Python Programming: level 1 (zero-to-hero)
Duration of Course: 5 weeks
Pre-requisites: No, there is no pre-requisite for this course. This course is designed to train learners having no python programming knowledge.
Course Overview:
This course provides an introduction to programming and the Python language. Students are introduced to core programming concepts like data structures, conditionals, loops, variables, and functions. This course includes an overview of the various tools available for writing and running Python, and gets students coding quickly. It also provides hands-on coding exercises using commonly used data structures, writing custom functions, and reading and writing to files. This course may be more robust than some other introductory python courses, as it delves deeper into certain essential programming topics.
What you will learn:
- Identify core aspects of programming and features of the Python language
- Understand and apply core programming concepts like data structures, conditionals, loops, variables, and functions
- Use different tools for writing and running Python code
- Design and write fully-functional Python programs using commonly used data structures, custom functions, and reading and writing to files
- Great experience of OOPs, Class, Object.
Course Details:
WEEK 1
OVERVIEW OF PYTHON LANGUAGE:
- Installation and environment setup for python
- Hardware and software requirements
- Introduction to python programming languages
- Using interpreter
- Debugging
- Real-world project demonstration and case study
DATA STRUCTURE, VARIABLES AND DATA TYPES:
- variables, values and types
- Tuples
- List
- Dictionary
- Operation on data structures
- Slicing
- Variable names and keywords
- Operators and operands
- Expressions and statements
- Interactive mode and script mode
- Order of operations
- String operations
- Comments
- Debugging
- Student work: practical demonstrations and discussion
WEEK 2
DECISION MAKING AND LOOP:
- if , if-else, nested-if, if-else-if, switch, break, continue
- Logical operators, Boolean expressions, Modulus operator
- Conditional execution, Alternative execution
- Recursion, Stack diagrams for recursive functions, Infinite recursion
- for, while, do-while
FUNCTIONS:
- Building module
- Function, function types, function call
- Lambdas, Map/Filter
- Type conversion functions
- Composition
- Adding new functions
- Flow of execution
- Function parameters and arguments
- Local variable and parameters
- Stack diagrams
- Fruitful functions and void functions
WEEK 3
FILES AND EXCEPTION HANDLING:
- Text file, File path
- CSV file
- Pickling
- Database
- Understanding exceptions,
- The AssertionError Exception
- Handling exception
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CONCEPTS OF OOPs:
- Introducing class and object
- Real-world example of class and object
- Creating a class
- Inheritance
- Polymorphism
- Encapsulation
- Operator overloading
WEEK 4
CLASS AND OBJECTS:
- User-defined compound types
- Attributes
- Instances as arguments
- Instances as return values
- Objects are mutable
- Callable and non-callable object
- Decorators
CLASS AND METHOD:
- The self, Optional arguments
- Printing objects
- The init method, The initialization method
- The __str__ method
- Operator overloading
- Polymorphism
CLASS AND FUNCTION:
- Time, Pure functions
- Modifiers
- Algorithms
INHERITANCE:
- Class attributes
- Private, Protected and Public
- Multiple Inheritance
- Data encapsulation
Students’ final project selection and guidance: students can select any python project as final project. Our team will guide students to brainstorm interesting project that demonstrates all learning throughout this course.
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BONUS from DreamTech to our valuable students:
WEEK 5 (EXTRAS)
GitPython:
- Initializing Git repository, Create your Git repo, access on python
- Blobs, Commit, Tag, Trees
- Remote repo: Clone, fetch, pull, push
- Understanding simple workflow of Git.
Python Programming Level 2
INTRODUCING DJANGO:
- Introduction and demonstration
- Installation process
- Django template
- Recommendations
Python Programming Level 3
INTRODUCING PYTHON FOR DATA SCIENCE AND MACHINE LEARNING:
- Pandas, Numpy
- Data visualization, matplotlib
- Jupyter notebook
- Machine learning demonstrations
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