98735 - PYTHON CODING AND DATA SCIENCE

Learning outcomes

The focus of the course is on Phython coding & Data Science which have gained great popularity in the last few years, especially in the field of financial applications. Students will acquire a good knowledge of Coding with a special focus to financial application and frontier topics of green finance.

Course contents

Basic Python

1. Using the Python Interpreter
2. An Informal Introduction to Python
   
3. Control Flow Tools
   
4. Data Structures
5. Modules
6. Input and Output
7. Errors and Exceptions
8. Classes
9. Brief Tour of the Standard Library
10. Virtual Environments and Packages
    
    

NumPy

1. Array objects
   The N-dimensional array (ndarray)
   Scalars
   Data type objects (dtype)
   Indexing routines
   Iterating Over Arrays
   Standard array subclasses
   Masked arrays
   The array interface protocol
   Datetimes and Timedeltas
   

1. Constants
2. Routines
   Array creation routines
   Array manipulation routines
   Binary operations
   String operations
   Mathematical functions with automatic domain
   Floating point error handling
   Functional programming
   NumPy-specific help functions
   Input and output
   Linear algebra (numpy.linalg)
   Logic functions
   Masked array operations
   Mathematical functions
   Miscellaneous routines
   Random sampling (numpy.random)
   Set routines
   Sorting, searching, and counting
   Statistics

SciPy

          Introduction
          Special functions (scipy.special)
          Integration (scipy.integrate)
          Optimization (scipy.optimize)
          Interpolation (scipy.interpolate)
          Linear Algebra (scipy.linalg)
          Statistics (scipy.stats)


pandas

1. basics
   Object creation
   Viewing data
   Selection
   Missing data
   Operations
   Merge
   Grouping
   Reshaping
   Time series
   Categoricals
   Plotting
   Getting data in/out
   Gotchas
2. Intro to data structures
3. Essential basic functionality
4. IO tools (text, CSV, HDF5, …)
   
5. Indexing and selecting data
   
6. MultiIndex / advanced indexing
   
7. Merge, join, concatenate and compare
   
8. Reshaping and pivot tables
   
9. Working with text data
   
10. Working with missing data
    
11. Categorical data
    
12. Computational tools
    
13. Group by: split-apply-combine

Elements of matplotlib

Readings/Bibliography

https://docs.python.org/3/tutorial/

https://numpy.org/doc/stable/reference/index.html

https://docs.scipy.org/doc/scipy/tutorial/index.html

https://pandas.pydata.org/docs/getting_started/tutorials.html

https://matplotlib.org/stable/tutorials/introductory/index.html

Teaching methods

Teaching will be in blended mode. Some lecture in presence some online.

Some lecture will be just the teacher talking, explaining a subject but must will be a mixture of teaching and exercises

Assessment methods

Through the course there will be from 6 to 10 home assignment that students are expected to do and return to the tutor for grading.

Home assigned are tailored to occupy the student from 1 to 2 hours.

 

Final exam will be a written exam consisting in a challenging programming task.

Th final grade will be weighted equal between home assignment and the final exam.

Writing a computer program entails, among other three aspects I want to emphasize: correctness, clean programming style and performance. Correctness will absorb 60% of the value, coding style will weight 30%, the remaining 10% is taken up by performance

Teaching tools

Most will be frontal lectures were the teacher explains concepts and discusses examples.

Python code will be showcased both as standalone scripts, developed within  an IDE but most of all examples will be presented via the Notebook.

The most important tool will be the large number of exercises that will be proposed and will challenge the student.

Office hours

See the website of Pietro Rossi