02-201/02-601 Programming for Scientists

1. The Ancient Greeks and fundamental mathematical algorithms
2. Monte Carlo simulation applied to a variety of topics, including random walks and simulating a presidential election
3. Cellular automata and Conway’s Game of Life
4. Evolutionary dynamics and game theory, with social science applications
5. Finding replication origins in bacterial genomes
6. Building evolutionary trees for viruses from genetic data
7. Using object-oriented programming to build a galaxy simulator
8. Introductory cryptography
9. Data visualization
10. Protein folding

Programming topics are listed below.

1. Imperative programming constructs: functions, if-statements, loops (for, while), switch- statements, expressions
2. Basic data structuring constructs: variables, arrays, strings, structs, types, and pointers 2
3. Reading and writing files
4. How to build large programs using top-down design
5. Basic execution and memory model (Von Neumann architecture)

1. Linear data structures: arrays, lists, stacks, queues; binary search 

2. Dictionary data structures: binary search trees including tree traversals (DFS, BFS, pre-, in-, post-order); hash tables. 

3. Divide and conquer, recursion 

4. Assertions, preconditions, postconditions
5. Code documentation
6. Unit tests — testing small sections of code
7. Debugging — strategies, common errors
8. Profiling — figuring out what’s taking so long 

9. Bigger units of code: Modules, namespaces, packages
10. Type interfaces and user-defined types
11. Object-oriented programming
12. Design patterns
13. Parallelism
14. Concurrency 

15. Turing machines
16. Halting problem and uncomputable functions
17. NP and NP-complete problems

Programming assignments (30% of grade)

Exams (40% of grade)

Project (15% of grade – 02-601 only)

Pre-class exercises (7.5% of grade)

Attendance and participation (7.5% of grade)

Our first goal for this course is to make you comfortable writing your own programs and have a better understanding of how computing works, at multiple levels.

Our second goal is to convince you how much fun programming is! Writing a program is like solving a sudoku puzzle — programming tests (and builds!) your powers of concentration and logical thinking. But programming is more rewarding than sudoku because it equips you with a transferable skill instead of the ability to fill in a square of numbers.

Our third goal is to help you understand some fundamental scientific (in particular, biological) algorithms on a high-level.

Finally, for students taking the graduate version of the course (02-601), we want you to gain independence to hack your own scientific problem by planning and executing a longer programming assignment of your own choice, so that you will be capable of programming independently in the future.