This course teaches you how to work with text data, list comprehensions, anonymous (Lambda) functions, and other Python tools that will be your indispensable stepping stones to becoming an advanced Python user.
This course is short yet intense. We assume you’re no stranger to Jupyter and Python fundamentals, such as conditionals, functions, sequences, and iterations. Nevertheless, a lack of experience working with text data, list comprehensions, and anonymous (Lambda) functions would immediately reveal to your data science colleagues that coding in Python hasn’t become second nature. In the following sections, we explain these intermediate programming tools in detail and provide an extensive set of exercises to ensure your ability to comply with today’s best practices in the field.
This course allows you to dig deeper than Python fundamentals. It focuses on intermediate tools that prove effective and efficient when processing large amounts of text and quantitative data.
What are intermediate Python tools, and why do we need to learn and practice them?
Whether you use Python for general programming or analytics, you’ll not only manage quantitative data; you’ll also need to clean and preprocess vast amounts of text data. This section covers various tools—such as argument specifiers and string accessors—to help you manipulate text data. We’ll also work with widely-applicable Python string methods, such as .split(), .strip(), and .format().
Once accustomed to manipulating text data, doors will open for you to employ several tools that will optimize the use of iterations and functions to the highest level. You’ll learn and apply iteration over range objects, nested for loops, list comprehensions, and anonymous (Lambda) functions.
Student feedback
“If you don’t want to be labeled as a Python beginner or newcomer by your colleagues, you mustn’t rely on fundamentals to achieve impressive analytic or programming results. This course is devoted to teaching efficient tools for working with text data, lists, and functions that are more sophisticated to learn but yet provide limitless application.”
Worked at the European Commission
Intermediate Python Programming
with Martin Ganchev
