Course Description

This introductory course offered by SDA_Bocconi, a part of their Professional Certificate in Data Science Program, provides learners with foundational knowledge in Python programming. Ideal for beginners, it covers a range of essential Python concepts, tools, and modules, preparing students to handle tasks and challenges in computing effectively.

What Students Will Learn

• Installation of Python and familiarization with its various front-end tools such as Spyder and Jupyter Notebook.
• Basics of writing, executing, and error handling in Python scripts.
• Understanding simple and complex Python data structures like integers, floats, strings, lists, and dictionaries.
• Using loops, conditional statements, and custom functions for automating repetitive tasks.
• Mastery of Python's vastly utilized pandas library to manage and manipulate data effectively in a format similar to Excel spreadsheets.

Course Prerequisites

No prerequisites are required for this course. It is designed for individuals starting from scratch, making it approachable for anyone with an interest in learning Python programming.

Course Outline

• Week 1: Introduction to Python installation and frontends like Spyder and Jupyter Notebook.
• Week 2: Fundamental concepts of algorithms and basic Python objects, error interpretation.
• Week 3: Exploration of complex Python objects and their associated methods.
• Week 4: Understanding and implementing conditional statements and loops.
• Week 5: Design and application of custom functions.
• Week 6: Introduction to pandas for data manipulation and operations.

Who is this Course for?

This course is ideally suited for beginners seeking to understand the basics of Python programming, data manipulation, and computational logic. It is particularly beneficial for those looking to enter fields related to software development, data analysis, and scientific research.

Application of Skills in the Real World

Skills acquired from this course can be primarily used for:

• Developing software applications using Python.
• Performing data analysis and visualization.
• Automating tasks to optimize business processes.
• Conducting academic or scientific research that requires computational support.