionaries, Strings).
Implement conditional statements when the program logic requires a branching structure.
Create simple, modular functions, including anonymous functions, that can be reused.
Describe the difference between copying an object vs. referencing an object and how that could affect variables in a data analysis.