Workshops :

1. Master The Python Interview 1-Day workshop Know More | 2.One Day Workshop - Python Project (Learn how to approach programming) Know More | 3. The Extraordinary Python Coder - Workshop Know More | 4. Artificial Intelligence for Everyone Know More | 5. Data Analytics for Solving Business Problems Know More | 6. Machine Learning for Predictive Analytics Know More

Join India's No.1 Outcome Focused

Core and Advanced Python

Curriculum Based on Real insights From 50 Top tech Companies

About Invictus Engineers Python Full Stack Training Program

Invictus Engineers Python Full Stack Training Program is a tailor-made program for you to make or take your career in Python Full Stack to the next level via:

  • Vetted curriculum by Industry experts
  • 1:1 mentorship from industry veterans
  • Live business scenario projects
  • Extensive Career support
Weeks Duration
Hr/week Therory
Hr/week Lab

I. Core Python: 3 weeks

Week 1

  1. Introduction to Python Programming
  • 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


  1. 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


  1. 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


  1. 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


Week 2

  1. 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


  1. 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


  1. 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


  1. 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


Week 3

  1. 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 Simulation
  • 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

II. Advanced Python: 3 weeks (Project development centric)

Week 4

  1. Starting Your Project
  • Laying Out Your Project
  • Version Numbering
  • Coding Style and Automated Checks
  1. Modules, Libraries, and Frameworks
  • The Import System
  • Useful Standard Libraries
  • External Libraries
  • Package Installation: Getting More from pip
  • Using and Choosing Frameworks
  1. Documentation and Good API Practice
  • Documenting with Sphinx
  1. 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

Week 5

  1. Distributing Your Software
  • The History
  • Packaging with setup.cfg
  • The Wheel Format Distribution Standard
  • Sharing Your Work with the World
  • Entry Points
  1. Unit Testing
  • The Basics of Testing
  • Virtual Environments
  • Testing Policy
  1. 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

Week 6

  1. Functional Programming
  • Creating Pure Functions
  • Generators
  • List Comprehensions
  • Functional Functions Functioning
  1. The Abstract Syntax Tree, Hy, and Lisp-like Attributes
  • Looking at the AST
  • Extending flake8 with AST Checks
  • Introduction to Hy


  1. 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


  1. 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


  1. Managing Relational Databases
  • RDBMSs, ORMs, and When to Use Them
  • Database Backends
  • Streaming Data with Flask and PostgreSQL

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