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Python Core Advanced Training


New Batches Starts For Python Core Advanced Training From November

New Batches Starts For Python Core Advanced Training From November

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Python Core Advanced

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

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Weeks Duration
Hr/Week Therory
Hr/Week Lab
Students per Batch

Core Python: 3 weeks

Week 1

  • 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
  • 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.
  • 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
  • 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

  • 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
  • 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
  • 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
  • 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

  • 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: 3 weeks

Week 4

  • Laying Out Your Project
  • Version Numbering
  • Coding Style and Automated Checks
  • The Import System
  • Useful Standard Libraries
  • External Libraries
  • Package Installation: Getting More from pip
  • Using and Choosing Frameworks
  • 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

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

  • Creating Pure Functions
  • Generators
  • List Comprehensions
  • Functional Functions Functioning
  • Looking at the AST
  • Extending flake8 with AST Checks
  • Introduction to Hy
  • 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
  • Multithreading in Python and Its Limitations
  • Multiprocessing vs. Multithreading
  • Event-Driven Architecture
  • Other Options and asyncio
  • Service-Oriented Architecture
  • Interprocess Communication with ZeroMQ
  • RDBMSs, ORMs, and When to Use Them
  • Database Backends
  • Streaming Data with Flask and PostgreSQL


T. Aditya

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

H. Varsha

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!

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

I did my Python Training in Hyderabad at Invictus. The Python Training Programme was good. My Python Mentor at Invictus gave us an in-depth training of the Python programming language and its application. Overall a very good training platform for the Python course. I will recommend Invictus to my friends.

A. Isaac

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!