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Python 3 for Beginners: Programming & Coding Practice
3,955 students
Python 3 for Beginners: Programming & Coding Practice
Learn Python 3 control flow, collections and OOP, with coding practice and reading labs on data cleaning and functions.
Last updated 10/2026
English
English [Auto],
What you'll learn
- Use Python variables, conditions and loops to solve small programming tasks.
- Work with collections and explain how their contents change during execution.
- Describe classes and object-oriented relationships through introductory examples.
- Clean and count task labels using explicit rules in the reading practice lab.
- Explain why a mutable default argument can retain data between function calls.
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Coding Exercises
This course includes our updated coding exercises so you can practice your skills as you learn.
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Course content
10 sections • 67 lectures • 7h 34m total length
Requirements
- No previous Python qualification is required. Basic computer skills and a willingness to type and test examples are sufficient for the opening lessons.
- Use Python 3 and an editor or compatible execution environment. Additional setup may be needed if you choose to reproduce the later database examples.
- Be ready to inspect intermediate values and change sample inputs; the course's practical value comes from checking code as well as watching it.
Description
Start programming in Python 3 with lessons and small coding activities that make your reasoning visible. This course takes you from introductory program flow into variables, decisions, repetition and structured data, with additional object-oriented and SQL material for a broader foundation.
Follow the environment demonstrations, then explore the core Python concepts in sequence. Practise recognising which values a program stores, which condition controls a branch and how repeated work is organised. Later sections introduce object-oriented ideas, additional Python topics and database operations.
Turn a concept into a small result
Describe a task before deciding which statements to write.
Trace a loop and explain when it should stop.
Organise data and separate a calculation into reusable behaviour.
Compare an expected output with the result of a changed input.
The final reading activities use fictional task labels to practise cleaning and counting text. Decide how whitespace, repeated labels and empty input should be handled before implementing a summary. Another lesson examines why a list used as a default argument can retain information from an earlier call, giving you a concrete behaviour to investigate.
This course suits beginners and learners who want to revisit Python foundations through explanations and practice. Basic computer skills and a willingness to test your own examples will help you make progress. It is an introduction to programming, not comprehensive training in every Python application area.
Review the curriculum for the full balance of language, object-oriented and database lessons. Recorded tool setup may differ from current interfaces; use current documentation to prepare your practice environment. Choose the course for a structured starting point and allow time to experiment between lessons.
Learning Python as a way to express a process
Python's readable syntax can make a first example approachable, but readability alone does not guarantee understanding. This course helps you connect the statements with the process they describe. You begin with variables and control flow, then encounter collections, object-oriented concepts, additional Python topics and an SQL extension. The practical reading work gives you focused questions about cleaning data and function behaviour. The goal is a broad beginner foundation, with enough structure to identify which idea needs more practice when a result is unexpected.
Starting with values that have a purpose
A variable should represent something you can explain. The early lessons give you a place to consider data types, operators and conversions in that context. If text enters a calculation, ask what transformation is required and what assumption supports it. Keep intermediate values in your notes. This makes it easier to locate a misunderstanding before it spreads through a longer program. It also turns a small example into evidence of reasoning, rather than a line of code remembered only because it ran successfully once.
Conditions as explicit choices
A decision rule should divide inputs in a way that matches the task. Write the cases before choosing the Python expression. Which inputs are accepted? Which lead to a different result? What happens at the boundary? The conditional lessons support this kind of investigation. A few deliberate test values can reveal more than many arbitrary ones. You are learning to state and check an interpretation, not merely to obtain an output that resembles a demonstration.
Loops and collections
Repeated processing needs both data and a rule for progress. The course's loop and collection material helps you examine how values are visited and changed. Follow a small example manually before running it. Record which values are read, which are updated and when repetition finishes. This is particularly useful when a result depends on the order of operations. If your trace and the output disagree, locate the first difference. That gives you a narrower and more productive question than deciding that the entire program is confusing.
Objects and broader Python ideas
The object-oriented section introduces a way to organise state and behaviour, while later topics extend the foundation. Treat each extension according to the depth shown in the curriculum. A class should have a responsibility you can describe; a method should have an understandable effect. You do not need to introduce a large hierarchy to begin learning those ideas. The course is not an exhaustive reference to every Python library or professional development pattern. Its purpose is to make a collection of introductory concepts more connected and usable.
Cleaning and counting task labels
The reading lab gives data preparation a concrete purpose. A collection of task labels can contain inconsistencies that affect a count. Before changing the text, define what should count as the same label and what information must be preserved. Then compare expected and observed totals. This connects earlier work on strings, collections and conditions with a small investigation. The activity does not promise a production data-cleaning pipeline. It gives you a focused problem in which the rule can be explained and its effect checked.
Why a default argument can remember
The article on mutable default arguments examines behaviour that can surprise someone familiar only with isolated calls. A list used as a default may retain information between calls in a way the author did not intend. The learning opportunity is to ask when a value is created and which calls share it. Keep the example small and compare two calls rather than inspecting only the first. This makes a language detail meaningful through an observed result and encourages more deliberate reasoning about function inputs.
Files, databases and the wider view
The curriculum also introduces further Python and SQL-related topics. These help connect a short program with information outside its immediate calculation. A file and a database record serve different roles, so identify what each example is trying to demonstrate. Additional setup may be needed if you reproduce the database work. The extension is introductory; it does not turn the course into complete database administration or specialist backend training. Use it to recognise a possible next direction after the core Python concepts are clearer.
A realistic practice routine
Choose a small example after each concept and change one assumption. Predict the result, run the code and explain any difference. This routine works whether you are new to programming or returning to an early topic you never fully understood. The course suits someone who wants breadth at a beginner level and concrete reading exercises. It does not promise instant expertise, a job outcome or mastery of every Python use case. A useful result is a foundation you can explain independently, plus a clear list of topics for deeper practice or a later focused project.
Curriculum at a glance
Introduction to Programming & Sequence and Python Programming Language
Variables & Data Types
If-Statements and Conditional Logic
Loops and Iteration
Arrays
Object-oriented Programming
Advanced Concepts in Python
Beyond Python: From Python to SQL - Using Databases to Store Data
Who this course is for:
- You are new to Python and want a broad introduction to programming logic, collections, functions and object-oriented ideas.
- You have used isolated Python snippets and want to understand how a complete example reaches its result.
- You prefer small coding tasks and reading labs that connect concepts with data cleaning, counting and function behaviour.
- You are building a foundation for later Python study and want initial exposure to files, databases and the additional topics shown in the curriculum.