Course contents, Grading and Class material

[Course Outline]  [Evaluation]  [In Class]





Course contents
  1. Course contents & References

Evaluation

Evaluation will be done based on:
35% - Lab. Assignments
20% - Midsem
35% - Endsem
10% - Project
In Class

The following 2 books contain most of the Python material and Book1 also has many parts of the statistics and hypothesis testing material.

Book1: John V Guttag, Introduction to Computation and Programming Using Python with Application to Understanding Data, 2nd Ed., MIT Press, 2016.

Book2: Allen Downey, Think Python: How to Think Like a Computer Scientist, 2nd Ed., Green Tea Press, 2015. Also available online.

For statistics, probability, hypothesis testing see:
D S Moore, G P McCabe, B A Craig, Introduction to the Practice of Statistics, 8th Ed., WH Freeman and Co., 2014.
  1. About course, Linux and command line
    Source:
    About course, linux and command line
  2. Pattern matching and regular expressions
    Source:
    Regular expressions
  3. Python
    Source:
    1. Python basics (Python code)
    2. Functions, recursion, scope (Python code)
    3. Modules, files (Python code)
    4. Classes (Python code)
    5. Decorators, property class (Python code)
    6. Iterators Exceptions (Python code)
    Other sources:
    Python resources
    Book1: chps 1-5, 7, 8.
    Book2: chps 1-19.
    4. Statistics, proabability, hypothesis testing
    Data, exploratory analysis:
    Data and visualization for exploration
    Statistics and probability
    Populations, samples, distributions, probability