Date & Time
Tuesday, December 6, 12:00-1:50 p.m., PM 253
Overview
The final exam is comprehensive will cover all material from the
beginning of the semester through the end but with some emphasis on
material covered since Test 2. The final is more
comprehensive than Test 2 was. This material includes
everything discussed in lectures and covered in assignments, and aligns
with chapters 1-10 from the recommended text. We have covered some
additional topics (including data, visualization, and machine learning)
the text has not, and we did not specifically cover the data science
additions to each chapter although some overlap with the last weeks of
the course. Because this is a programming principles course, there will
be questions related to principles as well as questions that involve
syntax. You may be asked to write, analyze, and/or debug code.
Example Types of Questions
- Examples from Test 1
- Examples from Test 2
- What are the differences between a numpy array, a tuple, and a
list?
- Write a statement to refer to a specified subarray (indexing,
slicing).
- What is the difference between a view and a copy of a numpy
array?
- What differentiates a pandas series from a numpy array?
- Given a data frame, write an expression to select the age and height
columns from rows with names starting with “A”.
- Given two data frames, what operations might be used to transform
one into the other (i.e. melt, pivot, transpose).
- Given a data frame of houses for sale in different parts of the
United States, write code to find the maximum price of a house in each
zipcode.
- What is the purpose of data visualization?
- Given a dataset, how might you create a visualization that shows all
of the columns? Think about the different ways to encode data
attributes.
- What are the differences between matplotlib’s stateful and
object-based styles?
- How can multiple visualizations of the same dataset be useful?
- Why is interaction important in visualization? What are these
different types of interactions?
- Why do we have a testing and training set in machine learning?