Basics of Python

Finalità
Purpose

As an instructor, my purpose in teaching the basics of Python through practical exercises is to help the students develop a strong understanding of the language and its fundamental concepts by allowing them to apply what they learn in a hands-on way. Through practical exercises, they can gain real-world experience in programming and problem-solving, building their confidence and ability to write code independently. Additionally, practical exercises can help them develop critical thinking skills as they work through challenges and develop creative solutions to problems. Ultimately, my goal is to equip the students with the foundational knowledge and skills they need to become proficient Python programmers.

Software utilizzato
Software used

Google Colab (https://colab.research.google.com/)

Programma del corso
Course program

1. Introduction to Python: installation, Python shell, variables, and data types
2. Control Flow: conditional statements and loops
3. Functions: defining and calling functions, parameters and arguments, return values
4. Data Structures: lists, tuples, and dictionaries
5. File Input and Output: reading and writing files
6. Exception Handling: handling errors and exceptions in Python
7. Modules and Packages: importing and using modules, creating and installing packages
8. Object-Oriented Programming: classes, objects, inheritance, and polymorphism
9. Descriptive Statistics: mean, median, mode, variance, standard deviation, and percentile calculations using Python libraries such as NumPy and Pandas
10. Data Visualization: creating charts, histograms, and scatter plots to visually represent data using Matplotlib and Seaborn libraries
11. Probability Distributions: understanding and working with probability distributions, such as the normal distribution and the binomial distribution, using Python libraries like SciPy
12. Hypothesis Testing: performing hypothesis tests, such as t-tests and ANOVA, to test the significance of results using Python libraries like StatsModels and Scikit-Learn.