Coursework Aim and Coding Environment:

· In this coursework you will develop Python code related to algorithms that solve polynomial interpola- tion problems, and you will study some of the algorithms’ behaviour.

· You should write (and submit) plain Python code (.py), and you are strongly encouraged to use the Spyder IDE (integrated development environment). Hence you should not write IPython Notebooks (.ipynb), and you should not use Jupyter).

How and Where to run Spyder:

· Spyder comes as part of the Anaconda package (recall that Jupyter is also part of Anaconda). Two

options on how to run Spyder are:

(1) You can choose to install Anacondaon you personal device (if not done already).

(2) You can open Anaconda on the computers in some UNNC computer labs.

Some Further Advice:

· You are expected to have basic familiarity with Python, in particular: logic and loops, functions, NumPy and matplotlib.pyplot. Note that it will always be assumed that the package numpy is imported as np, and matplotlib.pyplot as plt.

· Helpful resources: Python 3 Online Documentation – Spyder IDE (integrated development editor) – NumPy User Guide – Matplotlib Usage Guide – Analytical and Computational Foundations (MATH1028 UNNC) (FCH1 20-21) Moodle page,

· Write your code as neatly and readable as possible so that it is easy to follow. Add some comments to your code that indicate what a piece of code does. Frequently run your code to check for (and immediately resolve) mistakes and bugs.

· Your work will be mostly marked by running your functions and comparing their output against the correct results.