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Exam Schedule

Computing with Data



Lecture Date Topic Material
1 Tue Jan 19 All About Data and Computing with Python
2 Thu Jan 21
3 Tue Jan 26 Approximations in Numerical Computing
4 Thu Jan 28
5 Tue Feb 02 Designing a numerical experiment
6 Thu Feb 04 Randomness and Simulation. Monte Carlo
7 Tue Feb 09 An approximation tool. Taylor Series
8 Thu Feb 11 Linear Algebra Meets Computation

Computing with arrays of data



Lecture Date Topic Material
9 Tue Feb 16 Norms
10 Thu Feb 18 Solving Systems
11 Tue Feb 23 Factorizations and Eliminations
12 Thu Feb 25 no class.
13 Tue Mar 01 Pivoting and Computational Expense and Accuracy
14 Thu Mar 03 An in-class project. Options Pricing with Monte Carlo
15 Tue Mar 08 Eigen Everything
16 Thu Mar 10 The Power Method and Ranking Things
17 Tue Mar 15 Singular Values and Compression
18 Thu Mar 17 Othogonality
19 Sat Mar 19 Spring Break (no class)
20 Mon Mar 21 Spring Break (no class)

Approximating data



Lecture Date Topic Material
21 Tue Mar 29 Your Graph is a Sparse Matrix
22 Thu Mar 31 Finding Least-Squares
23 Tue Apr 05 Interpolation
24 Thu Apr 07 no class
25 Tue Apr 12 Interpolation and Splines
26 Thu Apr 14 Nonlinear Equations
27 Tue Apr 19 Nonlinear Equations
28 Thu Apr 21 Optimization
29 Tue Apr 26 Optimization
30 Thu Apr 28 Optimization
31 Tue May 03 Final Exam Review (practice exam)

Office/Discussion Hours

Day Time Type (Location) People
Mon 2:00-3:00pm OH (4324 Siebel) Michael Heath
Tue 11:00-12:00am OH (2103 Siebel) Eric Shaffer
Wed 9:30-11:30am OH (0209 Siebel) Erin Carrier
OH (0209 Siebel) David Raju
Wed 4:00-6:00pm Discussion (1131 Siebel) Ryne Beeson
Thur 9:30-11:30am OH (0209 Siebel) Nathan Bowman
Thur 5:00-7:00pm Discussion (1131 Siebel) Karthik Ramaswamy

The lectures will have associated pre-lecture material: a collection of videos, slides, short write-ups, etc that will support the upcoming lecture.

Quiz Policy

As part of the pre-lecture material, there will often be associated quizzes. These quizes will be short and directly related to the latest material.

Importantly, the quizzes are designed to help you go through pre-lecture material! They are not a major component of the grade, but should be a helpful step toward understanding the course content.

Homework Policy

Homeworks will be released on weekly basis, being released on Fridays and due on Fridays at 4pm (leaving approximately one week to complete).

There are a few rules that are different from quizzes:

Exam Policy

There will be three exams: two midterm exams and a final. The final will be comprehensive but weighted toward the last section of material in the course.


If you have requests for special accomodations in the classroom, with the digital content, or for exams, please see Prof. Shaffer at


HW 25%
Exam1 20%
Exam2 20%
Final 25%
Quiz 10%

Detailed Distribution:

Academic Grading System in the US

Course Grade Total Score as weighted above
A+ [97,100)
A [93,97)
A- [90,93)
B+ [87,90)
B [83,87)
B- [80,83)
C+ [77,80)
C [73,77)
C- [70,73)
D+ [67,70)
D [63,67)
D- [55,63)
F [ 0,55)

Numerical Methods

Python Help

Numpy Help


We will be using Python with the libraries numpy, scipy and matplotlib for in-class work and assignments. No other languages are permitted. Python has a very gentle learning curve, so you should feel at home even if you've never done any work in Python.

Virtual Machine Image

While you are free to install Python and Numpy on your own computer to do homework, the course staff will only provide support for using the supplied virtual machine image.

Download Virtual Machine »