Python Core Advanced Training

Course Duration : 14 Weeks

Level : All Levels

Instructor-Led Training : Yes

Live Classroom: Yes

Live-Online : Yes

Where frontend meets backend in perfect harmony

This Python course assists users in mastering AI and machine learning technologies, as well as updating their abilities and understanding these languages. This Python training covers the fundamentals of Python, data operations, conditional expressions, shell scripting, and Django. This Python course will provide you with hands-on programming experience and prepare you for a lucrative career as a Python programmer

Vetted curriculum by Industry experts

Real-Time Experienced Trainers

Hands-On Learning

Extensive Career support

Excel in Python with our Core & Advanced Training! This comprehensive course takes you from basics to advanced concepts, equipping you with essential skills to master Python programming. Enrol today!

Weeks Duration

Hr/Week Therory

Hr/Week Lab

Students

Students per Batch Core Python

Introduction to Python Programming

Introduction
Introduction

Variables and Assignment Statements

Arithmetic

Function print and an Intro to Single- and Double-Quoted Strings

Triple-Quoted Strings

Getting Input from the User

Decision Making: The if Statement and Comparison Operators

Objects and Dynamic Typing

Intro to Data Science: Basic Descriptive Statistics
Control Statements and Program Development
Introduction

Algorithms

Pseudocode

Control Statements

if Statement

if…else and if…elif…else Statements

while Statement

for Statement: Iterables, Lists and Iterators & Built-In range Function

Augmented Assignments

Program Development: Sequence-Controlled Repetition: Requirements Statement,
Pseudocode for the Algorithm, Coding the Algorithm in Python & Introduction to
Formatted Strings

Program Development: Sentinel-Controlled Repetition

Program Development: Nested Control Statements

Built-In Function range: A Deeper Look

Using Type Decimal for Monetary Amounts

Break and continue Statements

Boolean Operators and, or and not

Intro to Data Science: Measures of Central Tendency—Mean, Median and Mode.

How to write your first programs

Functions
Introduction

Defining Functions

Functions with Multiple Parameters

Random-Number Generation

Case Study: A Game of Chance

Python Standard Library

math Module Functions

Using IPython Tab Completion for Discovery

Default Parameter Values

Keyword Arguments

Arbitrary Argument Lists

Methods: Functions That Belong to Objects

Scope Rules

import

Passing Arguments to Functions

Function-Call Stack

Functional-Style Programming

Intro to Data Science: Measures of Dispersion
Sequences: Lists and Tuples
Introduction

Lists

Tuples

Unpacking Sequences

Sequence Slicing

Del Statement

Passing Lists to Functions

Sorting Lists

Searching Sequences

Other List Methods

Simulating Stacks with Lists

List Comprehensions

Generator Expressions

Filter, Map and Reduce

Other Sequence Processing Functions

Two-Dimensional Lists

Intro to Data Science: Simulation and Static Visualizations
Dictionaries and Sets
Introduction

Dictionaries: Creating a Dictionary, Iterating through a Dictionary, Basic Dictionary
Operations, Dictionary Methods keys and values, Dictionary Comparisons, Dictionary
Method update, & Dictionary Comprehensions

Sets: Comparing Sets, Mathematical Set Operations, Mutable Set Operators and
Methods, & Set Comprehensions

Intro to Data Science: Dynamic Visualizations
Array-Oriented Programming with NumPy
Introduction

Creating arrays from Existing Data

Array Attributes

Filling arrays with Specific Values

Creating arrays from Ranges

List vs. array Performance: Introducing %timeit

Array Operators

NumPy Calculation Methods

Universal Functions

Indexing and Slicing

Views: Shallow Copies

Deep Copies

Reshaping and Transposing

Intro to Data Science: pandas Series and DataFrames
Strings: A Deeper Look
Introduction

Formatting Strings: Presentation Types, Field Widths and Alignment, Numeric
Formatting, & String’s format Method

Concatenating and Repeating Strings

Stripping Whitespace from Strings

Changing Character Case

Comparison Operators for Strings

Searching for Substrings

Replacing Substrings

Splitting and Joining Strings

Characters and Character-Testing Methods

Raw Strings

Introduction to Regular Expressions: re Module and Function fullmatch, Replacing
Substrings and Splitting Strings, & Other Search Functions; Accessing Matches

Intro to Data Science: Pandas, Regular Expressions and Data Munging
Files and Exceptions
Introduction

Files

Text-File Processing: Writing to a Text File, Reading Data from a Text File

Updating Text Files

Serialization with JSON

Focus on Security: pickle Serialization and Deserialization

Additional Notes Regarding Files

Handling Exceptions: Division by Zero and Invalid Input, try Statements, Catching
Multiple Exceptions in One except Clause, What Exceptions Does a Function or
Method Raise?, & What Code Should Be Placed in a try Suite?

finally Clause

Explicitly Raising an Exception

(Optional) Stack Unwinding and Tracebacks

Intro to Data Science: Working with CSV Files
Object-Oriented Programming
Introduction

A Custom Class: Composition: Object References as Members of Classes

Controlling Access to Attributes

Properties for Data Access

Simulating “Private” Attributes

Case Study: Card Shuffling and Dealing Simulations

Inheritance: Base Classes and Subclasses

Building an Inheritance Hierarchy; Introducing Polymorphism

Duck Typing and Polymorphism

Operator Overloading

Exception Class Hierarchy and Custom Exceptions

Named Tuples

A Brief Intro to Python 3.7’s New Data Classes: Creating a Data Class, Using the Data
Class, Data Class Advantages over Named Tuples, & Data Class Advantages over
Traditional Classes

Unit Testing with Docstrings and doctest

Namespaces and Scopes

Intro to Data Science: Time Series and Simple Linear Regression

Advanced Python

Starting Your Project
Laying Out Your Project

Version Numbering

Coding Style and Automated Checks
Modules, Libraries, and Frameworks
The Import System

Useful Standard Libraries

External Libraries

Package Installation: Getting More from pip

Using and Choosing Frameworks
Documentation and Good API Practice

Handling Timestamps and Time Zones
The Problem of Missing Time Zones

Building Default datetime Objects

Time Zone–Aware Timestamps with dateutil

Serializing Time Zone–Aware datetime Objects

Solving Ambiguous Times
Distributing Your Software
The setup.py History

Packaging with setup.cfg

The Wheel Format Distribution Standard

Sharing Your Work with the World

Entry Points
Unit Testing
The Basics of Testing

Virtual Environments

Testing Policy

Methods and Decorators
Decorators and When to Use Them

How Methods Work in Python

Static Methods

Class Methods

Abstract Methods

Mixing Static, Class, and Abstract Methods
Functional Programming
Creating Pure Functions

Generators

List Comprehensions

Functional Functions Functioning
The Abstract Syntax Tree, Hy, and Lisp-like Attributes
Looking at the AST

Extending flake8 with AST Checks

Introduction to Hy
Performances and Optimizations
Data Structures

Understanding Behavior Through Profiling

Defining Functions Efficiently

Ordered Lists and bisect

namedtuple and Slots

Memoization

Faster Python with PyPy

Achieving Zero Copy with the Buffer Protocol

Scaling and Architecture
Multithreading in Python and Its Limitations

Multiprocessing vs. Multithreading

Event-Driven Architecture

Other Options and asyncio

Service-Oriented Architecture

Interprocess Communication with ZeroMQ
Managing Relational Databases
RDBMSs, ORMs, and When to Use Them

Database Backends

Streaming Data with Flask and PostgreSQL

Testimonial's

I recommend this course to anyone looking to get into programming or those looking to sharpen their skills. I had zero experience with python before the course and the instructor was both patient enough to work with me yet still love the course flowing for the more advanced members

Aditya

The course was very comprehensive and easy to understand. The instructors made sure that they are giving the information in a way that won’t make me confused. Thank you so much for this great course!

H. Varsha

F. Darshini

Invictus has improved my practical skill in programming due a lot of practices and guidance. Taking Python core and advanced has really helped me gain confidence to take over so many programming challenges.

I. Arya

My learning experience at Invictus’s Python Training Program was excellent. Well-structured Python Course modules with regular assessment sessions helped me to understand the language at ease. Thanks to my Python trainer, nice work Invictus!

A. Isaac

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