Course Summary

Welcome to Let's Learn Python for Data Science! We extend a warm welcome, especially if this is your first encounter with Python, data science, or programming in general.

In this course, we will embark on a journey through Python, assuming no prior programming experience. Each topic is designed to be beginner-friendly, allowing learners from all backgrounds to comfortably grasp the concepts.

While data science heavily relies on statistics, we will prioritize simplicity by keeping the statistics and math to a minimum throughout this course.

Python stands out as one of the most popular programming languages for data science due to its user-friendly nature and its powerful abilities in tackling complex challenges in big data analysis. The adoption of Python is further amplified by the comprehensive data science packages Pandas and Seaborn as well as machine learning packages Scikit-learn, Tensorflow, and PyTorch, which offers extensive functionalities. In addition, Python is increasingly being integrated into academic curricula and embraced across various industries.

This course can help you make better informed decisions by giving you a toolset for data summarization, cleaning, and visualization.

Digi Cafe courses are built around the following three pillars:

• User-friendly textbook: Courses are designed as user-friendly as possible, and are text-based so it is easier for a student to find and review material.
• Interactive code: Code editors that can be run in the browser are spread throughout lessons to get hands-on experience learning the programming language.
• Community: If you have any questions or just want to engage in discussion on the course material, you may join the Digi Cafe Discord community where we have a chat room for each programming language.

Learning Goals

Upon completing this course, you will have acquired the following knowledge and skills:

• What is data: We will start with exploring the world of data science and learn about the dataframe, a data structure that organizes data in an intuitive and easy to work with way.
• What is programming: We will take a brief detour into the history of programming to gain an understanding of how a how a computer interprets code as well as how it's managed.
• Assignment and classes: We will finish the introduction section by learning some fundamental programming concepts such as assignment and classes.
• Pandas and Seaborn: We will learn the Pandas and Seaborn packages, which are Python's most popular packages for data manipulation and visualization respectively.
• What is Pandas: In Pandas we will learn how to chain methods, summarise data, create new data from existing data, work with grouped data, and transform its shape.
• Seaborn: In this visualization package we will learn more on how to make visualizations such as scatter plots, line plots, bar plots, and add colour to them.
• Loops and conditional statements: These two essential programming techniques are crucial for controlling program flow and automating tasks effectively, where loops enable code repetition and conditional statements allow for selective code execution based on conditions.
• A final project: We will conclude with a final project which utilizes everything we have learned in the course.

In this course, our primary focus will be on learning Python in the context of data science. Instead of simply memorizing individual commands and functions, we will adopt a comprehensive approach. Each lesson will build upon the previous ones, systematically breaking down Python concepts to ensure a thorough understanding. So that you can gain first hand experience with Python, many of the lessons in this course include interactive code blocks with Python code that can be run directly on the webpage.

By the end of the course, you will possess a strong foundation in both Python programming and data science. This knowledge will serve as a solid base for further exploration and continued learning in these fields.