This 15-video course lets learners explore the basics of how to use the Python programming language. You will learn to set up with an interactive environment that allows you to develop and run Python scripts on your machine. Begin by installing Anaconda, an open-source distribution of the Python and R programming languages. You will learn to write your first meaningful program in Python, then create a Jupyter notebook, the most popular tool for writing and running Python code. You will learn how to do simple coding by using Python's Jupyter notebooks, and explore different Jupyter functionalities, including built-in functions. Learners will explore how to use a Python variable to store values, and learn to differentiate between variables of different types, and the different ways to assign values to variables. You will examine how variables act as containers, and you will learn how to change values that are inside a container. Finally, you will learn to use integers, floating-point numbers, strings, and to work with Boolean values.

Learn how to work with lists, tuples, and strings in Jupyter notebook in Python in this 14-video course. You will discover similarities and differences between tuples and lists and see how strings are essentially just a list of characters. Begin with an introduction to lists, and then create and initialize lists in Python. You will then access and update list elements; add, remove, sort, and reverse elements from a list; execute built-in functions with lists, and create new lists from existing lists by using slicing operations. Next, examine how to extract specific elements from the original list using step size; perform list functions on strings; invoke functions on the string object; and access substrings with slicing operations. Receive an introduction to tuples, exploring the similarities between lists and tuples, then move on to understanding tuple immutability by specifying differences between lists and tuples. Then an introduction to other complex data types and using dictionaries and sets in Python. The concluding exercise concerns recalling differences and similarities between lists and tuples.

This 9-video course helps learners explore dictionary data type in Python. Dictionaries are associative containers used to store key-value pairs. Given a key, finding the associated value is optimized by Python to be extremely efficient. First, receive an introduction to dictionaries in Jupyter Notebook in Python. You will learn how to create and initialize dictionaries, then learn about nesting complex data types within dictionaries. Continuing with the study of Python dictionaries, you will explore what functions and methods can be invoked on these dictionaries, such as modifying and updating dictionaries using dictionary methods. Next, you will be introduced to sets, another commonly used complex data type that Python supports. You will then create and initialize sets. This leads on to performing set operations such as union, intersection difference, and other set operations. You will also examine nested lists, and work with nested types within other complex data types. In the final tutorial, you will learn how to convert lists to dictionaries and vice versa. The concluding exercise entails recalling features of dictionaries and sets.

Explore copying operations on containers in Python in this 9-vdeo course, which examines the subtle distinction between shallow and deep copies. Changes made to shallow copies affect the original whereas with deep copies they do not. Learners begin by observing Jupyter notebook in Python, where you will be performing shallow and deep copies of Python strings. You will learn how to create shallow copies of lists, and then create deep copies of lists where changes to the copy do not affect the original. Following this, you will begin working with tuples, a process which you will discover is quite simple because tuples are immutable. So you will learn how to create shallow and deep copies of tuples. You will also learn how deep copies of dictionaries work, and perform shallow and deep copies of sets. In the closing exercise, learners are asked to recall how shallow and deep copies work for complex data types.

Learners will explore implementations of the order of precedence of operators, using if-elif-else statements to evaluate multiple conditions and conversions between various data types in Python, in this 15-video course. Key concepts covered here include how conditions in Python work, and how to evaluate conditions by involving primitive data types using if statements and complex data types using if statements. Next, evaluate multiple conditions for decision making with nested control structures; identify how to use the if-else statement to make decisions involving complex data types such as lists, tuples, and dictionaries; and learn how to convert an integer to a float and a float or an integer to a string, and vice-versa. Learners then observe how to convert primitive data types to complex data types, to convert between various complex data types, and to convert between various complex data types and view base conversions with Python built-in functions; and to solve various programming problems with Python built-in methods. Finally, you will learn to solve various programming problems by using if-elif-else statements and nested if-else statements.

Loops are one way to perform the same operations repeatedly in a program. For loops are the control structure to use when the repeated operations are performed on a sequence such as a list or a tuple. In this 9-video course, you will explore different ways to iterate over a sequence using for loops. Key concepts covered in this course include how to use for loops to process elements in a list and characters in a string; and how to code for loops to iterate over values in a tuple and the keys and values in a dictionary. Next, learn the function of associating an else block with a Python for loop; include if-else statements and other for loops within a for loop; how to generate a sequence of consecutive integers with the range function; and how to use the range function to iterate over a large range of values and apply it within nested for loops. Finally, observe how to write for loops in order to iterate over 1-dimensional and 2-dimensional sequences.

Explore how iterating over elements using for loops can be controlled using the break and continue statements in Python. Creating sequences from other sequences using comprehensions is also covered in this 9-video course. Key concepts covered here include how to terminate a for loop when a specific condition is met using the break statement; learning how the break statement affects the code in the else block of a for loop; and observing how to skip an iteration of a for loop when a specific condition is met using the continue statement. Next, learn how to use the continue statement along with the break statement within the same for loop; learn the fact that no action is performed under specific conditions by using the pass statement; and create a list out of the contents of another list using a comprehension. Finally, you will learn about conditions in list comprehensions in order to filter elements used in the source list and to define values in the newly created list.

While loops are one way to keep repeating a set of actions until a specific condition is met in Python. In this 11-video course, learners explore the use of while loops, considerations when implementing while loops, and use cases for while loops and for loops. Key concepts covered here include implementing a basic while loop and recognizing what conditions cause it to become an infinite loop; learning to use while loops to carry out actions while evaluating expressions based on numerical and string data; and examining while loops whose iterations depend on user input data. Next, learn syntax for defining while loops within a single line; learn to iterate over a list of elements with while loops; and learn to iterate over multiple lists and tuples with while loops. Learn when it is appropriate to use break keyword to stop a while loop, and learn to break out of a while loop and recognize use of the pass keyword within such loops. Finally, learn skip steps in individual iterations of a while loop using the continue statement.

Explore how Python facilitates code reuse by using functions in this 17-video course, which shows learners how to define functions, learn passing arguments to functions, and returning values from functions. The functions you will examine change the state of the program, may have side effects, and have observable effects other than their return values. Since functions with side effects are hard to parallelize and use in a distributed environment, you will learn correct ways of returning values from functions. First, you will learn how to invoke functions by using both positional and keyword arguments. You will next work with positional input arguments in custom functions, and learn that these are required arguments, and how to order these arguments to invoke your function. You will next learn to use variable length arguments in defining custom functions. Finally, you will learn how keyword arguments or named arguments are a way to make the intent behind function invocation absolutely explicit, and help prevent bugs in programs that are especially hard to detect.

This 13-video course offers learners an in-depth exploration of Python functions, by focusing on nuances such as argument passing by value and reference, and local and global variables. In this course, you will examine how functions are first-class citizens in Python, as with other data types. You will examine how Python allows functions to be stored in variables, passed into other functions as arguments, and returned from functions as return values. Next, you will learn how to identify and apply differences between parsing arguments by value and reference. Examine how Python treats functions on par with other data types, a key attribute of a program seeking to support the functional programming paradigm; and learn how to work with use and throw functions by using lam das. This course then covers how lightweight functions for one-off use called lambda functions or anonymous functions play an important role in keeping Python code both succinct and readable. Finally, you will learn how to appropriately choose between local and global variables for use in your program.

This course explores advanced Python function topics such as recursion, closures, and using generator functions to generate sequences. In 12 videos, you will learn how to use decorators to add functionality to code; examine how recursion can be used to construct code to solve complex problems; and learn to write a terminating condition for a recursive function. Next, you will learn how to use an Iterator to respond to a built-in next () function. Learners will also examine closures, and how as functions they maintain their own lexical environment; and explore how closures are functions that can yield dramatic results in the distributed processing of code, and are widely used in the implementation of distributed processing frameworks. Then you will learn how to use generator functions to generate sequences. You will learn how sequences can iterated upon by other parts of your program. Finally, you will learn that using decorators offers simple ways of invoking higher-order functions.

In this practice lab, learners will be presented with a series of exercises to practice developing in Python. Exercises include tasks such formatting data types, implementing flow control and conditionals, copying containers, and performing loops with list comprehension methods. Learners will also practice converting data types, working with global and local variables within functions, invoking functions with varying parameters and implementing recursive functions and closures.

Learners can also use the environment as an open sandbox. No installation or configuration is required, so you can gain immediate hands-on experience. Create new files or upload your own from a storage location of your choice, such as GitHub, and you can practice coding right away! You can even download a copy of your work when you’re done.

Whether you’re looking to dive into the code presented within our courses or you want to work on your own coding projects, this lab environment will provide you with everything you need. So, go ahead and start coding today!

Final Exam: Python Novice will test your knowledge and application of the topics presented throughout the Python Novice track of the Skillsoft Aspire Python Novice to Pythonista Journey.