In this course, you will learn the fundamentals of Python programming, including syntax, control structures, data handling, and advanced concepts like object-oriented programming, APIs, and machine learning. By the end of the program, you will be able to write efficient Python scripts, create robust applications, automate tasks, and analyze data effectively. These skills are directly applicable to roles in data analysis, software development, automation, and machine learning.
Lesson Plan
Module 1: Introduction to Python
In this module, you will learn the fundamentals of Python, including its features, syntax, and setup. You will understand how to install Python and configure a development environment, write basic programs, and utilize variables, data types, and operators. These foundational skills will enable you to start coding in Python and develop a strong understanding of programming basics.
1.1 Overview of Python and its features
1.2 Installing Python and setting up the development environment
1.3 Python syntax and basic program structure
1.4 Variables, data types, and operators in Python
Module 2: Control Flow and Loops
This module covers decision-making and repetitive processes using Python’s control flow and looping structures. You will learn to implement conditional statements, use loops to perform repetitive tasks, and control program flow using keywords like break and continue. These skills are crucial for writing efficient and logical Python programs.
2.1 Conditional statements: if, else, elif
2.2 Looping structures: for loop, while loop
2.3 Iteration and control flow statements: break, continue
2.4 Understanding the flow of program execution
Module 3: Functions and Modules
Learn how to create reusable code using functions and modules. This module teaches you to define functions, pass parameters, and use return values. You’ll also explore how to organize and import Python modules to enhance your program’s modularity and scalability. These concepts will help you write clean, efficient, and maintainable code.
3.1 Creating and calling functions
3.2 Function parameters and return values
3.3 Scope of variables in functions
3.4 Introduction to modules and importing modules in Python
Module 4: Data Structures
This module focuses on Python’s powerful data structures, including lists, tuples, dictionaries, and sets. You will learn how to manipulate these structures and implement advanced techniques such as list and dictionary comprehensions. Mastering these skills will allow you to handle data more effectively and optimize your code.
4.1 Lists, tuples, and dictionaries
4.2 Accessing and manipulating elements in data structures
4.3 List comprehensions and dictionary comprehensions
4.4 Working with sets and advanced data structures
Module 5: File Handling
File handling is an essential aspect of any programming language. This module introduces you to reading, writing, and processing files in Python. You will learn to work with text and CSV files, handle file modes, and manage errors using exception handling. These skills are critical for data processing and file management in Python.
5.1 Reading and writing files in Python
5.2 Handling file objects and file modes
5.3 Processing text and CSV files
5.4 Exception handling and error management
Module 6: Object-Oriented Programming (OOP)
In this module, you will learn about object-oriented programming concepts such as classes, objects, inheritance, and polymorphism. These concepts will help you write code that is reusable, scalable, and easier to maintain. By applying OOP principles, you can design robust and complex Python applications.
6.1 Introduction to OOP concepts
6.2 Creating classes and objects
6.3 Class attributes and instance attributes
6.4 Inheritance and polymorphism
Module 7: Modules and Packages
This module teaches you how to structure your code effectively using modules and packages. You will learn to create custom modules, import external libraries, and leverage Python’s vast ecosystem of packages to enhance functionality. This knowledge will help you build scalable and maintainable projects.
7.1 Organizing code into modules and packages
7.2 Creating and importing custom modules
7.3 Exploring popular Python libraries and packages
7.4 Using external libraries to enhance program functionality
Module 8: Working with APIs and Web Services
Discover how to interact with external data sources using APIs and web services in Python. You will learn to make HTTP requests, parse JSON and XML responses, and build API clients. These skills are essential for integrating Python programs with modern web applications and services.
8.1 Understanding APIs and web services
8.2 Making HTTP requests using Python
8.3 Parsing JSON and XML responses
8.4 Building API clients and integrating with web services
Module 9: Introduction to Machine Learning with Scikit-Learn
This module provides an introduction to machine learning concepts and the Scikit-Learn library. You will learn to implement supervised and unsupervised algorithms, train models, and evaluate their performance. These foundational skills are critical for building machine learning solutions using Python.
9.1 Overview of machine learning concepts
9.2 Installing and importing the Scikit-Learn library
9.3 Supervised and unsupervised learning algorithms
9.4 Building and evaluating machine learning models
Module 10: Exception Handling
Learn how to handle errors and exceptions in Python effectively. This module teaches you the difference between syntax errors and exceptions, how to use try and except statements, and the role of the finally keyword. Proper exception handling ensures that your programs run smoothly, even in unexpected scenarios.
10.1 Difference between syntax errors and exceptions
10.2 Try and Except statement
10.3 Catching exceptions
10.4 Finally keyword in Python
Roadmap for the Certified Python Specialist Training Program
Week 1: Foundations of Python Programming
Overview of Python's capabilities and setting up the environment.
Mastering Python syntax, variables, data types, and operators.
Introduction to control flow, loops, and conditional statements.
Week 2: Functions, Modules, and Data Structures
Writing reusable code with functions and modules.
Organizing projects with modules and packages.
Exploring and manipulating lists, dictionaries, tuples, and sets.
Week 3: Advanced Programming and Real-World Applications
File handling: Reading, writing, and processing files.
Object-oriented programming (OOP): Classes, objects, inheritance, and polymorphism.
Using APIs to interact with web services and external data sources.
Week 4: Advanced Concepts and Machine Learning Introduction
Exception handling for robust applications.
Introduction to Scikit-Learn for machine learning basics.
Building, training, and evaluating machine learning models.
Tools Covered
Python Environment: Anaconda, IDLE, and Jupyter Notebook.
Development Tools: PyCharm, VS Code.
Data Handling: Pandas, NumPy.
Web Interaction: Requests, JSON/XML Parsing Libraries.
File Processing: CSV, OS, and File Libraries.
Visualization: Matplotlib, Seaborn.
Machine Learning: Scikit-Learn.
APIs and Web Services: Requests, Flask (for basic API handling).
Key Learning Outcomes
Foundational Programming Skills:
Understand Python syntax, data types, and control structures.
Develop clean, readable, and efficient code.
Data Handling and Processing:
Manipulate large datasets using Python's powerful data structures.
Perform file I/O operations and process structured/unstructured data.
Object-Oriented Programming (OOP):
Build reusable, scalable applications with classes, objects, and inheritance.
Apply OOP principles for better code organization and modularity.
API and Web Services Integration:
Make HTTP requests and interact with APIs.
Parse JSON/XML data for web-based integrations.
Automation and Efficiency:
Automate tasks using loops, functions, and file handling.
Write reusable code with custom modules and Python packages.
Introduction to Machine Learning:
Understand key machine learning concepts.
Build simple models using Scikit-Learn and evaluate their performance.
Error Handling and Debugging:
Handle exceptions to create robust and reliable applications.
Debug programs effectively for seamless execution.
Project Development:
Build real-world projects in areas like data analysis, web automation, and machine learning.
Apply end-to-end Python workflows for problem-solving.
Still need help?
Contact us