Software Engineering in Python

at SMART Courses

Learning at SMART Courses is backed by field professionals. Our aim is to keep students up-to-date with latest technology and trends.

Course Introduction

Welcome to the “Software Engineering Using Python” course! This comprehensive, six-month program is designed to provide you with a solid foundation in Python programming and software engineering principles. Whether you’re a beginner or looking to enhance your programming skills, this course offers a structured and practical approach to mastering Python and applying it to real-world software development challenges.

Course Objectives

By the end of this course, you will:

Understand Python Fundamentals: Gain a thorough understanding of Python syntax, data types, control structures, and core programming concepts.
Master Object-Oriented Programming: Learn the principles of object-oriented programming (OOP) and how to implement them in Python.
Develop Web Applications: Build and deploy web applications using the Flask framework, integrating databases and handling user inputs securely.
Explore Data Science: Utilize Python for data analysis, data visualization, and basic machine learning, leveraging libraries such as Pandas, Matplotlib, and Scikit-Learn.
Delve into Advanced Python: Explore advanced topics such as decorators, generators, and metaclasses, enhancing your coding efficiency and capabilities.
Work with APIs and Version Control: Learn how to interact with APIs, parse JSON data, and use Git for version control and collaboration.
Capstone Project: Apply your knowledge to a capstone project, showcasing your skills in a comprehensive, real-world application.

Who Should Enroll

Beginners: Individuals new to programming who want to start their journey with a versatile and powerful language.
Intermediate Programmers: Those who have basic knowledge of programming and wish to deepen their understanding of Python and software engineering.
Aspiring Software Engineers: Individuals aiming for a career in software development, web development, or data science using Python.

Learning Approach

Hands-on Exercises: Daily coding exercises and quizzes to reinforce learning and practice new concepts.
Mini-Projects: Weekly mini-projects to apply what you’ve learned in practical scenarios.
Capstone Project: A comprehensive project in the final month to demonstrate your skills and knowledge.
Peer Reviews and Feedback: Regular reviews and feedback sessions to help you improve and learn collaboratively.

Course Structure

This course is divided into six modules, each focusing on different aspects of Python and software engineering:

Python Fundamentals: Basics of Python programming, data structures, functions, and file handling.
Object-Oriented Programming: Core OOP principles, advanced OOP concepts, and exception handling.
Web Development with Python: Building web applications with Flask, integrating databases, and deploying applications.
Data Science with Python: Data analysis, visualization, and basic machine learning techniques.
Advanced Topics: In-depth exploration of advanced Python features, APIs, and version control with Git.
Capstone Project: A project-based module to consolidate your learning and showcase your skills.

Additional Resources and Assignments

Daily: Hands-on coding exercises and quizzes
Weekly: Mini-projects and peer reviews
Monthly: Major assignments and project milestones

Join us in this immersive learning experience and take your Python programming skills to the next level, preparing you for a successful career in software engineering!

Course Overview

Assignment

Schedule

Fees

Course Overview

Section 1: Python Fundamentals

Module 1: Introduction to Python

Course Introduction, Python Installation, and Setup
Python Syntax, Variables, and Data Types
Basic Operators and Expressions
Conditional Statements (if, elif, else)
Loops (for, while)

Module 2: Data Structures

Lists and List Operations
Tuples and Tuple Operations
Dictionaries and Dictionary Operations
Sets and Set Operations
Review and Practice Exercises

Module 3: Functions and Modules

Defining and Calling Functions
Function Parameters and Return Values
Lambda Functions
Modules and Packages
Review and Practice Exercises

Module 4: File Handling

Reading from Files
Writing to Files
Working with CSV Files
File Handling Errors
Review and Practice Exercises

Section 2: Object-Oriented Programming

Module 1: Introduction to OOP

Classes and Objects
Attributes and Methods
The self Parameter
Constructor and Destructor
Review and Practice Exercises

Module 2: Advanced OOP Concepts

Inheritance
Method Overriding
Encapsulation and Data Hiding
Polymorphism
Review and Practice Exercises

Module 3: Exception Handling

Introduction to Exceptions
Try, Except, and Finally Blocks
Handling Multiple Exceptions
Custom Exceptions
Review and Practice Exercises

Module 4: Working with Libraries

Introduction to Standard Libraries
Working with math and random Modules
Working with datetime and calendar Modules
Introduction to Third-Party Libraries (e.g., requests, numpy)
Review and Practice Exercises

Section 3: Web Development with Python

Module 1: Introduction to Web Development

Basics of HTTP and Web Servers
Introduction to Flask
Setting Up a Flask Application
Routing and Views
Review and Practice Exercises

Module 2: Flask Templates and Forms

Jinja2 Templates
Creating and Rendering Templates
Forms and User Input
Form Validation
Review and Practice Exercises

Module 3: Database Integration

Introduction to SQL and Databases
Setting Up SQLite
Integrating SQLite with Flask
Performing CRUD Operations
Review and Practice Exercises

Module 4: Advanced Flask Features

Flask Extensions
User Authentication
File Uploads
Deploying Flask Applications
Review and Practice Exercises

Section 4: Data Science with Python

Module 1: Introduction to Data Science

Overview of Data Science
Setting Up the Data Science Environment
Introduction to Pandas
DataFrames and Series
Review and Practice Exercises

Module 2: Data Analysis and Visualization

Data Cleaning and Preparation
Data Analysis with Pandas
Introduction to Matplotlib
Data Visualization with Matplotlib
Review and Practice Exercises

Module 3: Advanced Data Analysis

Grouping and Aggregating Data
Merging and Joining DataFrames
Time Series Analysis
Advanced Plotting Techniques
Review and Practice Exercises

Module 4: Machine Learning Basics

Introduction to Machine Learning
Supervised vs. Unsupervised Learning
Introduction to Scikit-Learn
Building a Simple ML Model
Review and Practice Exercises

Section 5: Advanced Topics

Module 1: Advanced Python Concepts

Decorators
Generators
Context Managers
Metaclasses
Review and Practice Exercises

Module 2: Working with APIs

Introduction to APIs
Making API Requests with requests
Parsing JSON Data
Building a Simple API with Flask
Review and Practice Exercises

Module 3: Testing and Debugging

Introduction to Testing
Writing Unit Tests
Using unittest and pytest
Debugging Techniques
Review and Practice Exercises

Module 4: Version Control with Git

Introduction to Git and GitHub
Basic Git Commands
Branching and Merging
Collaboration with GitHub
Review and Practice Exercises

Section 6: Capstone Project and Review

Module 1: Project Planning

Introduction to the Capstone Project
Project Ideation and Selection
Defining Project Scope and Requirements
Setting Up the Project Environment
Review and Initial Feedback

Module 2: Project Development – Part 1

Designing the Project Architecture
Implementing Core Features
Adding User Authentication (if applicable)
Integrating with a Database
Review and Progress Check

Module 3: Project Development – Part 2

Implementing Additional Features
Data Handling and Processing
API Integration (if applicable)
Testing and Debugging
Review and Progress Check

Module 4: Project Finalization and Presentation

Finalizing Features
User Interface Enhancements
Writing Documentation
Preparing the Project Presentation
Final Project Presentation and Review

Assignment

Schedule

Fees