Learn Python from a University Professor

We’ll use this first lesson to introduce our live programming environment. First, I’ll show what it looks like to see a Python program executing, and how you, as a user of your own program, can interact with it. Then, I’ll introduce our interactive Python code editor, available here:

https://computersciencecamp.com/courses/python/tools

You'll be able to use the code editor to write and run your first program – one that simply prints a greeting message on the screen. After signing up on Computer Science Camp, you'll be able to create new Python projects and test your programs, as instructed on the lesson videos.

In this lesson we’ll introduce the concept of a “variable” as the a basic tool to save data in our program memory. We’ll then show how to leverage variables as a temporary storage for sequences of simple arithmetic computations. And we’ll conclude this lesson with a simple strategy to document our code through Python comments.

In this lesson, we cover basic user interaction in Python. Our hands-on work consists in creating a couple of programs that ask the user for an input through the keyboard. We then convert this input into different data types for further processing. The lesson concludes with some remarks about Python runtime errors, also known as exceptions.

This lesson covers three types of built-in Python operators in detail: arithmetic, relational, and logical operators. We’ll see how to use these operators to increase the complexity and power of our computations, and you’ll practice their use by designing a simple interactive calculator.

In this lesson we study how Python extends its built-in functionality with external modules, and how your program can access these modules with special "import" statements. Our hands-on work includes two problems that focus on Python's “math” module.

So far, all of our Python programs have executed statements sequentially. In a program that has 3 lines of code, we expect line 1 to run first, then line 2, then line 3. While this execution model has already allowed us to introduce some level of interaction with the user, the real power of a programming language comes when you can alter the sequential control flow of your program by deciding what lines of code to execute, not to execute, or to execute multiple times based on certain conditions. In this lesson we introduce conditional execution as the most basic form of non-sequential control flow in Python.

In the previous lesson we’ve introduced three variations of the ‘if’ statement as a simple mechanism to control what code to execute based on conditions given as Boolean expressions. In this lesson, we’ll practice conditional execution with some interactive exercises. First, I’ll show you two examples for which you and I will be writing code together, and then I’ll propose a third problem that I’ll ask you to complete before I present my proposed solution.

In this lesson, I'll discuss two methods of indenting our code when using control flow, based on tabs and spaces. I'll present the advantages and disadvantages of each approach, and the built-in support for tab-based indentation in our code editor.

In this lesson, I’ll introduce the ‘while’ loop as the most basic type of repetition control flow structure in Python. I’ll also use this lesson to define the concept of an “algorithm”, which will stay with us throughout the course, together with some preliminary algorithmic design guidelines using flowcharts.

In this lesson I’ll introduce two Python keywords, called ‘break’ and ‘continue’, that allow us to alter the natural behavior of a loop, offering additional flexibility to control the repeated sequences of statements in our code. I’ll say a word or two about how to avoid getting stuck in infinite loops. And as always, we’ll conclude with a practice problem for you to internalize this new material.

In this lesson, I’ll provide a brief introduction to Python lists as a feature that allows us to group multiple values into one variable, and I’ll focus on a control flow structure called a ‘for’ loop, that allows us to conveniently traverse these sequences.

In the present chapter of this course will cover custom functions in detail: what is their main purpose, why are they necessary as the complexity of programs increases, how can we manage errors across functions, and more. In this lesson we’ll focus on the basics: how you can define your own functions and invoke them later from your main program.

In this lesson we’ll increase our ability to communicate data between custom functions and a main program through the use of what we call “fruitful functions”. As a practice problem, we’ll work on an algorithm that translates numbers from decimal to binary (or base 2), as we leverage a fruitful function to pack this functionality. I’ll conclude with some remarks about the lifetime of variables within and across functions – what we call the “scope” of a variable.

In this lesson I’ll introduce three new syntactic features related with function definitions and invocations that provide additional flexibility when dealing with argument passing. These features are called ‘default arguments’, ‘keyword arguments’, and ‘arbitrary arguments’.

Recursion is a method of solving a problem where its solution depends on solutions to smaller instances of the same problem. In Python we can write our own recursive functions, characterized by the fact that they invoke themselves within their own code. In this lesson, I’ll introduce this interesting idea bit by bit, by revisiting a familiar numeric algorithm —the calculation of the factorial of a number—, and I’ll show a detailed analysis of a recursive function that solves it.

In this lesson, I’ll continue discussing recursion through another numeric algorithm – the calculation of Fibonacci numbers. In the second part of the lesson, it’ll be time for you to get to work and design your own recursive function: one that identifies symmetric strings, also known as ‘palindromes’.

In this lesson we’ll talk about a specific kind of Python errors called ‘exceptions’. We’ll review some known situations in which such exceptions can occur. Then we’ll learn how to prevent exceptions from terminating a program, as we intercept them and handle them more gracefully. We’ll see how we can even raise our own custom exceptions. And finally, I’ll propose a problem where you’ll practice all these new concepts.

In this lesson, I'll present some introductory concepts for this chapter related with data types, with a focus on container data types. I'll present a formal framework to assess the computational cost of operations on containers, and I'll show two syntactic features used to manage them, based on operators and functions.

In this lesson, we’ll begin our deep dive into container data types by discussing Python lists. We’ll talk about the basic operations that allow us to declare, access, and manage lists, and I’ll propose an introductory problem for you to get your first hands-on experience with a list.

In this lesson, we will expand our knowledge on Python lists by introducing a more advanced set of operators, built-in functions, and methods used to manage these structures. With these additional tools you will be able to design more advanced algorithms that rely on the use of lists.

In this lesson, we’ll delve into some details about how lists are internally represented, which will provide necessary insights on how to use lists as function arguments and return values. In the second part of this lesson, we’ll start with the first of a series of practice problems, where you’ll need to apply all concepts related with lists studied so far.

In this two-part lesson, you'll be working on two different problems related with lists, where you'll have the chance to practice the concepts studied in previous lessons.

In this lesson you'll be working on two additional problems related with lists, with a level of completely that is slightly higher than the problems you've encountered so far.

In this lesson I’ll introduce a container data type, similar to a list, called a ‘tuple’. We’ll look at the syntax used to define and access tuples, the contexts in which you’ll want to use them, and the operators and functions related with them.

In this lesson, I'll propose a problem for you to practice the use of tuples as a way of returning multiple values in a function.

In this lesson, I'll propose a problem where you'll need to process a data set organized as a list of tuples by sorting it in ascending order.

In this lesson, I’ll introduce Python dictionaries as powerful, widely used container data structures. We’ll learn how to create, modify, and query dictionaries, and I’ll discuss the uniquely efficient cost of these operations.

In this lesson, I’ll propose an introductory problem to practice the creation of a dictionary followed by runtime insertions and updates.

In this lesson I’ll expand our ability to manage dictionaries by presenting additional operators, built-in functions, and methods available for them.

As I was describing the operations supported by dictionaries, I've emphasized the ability of searches, insertions, and deletions to operate with *constant* cost. This means that, whether a dictionary has ten, one thousand, or one million key-value pairs in it, searching for one will take the same amount of time. In this lesson I'll describe the magic used by Python under the covers in order to accomplish this type of efficient behavior in dictionaries.

Strings occur frequently in programs, and Python provides a variety of tools to process them. Processing a string may involve converting it to uppercase, finding occurrences of a sub-string in it, or formatting it before printing it into the terminal, just to name a few examples. This chapter will be dedicated to more advanced Python features to perform these tasks and more.

Converting any data type into a string is useful when presenting that information to the user on the console through ‘print’ statements, and here we face the opportunity to format those strings in the most user-friendly way possible. In this lesson, we’ll study the tools provided by Python to conveniently format strings with the desired alignment, padding, and other layout preferences.

In this lesson I'll present a list of frequently used operators and functions related with strings, which will complement slicing, formatting, concatenation, and other features studied before.