Università Roma Tre

The course will cover the following aspects of Python programming:

• Introduction to Programming: Computer architectures; memory and data; CPU and programs; programming languages; problems, algorithms, and programs.
• Using the Python Interpreter: Invoking the interpreter; passing arguments; interactive mode; notebooks; online coding platforms.
• Basic Python Programming Concepts: Variables and assignments; expressions and statements; operations; printing; comments; debugging; data types; numbers and strings; input.
• Functions: Built-in functions; function calls; importing modules and functions; mathematical functions; function composition; defining new functions; parameters and arguments; required and optional arguments; argument order and keyword assignment; variable scope.
• Making Decisions: Boolean expressions and logical operators; conditional and alternative execution; if-elif-else structure; chained and nested conditionals.
• Iterations: Variable reassignment and updates; while loop; break statement; sequences and loops; the in operator; for loop.
• Data Structures (strings, lists, tuples, dictionaries): Definition, properties, operations, and methods; indexing vs. assignment; mutability and immutability; map, filter, and reduce; referencing and aliasing; packing and unpacking; search and reverse search; variable-length arguments.
• Files: Persistence; opening and closing files with the with statement; reading and writing; format operator; file names and paths; handling exceptions; pickling.
• Modules and Packages: Defining a module; defining a package; importing a package vs. importing a module vs. importing a function; installing packages.
• Classes and Objects: Classes, types, objects, and instances; instances as return values; attributes and methods; object mutability; instantiation and the __init__ method; operator overloading and special methods; static methods and class methods; inheritance.
• Pythonic Programming: Conditional expressions; EAFP (Easier to Ask for Forgiveness than Permission); list comprehensions; generator expressions; any and all operators; sets.
• Scientific Programming: Numpy, arrays, and broadcasting; Pandas, dataframes, and series; Scikit-Learn and an introduction to machine learning with Python; Matplotlib and data visualization in Python.

Allen B. Downey, “Think in Python" (2nd edition)”, Green Tea Press, 2015

MATLAB desktop, command window, workspace, current folder, command history, MATLAB help, windows and preferences. Workspace management, loading/saving variables from/on file. Array Editor, manual editing of variables. Script Editor, basic commands for opening/saving/modifying script files.
Mathematical expressions, numbers and format, variables, display format, variable assignment, mathematical functions as operands, arithmetic operators, mathematical functions as operators, ordering modifiers, conversion functions.
Vectors and bidimensional matrices, building vectors and matrices, loading vectors and matrices, functions for vector/matrix generation (zeros, ones, rand, randn, eye etc.), concatenation, transposition, vector length, matrix dimension, matrix arithmetical operations, element-by-element operations, matrix functions, element-by-element functions, accessing/changing/deleting entries or blocks of matrices.
Norm of vectors and matrices, operator “:”, aggregate functions, indexing of vectors and matrices, single/double index, vectorial index. Boolean variables, relational operators, logical operators, logical expressions on scalars, vectors and matrices, logical indexing.
Multidimensional numerical arrays, characters and strings, function “char”. Cell array, cell array indexing, cell access, access to the cell content, function “cell”. Structure, function “struct”, structure indexing, access to the structure fields.
Polynomials, evaluation of polynomials, sum/difference/product/division of polynomials, polynomial derivation, polynomial roots, polynomials from the roots. Complex numbers, imaginary unit, building complex numbers, Cartesian and polar representation of complex numbers. Numerical sequences and series.
Graphical objects, types and hierarchy, handles. Reading/writing object properties, finding property values, copying/deleting objects. “Figure” objects, “Axes” objects, “Line” objects.
Colours, RGB representation. 2D graphics: function "plot" and "subplot", drawing points and lines on axes, plotting mathematical functions, plotting complex numbers, drawing multiple lines with matrices, plotting 2D parametric curves, “hystogram” function, other useful functions for 2D plots.
Line style, colours, markers, figure saving. 3D graphics: functions “plot3”, “surf” and “mesh”, bidimensional grid generation with “meshgrid”, plotting 3D parametric curves. Examples of 2D and 3D graphics.
MATLAB programming, M-files, script and functions, input/output commands, flux control, loops. Types of functions, primary functions, auxiliary functions, nested functions, anonymous functions, function handles. Global variables, script/function interruption, program debugging and comments.
Functions of functions for solving mathematical problems: graphs of functions, searching for the zeros of a mathematical function, solution of non-linear algebraic systems, definite integral computation, scalar function minimization, multidimensional non-linear constrained/non-constrained optimization, integration of first order Cauchy problems.

Lesson slides.