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Python Training
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Python Training

4.1

This Python Training Course takes you from writing your first script to building real automation tools, analyzing data, and consuming APIs. You'll master core programming concepts, object-oriented design, and industry-standard libraries used by professional developers every day. By the end, you'll have a portfolio-ready capstone project and the practical skills employers are actively looking for.

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What you will learn:

You will start with Python fundamentals — syntax, data types, and control flow — and progressively advance to functions, object-oriented programming, and file handling. You will work with NumPy and Pandas to process and analyze real datasets, and use the requests library to interact with REST APIs. The course also covers automated testing with pytest, version control with Git and GitHub, and data visualization with Matplotlib and Seaborn. You will apply professional coding standards including PEP 8, type hints, and documentation practices. A final capstone project ties every skill together into a complete, presentable Python application.

How you study in practice Python Training

How you practice Python Training

For companies that want to train their team

With Dedika for Business, the course includes exercises and examples tailored to your own business and the way your company needs.

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Course content

8 Chapters • 34 LessonsDuration between 4 and 360 hours (you decide)

Chapter 1See details

Python Foundations and Environment Setup

  • Lesson 1 • Installing Python and Dev Tools

    Covers Python interpreter installation, version selection, and IDE setup. Establishes the baseline environment needed for all subsequent coding exercises.

  • Lesson 2 • Basic Input, Output, and Operators

    Covers print(), input(), and arithmetic, comparison, and logical operators. These primitives appear in every program written in the course.

  • Lesson 3 • Variables, Data Types, and Literals

    Teaches dynamic typing, built-in data types, and literal notation. Understanding types is prerequisite to all data manipulation chapters.

  • Lesson 4 • Python Syntax and Code Structure

    Introduces indentation rules, comments, and statement structure. Correct syntax habits prevent errors throughout the entire course.

Chapter 2See details

Control Flow and Program Logic

  • Lesson 1 • Comprehensions and Functional Iteration

    Teaches list, dict, and set comprehensions alongside map() and filter(). Concise iteration patterns improve readability in data-heavy programs.

  • Lesson 2 • for Loops and Iteration

    Covers iterating over sequences and using range(). Loop patterns introduced here recur in collections, files, and data processing chapters.

  • Lesson 3 • while Loops and Loop Control

    Introduces condition-driven loops and break, continue, and pass statements. These controls enable precise flow management in complex algorithms.

  • Lesson 4 • Conditional Statements

    Teaches if, elif, and else blocks with Boolean expressions. Conditional logic is the foundation of all decision-making code in later chapters.

Chapter 3See details

Functions and Code Reusability

  • Lesson 1 • Lambda Functions and Decorators

    Introduces anonymous functions and function-wrapping decorators. These tools appear extensively in frameworks and functional-style code.

  • Lesson 2 • Defining and Calling Functions

    Covers def syntax, return values, and calling conventions. Functions are the primary unit of reuse in all subsequent project work.

  • Lesson 3 • Advanced Parameter Techniques

    Teaches default values, keyword arguments, *args, and **kwargs. These patterns enable flexible APIs used in libraries and frameworks later.

  • Lesson 4 • Scope, Namespaces, and Closures

    Explains LEGB scope rules, global/nonlocal keywords, and closure mechanics. Scope mastery prevents subtle bugs in larger programs.

Chapter 4See details

Data Structures: Lists, Tuples, Sets, and Dicts

  • Lesson 1 • Lists: Creation and Manipulation

    Covers list indexing, slicing, and all mutating methods. Lists are the most common structure in data processing and algorithm work.

  • Lesson 2 • Tuples and Immutable Sequences

    Teaches tuple creation, packing, unpacking, and use cases. Immutability makes tuples ideal for fixed records and dictionary keys.

  • Lesson 3 • Sets and Set Operations

    Introduces set creation, membership testing, and mathematical operations. Sets enable fast deduplication and relationship queries in data pipelines.

  • Lesson 4 • Dictionaries: Keys, Values, and Methods

    Covers dict creation, access patterns, iteration, and merging. Dicts underpin JSON handling, configuration, and caching in real applications.

  • Lesson 5 • Choosing and Nesting Data Structures

    Analyzes time complexity trade-offs and nested structure patterns. Informed structure selection is critical for performant, readable code.

Chapter 5See details

Object-Oriented Programming in Python

  • Lesson 1 • Methods: Instance, Class, and Static

    Covers the three method types and their appropriate use cases. Correct method choice clarifies intent and improves API design.

  • Lesson 2 • Inheritance and Method Overriding

    Introduces single and multiple inheritance, super(), and method resolution. Inheritance reduces duplication across related class families.

  • Lesson 3 • Encapsulation and Special Methods

    Covers name mangling, dunder methods, and operator overloading. These features produce intuitive, Pythonic class interfaces.

  • Lesson 4 • Classes, Objects, and Attributes

    Teaches class definition, __init__, instance attributes, and object creation. Classes are the building blocks of all OOP-based projects.

Chapter 6See details

File I/O, Exceptions, and Modules

  • Lesson 1 • Exception Handling

    Teaches try, except, else, finally, and custom exceptions. Proper error handling prevents crashes and enables graceful degradation.

  • Lesson 2 • Working with CSV and JSON Files

    Introduces the csv and json standard-library modules for structured data. These formats are ubiquitous in data exchange and API integration.

  • Lesson 3 • Reading and Writing Files

    Covers open(), file modes, context managers, and binary vs. text I/O. File handling is essential for data ingestion and output in real projects.

  • Lesson 4 • Modules, Packages, and Imports

    Explains import mechanics, package structure, and the standard library. Modular design is the foundation of maintainable, large-scale Python projects.

Chapter 7See details

Working with Libraries: NumPy and Pandas

  • Lesson 1 • Data Cleaning and Transformation

    Covers missing value handling, type casting, and apply() transformations. Clean data is a prerequisite for accurate analysis and modeling.

  • Lesson 2 • NumPy Statistical and Linear Algebra Tools

    Introduces aggregation functions, random number generation, and matrix operations. These tools underpin machine learning preprocessing and simulation.

  • Lesson 3 • Grouping, Merging, and Aggregation

    Introduces groupby(), merge(), and pivot tables for summarizing data. These operations replicate SQL-style analytics entirely within Python.

  • Lesson 4 • NumPy Arrays and Operations

    Covers ndarray creation, indexing, broadcasting, and vectorized math. NumPy efficiency is the backbone of scientific and data-science workflows.

  • Lesson 5 • Pandas Series and DataFrames

    Teaches Series and DataFrame creation, indexing, and basic inspection. DataFrames are the primary structure for tabular data analysis.

Chapter 8See details

Applied Python: APIs, Automation, and Projects

  • Lesson 1 • Scheduling and Task Automation

    Introduces scheduled scripts, argument parsing, and logging. Production automation requires reliable scheduling and observable execution.

  • Lesson 2 • Capstone Project Planning and Delivery

    Guides students through scoping, building, and presenting a complete project. Synthesizes all core skills into a portfolio-ready deliverable.

  • Lesson 3 • File System and OS Automation

    Teaches os, pathlib, and shutil for file and directory automation. Automating repetitive file tasks saves significant time in professional workflows.

  • Lesson 4 • HTTP Requests and REST APIs

    Covers the requests library, HTTP methods, headers, and JSON responses. API consumption is a core skill in modern data and backend development.

Certification

Your valid completion certificate

This course is for you:

  • Career changers: seeking a structured path into tech from non-coding backgrounds.

  • Data professionals: wanting to replace manual spreadsheet work with Python scripts.

  • Students: building practical programming skills alongside a degree or bootcamp.

  • Marketers and analysts: looking to automate reporting and handle data more efficiently.

  • Hobbyists: eager to turn creative project ideas into working Python applications.

  • IT professionals: expanding their skill set beyond infrastructure into scripting and automation.

What our students say

Your classes are perfect. I purchased the one-year package and finally have the opportunity to follow various topics of my interest without needing to switch platforms... I thank you for everything you do, I've already recommended you to other people...
Giulio Carlo
Giulio CarloDigital Marketing Student
I like how the lessons are straight to the point and how I can switch chapters and skip content I don't need.
Mariana Ferres
Mariana FerresPhotography Student
I like the content and the presentation style and video transcription, which speeds up the process!
Luciana Alvarenga
Luciana AlvarengaNail Design Student
The platform is fast, simple to use. The diversity of content and complementary videos really help with learning.
André Felipe
André FelipePrompt Engineering Student

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