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Course Overview
Schedule of Classes
Course Curriculum
30 Subjects
SKT_PG_Theory_Core Python
96 Learning Materials
Introduction to programming languages
Introduction to Programming Language
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Types of Computer Languages
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Evolution of Computer Languages - 1
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Evolution of Computer Languages - 2
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Programming Paradigms - 1
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Programming Paradigms - 2
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Programming Paradigms - 3
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Programming Paradigms - 4
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Programming Translators
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Types of Scripts
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Logics Building
Logics Building & Flowchart
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Algorithm & Pseudocode
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Introduction to Python
Introduction to Python
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Python Limitations and Libraries
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History of Python
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Features of Python
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Python Applications
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Python Implementations
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Python vs other Languages
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Characteristics of Python
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Python Environment Setup
Downloading & Installation of Python
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Python Real-time IDEs
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Python Program Execution
Python Program Execution
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Basic Syntax
Comments and Indentations in Python
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Keywords and Identifiers
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Variables
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Input and Output Operations
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Data Types
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Type Conversions
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Operators
Introduction to Operators
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Arithmetic Operators
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Assignment Operators
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Relational Operators
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Logical Operators
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Bitwise Operators
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Membership Operators
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Identity Operators
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Conditional Statements
Introduction to Conditional Statement
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Simple If Statement
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If-Else Statement
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If-Elif-Else Statement
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Nested If-Else Statement
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Short-Hand If Statement
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Jump Statement
Match Case
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Range() Function & Del Keyword
Range() Function and Delete Keyword
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Loops
Introduction to Loops
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While Loop
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For Loop
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Nested Loops
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Loop Control Statements
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Functions
Introduction to Functions
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Implementing Functions
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Classification of Functions
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Nested Functions
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Scope of Variables in Functions
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First Class Functions
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Anonymous Functions
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Recursive Functions
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Lists
Introduction to Lists
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Accessing List Elements
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List Operations
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Built in List Functions
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List Methods-1
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List Methods-2
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List Methods-3
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Tuples
Introduction to Tuples
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Accessing Tuple Elements
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Iterating Tuple Elements
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Built-in Tuple Functions
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Tuple Methods
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Strings - 1
Introduction to Strings
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Accessing Values from Strings
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String Operations - 1
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String Operations - 2
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String Manipulation Methods
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Strings - 2
String Validation and Transformation Methods
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String Analysis Methods
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Numeric String Methods
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String Searching and Manipulation
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Strings - 3
Advanced String Operations
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String Alignment Methods
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String Formatting Methods
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String Splitting Methods
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Mastering in String Operations
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Dictionaries
Introduction to Dictionaries
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Working With Dictionaries
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Accessing Keys & Values from Dictinaries
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Built-in Dictionary Methods-1
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Built-in Dictionary Methods-2
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Sets
Introduction to Sets
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Working with Sets
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Sets Built-in Methods - 1
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Sets Built-in Methods - 2
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Set Operations-1
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Set Operations-2
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Comprehensions
Comprehensions
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SKT_PG_Theory_Advance Python
35 Learning Materials
Memory Management
Dynamic Typing
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Modules & Packages
Modules
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Importing Modules
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Creating Modules
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Modules Search Path
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What are Packages
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Creating Packages
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Python DateTime
Data Time Module
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Time module
Time Module - 1
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Time Module - 2
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Python Calendar Module
Calendar Module
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Random Module
Random Module
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Cryptographically Secure Random Generator
UUID Module
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Other Modules in Python
OS Module
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Sys Module
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Logger Module
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JSON Module
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Exception Handling
Introduction to Exception Handling
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Types of Exception Handling
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Errors in Exception Handling
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Working with Exception Handling
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Raising Exception and Creating User Defined Exception
Raising an Exception
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Exception Chaining
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Exception Lifecycle
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Warnings in Exception Handling
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File Handling in Python
Introduction to File Handling
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Types of Files & File Paths
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Types of File Access Modes
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Types of Binary File Access Modes
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Working with Binary File Access Modes
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Create File in Python
Creating an Empty Text File
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Creating File In A Specific Directory
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Open a File in Python
Access Modes for Opening a File
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Read File in Python
Reading a File
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Reading a File into List
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SKT_PG_Theory_OOPS with Python
27 Learning Materials
Introduction to OOPs
Introduction to Object Oriented Programming
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Classes and Objectes
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Class Variables and Instance Variables
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Constructor & Destructor Methods
Constructor method
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Destructor Method
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Types of methods
Types of methods
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Inheritance
Introduction to inheritance
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Multiple Inheritance
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Multilevel Inheritance
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Hierarchial Inheritance
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Hybrid Inheritance
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More on Inheritance
Super() function
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Method Overriding
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Method Resolution Order
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Polymorphism
Introduction to Polymorphism
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Polymorphic Function and Duck Typing
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Method Overriding
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Method Overloading
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Operator Overloading
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Introduction to encapsulation
Introduction to Encapsulation
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Access Modifiers
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Getters and Setters
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Abstraction
Abstraction
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Closures and Decorators
Closures
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Decorators
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Iterators and Generators
Iterators
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Generators
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OLD_SKT_Python Engg Applications
14 Learning Materials
Transpose of a Matrix without using NumPy
Introduction to Transpose of Matrix
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Function to Input a Matrix : Code Explanation
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Function to Input a Matrix : Code Implementation
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Function to Transpose a Matrix : Code Explanation
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Function to Transpose a Matrix : Code Implementation
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Comparing with In-Built Function in NumPy
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Determinant of Matrix without using NumPy
Manually Calculating Determinant of Matrix
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Laplace Expansion to Calculate Determinant
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Function to Find Determinant : Code Explanation
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Function to Find Determinant : Code Implementation
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Comparing with In-Built Function in NumPy
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Cofactor & Minor Matrix without using NumPy
Introduction to Cofactors & Minor Matrix
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Function for Cofactors & Minor Matrix : Code Explanation
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Function for Cofactors & Minor Matrix : Code Implementation
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SKT_DS_Theory_Anaconda Essentials
5 Learning Materials
Essential Programming Languages used for ML
Essential Programming Languages used for ML
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Anaconda
Introduction to Anaconda
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Installing Anaconda
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Anaconda Environment
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Jupyter Notebook
Jupyter Notebook
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SKT_DS_Theory_Pandas
50 Learning Materials
Introduction to Pandas
Introduction to Pandas
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Working with Pandas
Working with Pandas
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Pandas Series-1
Introduction to Series
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Creating Series from Lists
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Creating Series from Dictionary
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Creating Series from NumPy Array
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Creating a Series from Scalar Value
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Creating Series from NaN Values & Index Argument
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Pandas Series-2
Working with Series Indexing
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Accessing Data from Series
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Accessing Data from Series using Slicing
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Accessing Data from Series using loc & iloc
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Series Attributes-1
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Series Attributes-2
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Pandas Series-3
Sorting and Converting Type of Elements in Series
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Working with Null Values in Series
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Summarising Series-1
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Summarising Series-2
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Working with Duplicate Values
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Pandas Series - 4
Arithmetic Operations on Pandas Series
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Comparison / Relational Operations with Pandas Series - 1
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Comparison / Relational Operations with Pandas Series - 2
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Comparison / Relational Operations with Pandas Series - 3
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Logical Operators on Pandas Series
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Manipulating Pandas Series
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Pandas DataFrame - 1
Introduction to Pandas DataFrame
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Creating Pandas DataFrame
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DataFrame Attributes - 1
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DataFrame Attributes - 2
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Converting and Sorting Elements
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Working with Null Values in DataFrame
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Pandas DataFrame - 2
Summarising DataFrame - 1
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Summarising DataFrame - 2
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Working with Duplicates
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Arithmetic Operations on Pandas Dataframe - 1
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Arithmetic Operations on Pandas Dataframe - 2
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Pandas DataFrame - 3
Comparison Operations on Pandas Dataframe - 1
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Comparison Operations on Pandas Dataframe - 2
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Comparison Operations on Pandas Dataframe - 3
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Logical Operations on Pandas Dataframe - 1
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Logical Operations on Pandas Dataframe - 2
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Manipulating pandas Dataframe
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Pandas DataFrame - 4
Accessing Single Column in a DataFrame
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Accessing Multiple Columns
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Accessing Rows in a DataFrame
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Accessing Single Element in a DataFrame
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Reading Data from variuos types of files
Reading & Saving CSV File
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Reading & Saving Excel File
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Reading & Saving JSON File
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Reading Data from SQL Database
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SKT_DS_Lab L1_Pandas
2 Learning Materials
Basic Level Programming on Pandas Series-1
Write a code to create a Series from a NumPy array of random numbers and Print the Series.
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Create a Series with three NaN values, and specify index labels as 'A', 'B', and 'C'.
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Basic Level Programming on Pandas Series-2
Basic Level Programming on Pandas Series-3
Basic Level Programming on Pandas Series - 4
Basic Level Programming on Pandas DataFrame - 1
SKT_DS_Lab L2_Pandas
2 Learning Materials
Intermediate Level Programming on Pandas Series-1
Create a Series and set custom index labels in Pandas
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Create a Series with both positive and negative values and print its mean.
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Intermediate Level Programming on Pandas Series-2
Intermediate Level Programming on Pandas Series-3
Intermediate Level Programming on Pandas Series - 4
Intermediate Level Programming on Pandas DataFrame - 1
SKT_DS_Lab L3_Pandas
2 Learning Materials
Advance Level Programming on Pandas Series-1
Construct a Pandas Series from dictionary {'USA': 331, 'India': 1380, 'China': 1441, 'Brazil': 213}
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Python program using pandas library representing the marks of five students
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Advance Level Programming on Pandas Series-2
Advance Level Programming on Pandas Series-3
Advance Level Programming on Pandas Series - 4
Advance Level Programming on Pandas DataFrame - 1
SKT_DS_Theory_Data Analasys & Vis
143 Learning Materials
About Course
Course Overview
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Introduction to Data Analysis
What is Data Analysis?
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Why is Data Analysis important?
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Sources of Data
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Sampling Techniques
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Data Quality Issues
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Data Management & Storage
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Introduction to Data Visualization
Introduction to Data Visualization
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Data Wrangling
Introduction to Data Wrangling
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Subsetting a Dataset
Subsetting a Dataset
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Selecting Columns
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Selecting Rows
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Selecting Rows & Columns
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Slicing Columns
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Slicing Rows
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Slicing Rows & Columns
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Indexing
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Filtering
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Combining and Merging Data Sets
Database-Style Merging Datasets
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Merging on Index - Outer
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Merging on Index - Left
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Merging on Index - Right
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Concatenating Along an Axis
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Combining Data with Overlap
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Reshaping
Reshaping
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Data Transformation
Introduction for Data Transformation
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Removing Duplicates & Replacing Values
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Transforming Data Using a Function or Mapping
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Renaming Axis Indexes
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Data Discretization
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String Manipulation
String Manipulation
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Group By Operations
Introduction to Group By
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Exploring get_group
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Group by Syntax & Iterating Over Groups
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Grouping with Dicts and Series
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Grouping with Functions
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Grouping By Index Levels
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Data Aggregation
Introduction to Data Aggregation
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Syntax of Data Aggregation
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Column-wise and Multiple Function
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Returning Aggregated Data in "Unindexed" Form
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Group-wise Operations and Transformations
Apply: General split-apply-combine
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Quantile & Bucket Analysis
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Filling Missing Values with Group-specific Values
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Random Sampling & Permutation
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Random Sampling & Permutation using Group By
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Group Weighted Average and Correlation
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Pivot Tables and Cross-Tabulation
Pivot Table
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Cross Tabulation
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Data Preparation & Basic Models
Data Preparation
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Advanced Data Transformation Techniques
Linear Transformation
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Quadratic Transformation
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Non-polynomial Transformation
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Polynomial Transformation
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Rank Transformation
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Box-Cox Transformation
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Histogram
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Missing Values
Introduction to Data Cleaning
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Introduction to Missing Values
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Removing Missing Values
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Mean Imputation
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Median Imputation
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Mode Imputation
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Forward Fill Imputation
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Backward Fill Imputation
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Dealing with Noisy Data
Noisy Data
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Types of Noise Data
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Introduction to Noise Filtering
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Noise Filtering at Data Level
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Enhancing Data Analysis Strategies Against Noise
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Introduction to Outliers
Introduction to Outliers
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Detecting & Handling Outliers
Detecting outliers using Boxplot and IQR
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Detecting outliers using the Z-scores
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Detecting outliers using the percentile method
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Detecting outliers using Standard Deviation method
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Handling Categorical Data
Introduction to Categorical Data
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Categorical Encoding
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One Hot Encoding
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Label Encoding
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Ordinal Encoding
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Helmert Encoding
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Binary Encoding
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Frequency Encoding
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Mean Encoding
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Weight of Evidence Encoding
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Probability Ratio Encoding
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Hashing Encoding
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Backward Difference Encoding
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Leave One Out Encoding
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James Stein Encoding
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M-estimator Encoding
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Thermometer Encoder
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Which Encoding is best?
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Handling Numerical Data
Introduction to Numerical Data
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Introduction to Feature Scaling
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Min- Max Scaling
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Standardization
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Robust Scaling
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Max Abs Scaler
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Exploratory Data Analysis
Introduction to Exploratory Data Analysis
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Intoduction to Univariate Analysis
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Bar Plot for Univariate
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Pie Chart for Univariate
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Count Plot for Univariate
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Histogram for Univariate
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Box PLot for Univariate
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Density Plot for Univariate
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Line Plot for Univariate
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Intoduction to Bivariate Analysis
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Scatter Plot
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Line Plot
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Joint Plot
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Heatmap
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Stacked Bar Plot
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Grouped Bar Plot
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Clustered Heatmap
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Chi-Square Test
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Bar Plot
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Box Plot
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Violin Plot
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Point Plot
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Swarm Plot
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Introduction to Multivariate Analysis
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Heat Map for Multivariate
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Pair plot
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Feature Engineering
Introduction to Feature Engineering
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Feature Selection Techniques & Criteria
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Filter Feature Selection
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Wrapper Feature Selection
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Embedded Feature Selection
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Implications of Feature Selection Methods
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Representative Feature Selection Methods
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Leading and Recent Feature Selection Techniques
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Feature Extraction
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Feature Construction
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Experimental Comparative Analyses in Feature Selection
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Handling Imbalance Data
Introduction to Handle Imbalance Data
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Choose Proper Evaluation Metric
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Resampling
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SMOTE
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Balanced Bagging Classifier
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Threshold moving
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Augmentation
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SKT_DS_Lab L1_Data Analasys & Vis
4 Learning Materials
Basic Level Programming on Subsetting a Dataset
Generate the sample DataFrame and then choose any one of the Columns. Display its contents
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Creating and displaying Data. choose any one of the Row. Display its contents
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Basic Level Programming on Group By Operations
Group the DataFrame by a column and apply multiple aggregation functions
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Group the numbers in the num column they are even or odd and calculate the sum of each group data
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Basic Level Programming on Data Aggregation
Basic Level Programming on Group-wise Operations and Transformations
Basic Level Programming on Pivot Tables and Cross-Tabulation
SKT_DS_Lab L2_Data Analasys & Vis
2 Learning Materials
Intermediate Level Programming on Subsetting a Dataset
Construct a Sample DataFrame use the Slicing feature all columns whose names start with an ‘S’
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Intermediate Level Programming on Group By Operations
Given a list of dictionaries representing customers average order total for each city
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Intermediate Level Programming on Data Aggregation
Intermediate Level Programming on Group-wise Operations and Transformations
Intermediate Level Programming on Pivot Tables and Cross-Tabulation
SKT_DS_Lab L3_Data Analasys & Vis
3 Learning Materials
Advance Level Programming on Subsetting a Dataset
Write a Python Program to import any CSV file to Pandas DataFrame
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Advance Level Programming on Group By Operations
Load, explore a dataset , perform groupby() operations using pandas library and visualise data
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list of dictionaries representing employees group using Sorting higher salaries and longer.
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Advance Level Programming on Data Aggregation
Advance Level Programming on Group-wise Operations and Transformations
Advance Level Programming on Pivot Tables and Cross-Tabulation
SKT_DS_Theory_Mataplotlib
34 Learning Materials
Introduction to Data Visualisation
Introduction to Data Visualisation
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Matplotlib Architecture
Matplotlib Architecture
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Working with Matplotlib
Working with Matplotlib
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Decorators and Styles-1
Grids
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Axes
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Labels
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Markers
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Colors
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Title
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Decorators and Styles-2
Legends
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Ticks
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Font Styles
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Limits
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Decorators and Styles on the Bar Graph
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Different Types of Plots-1
Single Line Plot
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Multi-Line Plot
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Scatter Plot
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Stack Plot
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Stream Plot
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Different Types of Plots-2
Sub-Plot
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Step Plot
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Filled Plot
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Hexagonal Bin Plot
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Different Types of Plots-3
Box Plot
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Violin Plot
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Different Types of Graphs
Bar Graph
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Multi Bar Graph
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Stacked Bar Graph
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Different Types of Charts
Different Types of Charts
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Pie Chart
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Bubble Chart
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Polar Chart
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Histograms and Contours
Histogram
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Contour Plot
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SKT_DS_Theory_Seaborn
34 Learning Materials
Getting Started
Introduction to Seaborn
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Prerequisites & Dependencies
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Installation of Seaborn
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Basics of Seaborn Plotting
Introduction to Plots
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Decorators and Styles - 1
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Decorators and Styles - 2
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Seaborn Figure Styles - 1
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Seaborn Figure Styles - 2
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Seaborn – Color Palettes
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Seaborn Grids
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Relational Plots
Line Plot()
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Scatter Plot()
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Rel Plot()
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Distributions Plots
Histplot()
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KDEplot()
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ECDFplot()
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Rugplot()
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Displot()
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Pairplot()
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Categorical Scatter Plots
Stripplot()
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Swarmplot()
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Categorical Distribution Plots
Boxplot()
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Violinplot()
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Boxenplot()
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Categorical Estimate Plots
Pointplot()
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Barplot()
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Countplot()
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Catplot()
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Statistical Estimations
Error bars
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Estimating Regression Fits
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Multi-Plot Grids
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Advanced Seaborn
Plotting Univariate Distributions
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Plotting Bivariate Distributions
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Seaborn Python Case Study
Data Visualization Using Seaborn on Census Dataset
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SKT_DS_Lab L1_Seaborn
3 Learning Materials
Basic Level Programming on Basics of Seaborn Plotting
Write a program to add titles and labels to a Seaborn plot?
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Write a program to modify line styles and colors in Seaborn plot?
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Write a program to customize legends and markers in Seaborn plots?
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Basic Level Programming on Relational Plots
Basic Level Programming on Distributions Plots
Basic Level Programming on Categorical Scatter Plots
Basic Level Programming on Categorical Distribution Plots
SKT_DS_Lab L2_Seaborn
3 Learning Materials
Intermediate Level Programming on Basics of Seaborn Plotting
Write a program to use multiple line styles in one Seaborn plot?
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Write a program to apply dark theme to a seaborn plot?
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Intermediate Level Programming on Relational Plots
Write a program to create a grouped scatter plot using Seaborn
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Intermediate Level Programming on Distributions Plots
Intermediate Level Programming on Categorical Scatter Plots
Intermediate Level Programming on Categorical Distribution Plots
SKT_DS_Lab L3_Seaborn
3 Learning Materials
Advance Level Programming on Basics of Seaborn Plotting
Write a program to customize and save figures in high resolution in Seaborn?
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Write a program to customize a Seaborn plot with grids, axis labels and markers?
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Advance Level Programming on Relational Plots
Write a program to create a heatmap overlay for a relational plot in Seaborn?
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Advance Level Programming on Distributions Plots
Advance Level Programming on Categorical Scatter Plots
Advance Level Programming on Categorical Distribution Plots
SKT_DS_Theory_Plotly
14 Learning Materials
Introduction to plotly
Introduction to Plotly
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History and Evolution of Plotly
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Basic plot using plotly
Basic charts
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Chart Customization & Interactivity
Styling plotly
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Customize a plot using plotly
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Basic 2D Charts
Line Charts
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Scatter Plots
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Basic Bar Charts
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Grouped Bar Charts
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Stacked Bar Charts
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Pie chart
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Area Charts
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Box Plot
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Notched Boxplot
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SKT_DS_Theory_Statistics and Probability
59 Learning Materials
All About Data
Introduction to Data
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Case Study
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Properties of Data
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Data based on Structure & Application
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Types of Variables
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Relationship between Variables
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Introduction To Statistics
Introduction to Statistics
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History of Statistics
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Fundamental Elements of Statistics
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Statistical Thinking and Methods
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Describing Data with Graphs
Introduction to Describing Data with Graphs
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Graphs for Categorical Data
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Graphs for Numerical Data
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Scatter Plots
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Time Series Plots
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Seasonal Plots
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Trend
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Cyclic Variations
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Irregular movements
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Advanced Graphical Techniques
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Graphs For Compare Data
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Descriptive Statistics - Measures of Central Tendencies
Introduction to Measures of Central Tendencies
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Mean
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Arithmetic Mean
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Types of Mean
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Median
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Mode
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Skewness & Kurtosis
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Descriptive Statistics - Measure of Dispersion
Introduction to Dispersion
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Variance
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Standard Deviation
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Interpreting the Standard Deviation
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Range
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Data Analysis & Visualization Plots
Introduction to Outliers
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Univariate Outlier Detection
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Graphical Methods for Outlier Detection
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Robust Statistical Methods
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Multivariate Outlier Detection Methods
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Hypothesis Testing for Outlier Detection
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Impact of Outliers on Statistical Analysis
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Distribution Data & its Empirical Formula
Introduction to Distribution of Data
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Introduction Continuous Distribution
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Normal Distribution
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Central Limit Theorem
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Lognormal Distribution
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F Distribution
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Chi-square Distribution
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T - Distribution
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Weibull Distribution
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Limiting Distributions
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Bernoulli Distribution
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Binomial Distribution
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Geometric Distribution
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Uniform Distribution
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Frequency Distributions
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Non grouped Frequency Distributions
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Poisson Distribution
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Inferential Statistics- Hypothesis Testing
Introduction to Hypothesis Testing
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Z-test
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SKT_DS_Theory_SQL for Data Science
52 Learning Materials
Introduction to Databases & RDBMS
Understanding Databases
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Database Management System
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Relational Database
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Non- Relational Database
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History
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Installing SQL
Setting Up MySQL Environment
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Database Processing Paradigms
OLAP vs. OLTP
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Introduction to SQL
SQL Fundamentals and Introduction
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SQL Syntax & Basic Queries
SQL Syntax & Basic Queries
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SQL Data Types
Introduction to Data Types
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Numeric Data Types
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String Data Type
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Date and Time Data Type
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Binary Data Type
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Miscellaneous Data Type
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SQL Operators
Introduction to Operators
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Arithmetic Operators
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Comparison operators
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Logical operators
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Bitwise Operators
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Compound Operators
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String Operators
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Set Operators
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SQL Basics and Language Components
SQL Commands
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Data Definition Language Commands
CREATE Database
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USE Database
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DROP Database
External Link
ALTER Database
External Link
CREATE Table
External Link
RENAME Table
External Link
DROP Table
External Link
TRUNCATE Table
External Link
Data Manipulation Language Commands
Retrieving data from a table
External Link
Inserting data into a table
External Link
Updating existing data into a table
External Link
Deleting all records from a table
External Link
Data Query Language Commands
Introduction to Clauses
External Link
FROM Clause
External Link
WHERE Clause
External Link
WITH Clause
External Link
ORDER BY Clause
External Link
LIMIT Clause
External Link
SQL Functions
Introduction to MYSQL Functions
External Link
Numeric Functions
External Link
String Functions
External Link
Date Time Functions
External Link
Conversion Functions
External Link
User Defined Functions
External Link
SQL GROUP BY , HAVING & Aggregate Functions
SQL Aggregation
External Link
SQL Grouping - GROUP BY
External Link
SQL Grouping - HAVING
External Link
SQL Indexes
Creating Indexes
External Link
SKT_DS_Theory_Power BI
32 Learning Materials
Introduction to Power BI
Introduction to Business Intelligence
External Link
Introduction to Power BI
External Link
History of Power BI
External Link
Comparison of Power BI Version
External Link
Introduction to Building Blocks
External Link
Working with Power BI
Working with Power BI
External Link
Power Pivot and Data Sources
Power Pivot and Data Sources
External Link
Power Query for Data Transformation
Introduction to Power Query
External Link
Transformations
External Link
Data Transformations and Calculations
External Link
DAX
DAX
External Link
Basics of Data Modeling
External Link
Creating Calculated Columns (Basics)
External Link
DAX Time Intelligence
External Link
DAX Table Functions
External Link
Working with Filter Context (FILTER and ALL)
External Link
Overriding Filter Context
External Link
Semi-Additive Measures
External Link
Nested Row Context
External Link
Error Handling
External Link
Building PivotTables on Power Pivot
Creating Tabels And Matrices
External Link
Building Pivot Tables on Top of Power Pivot Tables
External Link
Using Pivot Charts
External Link
Introduction Power View
Introduction to Power View
External Link
Authoring Power View reports
From table to chart
External Link
Working with bubble charts
External Link
Slicers, cards and multiples
External Link
Adding maps to Power View
External Link
Working with multi-view reports
External Link
Improving Power Pivot models for Power View reporting
External Link
Deploying Power View reports
Saving Power View reports in document libraries
External Link
Power Pivot Gallery functionality
External Link
SKT_DS_Theory_ML-Supervised
104 Learning Materials
Course Overview
Curriculum Overview
External Link
Pre-Requisites
External Link
Introduction To Machine Learning
Introduction To Machine Learning
External Link
History of Machine Learning
External Link
Traditional Programming vs Machine Learning
External Link
What can go wrong with machine Learning?
External Link
AI vs ML vs DL
External Link
Data for Machine Learning
External Link
Machine Learning Approaches
External Link
Transfer Learning & Ensemble Learning
External Link
Machine Learning Workflow
Data Collection
External Link
Data Splitting and Preparation
External Link
Model Selection
External Link
Model Training & Evaluation
External Link
Model Optimization and Deployment
External Link
Model Monitoring and Maintenance
External Link
Supervised Learning
Supervised Learning
External Link
Classification & Regression
What is classification?
External Link
Binary Classification
External Link
Case Studies on Binary Classification
External Link
Multiclass classification
External Link
Regression and Case studies
External Link
Algorithms for Supervised Learning
Algorithms for Supervised Learning
External Link
Linear Regression
Introduction to Linear Regression
External Link
Simple Linear Regression
External Link
Case study for Simple Linear Regression (Salary Prediction)
Problem Statement- Salary Prediction
External Link
Data Collection
External Link
Data Understanding
External Link
Data Cleaning & Preprocessing
External Link
Exploratory Data Analysis (EDA)
External Link
Data Transformation & Data Splitting
External Link
Model Application
External Link
Introduction to Multiple Linear Regression
Multiple Linear Regression
External Link
Case study for Multiple Regression(Medical Insurance Cost)
Problem Statement-Medical Insurance Cost Prediction
External Link
Data Collection
External Link
Data Understanding
External Link
Data Cleaning & Preprocessing
External Link
Exploratory Data Analysis (EDA)
External Link
Data Transformation & Data Splitting
External Link
Model Application
External Link
Introduction to Polynomial Regression
Introduction to Polynomial Regression
External Link
Case study for Polynomial regression (Car Price Prediction)
Problem Statement
External Link
Data Collection
External Link
Data Understanding
External Link
Data Cleaning & Preprocessing
External Link
Exploratory Data Analysis
External Link
Transformation of data & Data splitting
External Link
Model Application
External Link
Assumptions of linear regression
External Link
Logistic Regression
Logistic Regression
External Link
Geometrical Intuition
External Link
Binary Logistic Regression
External Link
Case study for Binary logistic regression (Ad-click)
Problem Statement
External Link
Data Collection
External Link
Data Understanding
External Link
Data Cleaning & Preprocessing
External Link
Exploratory Data Analysis
External Link
Transformation of data & Data splitting
External Link
Model Application
External Link
Multinonial Logistic Regression
Introduction to Multinonial Logistic Regression
External Link
Case study for Multinomial logistic regression (iris)
Problem Statement
External Link
Data Collection
External Link
Data Understanding
External Link
Data Cleaning & Preprocessing
External Link
Exploratory Data Analysis
External Link
Transformation of data & Data splitting
External Link
Model Application
External Link
Assumptions of logistic regression
External Link
Naive Bayes
Introduction to Naive Bayes
External Link
Exploring Naive Bayes
External Link
NaiveBayes - Classifier & Regressor
External Link
Bayes Theorem
External Link
Types of Naive Bayes
External Link
Multinomial Naive Bayes
External Link
Bernoulli Naive Bayes
External Link
Parameter Estimation
External Link
Statistical Estimation Techniques
External Link
Smoothing techniques
External Link
Case Study on Naive Bayes(Heart Attack Prediction)
Problem Statement_Heart Attack Prediction
External Link
Data Collection
External Link
Data Understanding
External Link
Data Cleaning & Preprocessing
External Link
Exploratory Data Analysis(EDA)
External Link
Data Transformation
External Link
Feature selection & Data splitting
External Link
Model Application
External Link
KNN Algorithm
Introduction to KNN Algorithm
External Link
Exploring KNN Algorithm
External Link
Distance Metrics
External Link
Types of KNN Algorithms
External Link
KNN for Regression (California House price prediction)
Problem Statement- Housing Prices Prediction in California
External Link
Data Collection
External Link
Data Understanding
External Link
Data Cleaning & Preprocessing
External Link
Exploratory Data Analysis (EDA)
External Link
Data Splitting & Data Transformation
External Link
Model Application
External Link
KNN for Classification (Diabetes Prediction)
Problem Statement- Diabetes Prediction
External Link
Data Collection
External Link
Data Understanding
External Link
Data Cleaning & Preprocessing
External Link
Exploratory Data Analysis (EDA)
External Link
Data Splitting & Data Transformation
External Link
Model Application
External Link
SKT_DS_Lab L1_ML-supervised
1 Learning Materials
Basic level Program On Case study for simple linear regression
Apply a linear regression model so that Y is predicted based on X
External Link
Basic level Program On Case study for Multiple Linear regression
Basic level Program On Case study for Polynomial regression
Basic level Program On Case study for Binary logistic regression Basic level Program On
Basic level Program On Case study for Multinomial logistic regression
SKT_DS_Lab L2_ML-Supervised
1 Learning Materials
Intermediate Level Programming on Case study for Simple linear Regression
Analyze the diabetes dataset from scikit-learn of simple linear regression to predict
External Link
Intermediate Level Programming on Case study for Multiple regression
Intermediate Level Programming on Case study for Polynomial regression
Intermediate Level Programming on Case study for Binary logistic regression
Intermediate Level Programming on Case study for Multinomial logistic regression
SKT_DS_Lab L3_ML-Supervised
1 Learning Materials
Advance Level Programming on Case study for simple linear regression
Create a dataset with 1000 samples and 20 features using `sklearn.datasets.
External Link
SKT_DS_Theory_ML-Unsupervised
81 Learning Materials
Unsupervised Learning
Introduction to Unsupervised Learning
External Link
Types of Unsupervised Learning
External Link
Clustering
Introduction to Clustering
External Link
Types of clustering
External Link
Hierarchical Clustering
Hierarchical Clustering
External Link
Types of hierarchical clustering
External Link
Agglomerative Hierarchical Clustering
Agglomerative Hierarchical Clustering
External Link
Applications of Agglomerative Hierarchical Clustering
External Link
Distance measures
External Link
Linkage criteria
External Link
Dendrograms
External Link
Divisive Hierarchical Clustering
Divisive Hierarchical Clustering
External Link
Applications of Divisive Hierarchical Clustering
External Link
Comparing Agglomerative and Divisive Hierarchical Clustering
Comparing Agglomerative and Divisive Hierarchical Clustering
External Link
Case Study on Hierarchical Clustering
Problem Statement
External Link
Data Collection
External Link
Data Understanding
External Link
Data Cleaning and Preprocessing
External Link
Exploratory Data Analysis (EDA)
External Link
Implementing Hierarchical Clustering
External Link
K-Means Clustering
Introduction to K-Means Clustering
External Link
Applications of K-Means Clustering
External Link
Understanding K-means Clustering
External Link
Evaluating K-means Clustering-Using Silhouette Method
External Link
Evaluating K-means Clustering-Using Elbow Method
External Link
Evaluating K-means Clustering – Using the Gap Statistic
External Link
Advantages and Disadvantages Clustering Methods
External Link
Limitations of K-Means Clustering
External Link
Case Study on K-Means Clustering
Problem Statement
External Link
Data Collection
External Link
Data Understanding
External Link
Data Cleaning and Preprocessing
External Link
Exploratory Data Analysis (EDA) part-1
External Link
Exploratory Data Analysis (EDA) part-2
External Link
Implementing K-means Clustering
External Link
DBSCAN Algorithm
Introduction to DBSCAN Algorithm
External Link
Understanding DBSCAN Algorithm
External Link
Advantages and Disadvantages of DBSCAN
External Link
Case Study on DBSCAN Algorithm
Problem Statement
External Link
Data Collection
External Link
Data Understanding
External Link
Data Cleaning and Preprocessing
External Link
Exploratory Data Analysis (EDA)
External Link
Implementing DBSCAN Algorithm
External Link
Mean Shift clustering
Introduction to Mean Shift clustering
External Link
Understanding Density Estimation Techniques
External Link
Advantages and Disadvantages
External Link
Implementation of Mean Shift Clustering
Implementing Mean Shift clustering
External Link
Dimensionality Reduction Algorithm
Introduction to Dimensionality Reduction Algorithm
External Link
Types of dimensionality reduction techniques
External Link
Principal Component Analysis (PCA)
External Link
T-Distributed Stochastic Neighbor Embedding
External Link
Non-Negative Matrix Factrorization (NMF)
External Link
Independent Component Analysis(ICA)
External Link
Linear Discriminant Analysis (LDA)
External Link
Gaussian Mixture Models (GMM)
Introduction to Gaussian Mixture Models (GMM)
External Link
Case Study on Gaussian Mixture Models (GMM)
Data Collection
External Link
Data Understanding
External Link
Data Cleaning and Preprocessing
External Link
Implementing Gaussian Mixture Models
External Link
Association Rule Learning
Introduction to Association Rule Mining
External Link
Apriori Algorithm
External Link
FP-growth algorithm
External Link
Eclat Algorithm
External Link
Case Study on Association Rule Mining
Data Collection
External Link
Data Understanding
External Link
Data Cleaning and Preprocessing
External Link
Exploratory Data Analysis (EDA)
External Link
Implementing Association Rule Mining
External Link
Anomaly Detection
Introduction to Anomaly Detection
External Link
Unsupervised Anomaly Detection Techniques
External Link
Real-world Applications
External Link
Recommendation systems
Introduction to Recommendation Systems
External Link
Content-Based Recommendation Systems
External Link
Collaborative Recommendation Systems
External Link
Hybrid Recommendation Systems
External Link
Case Study on Recommendation systems
Data Collection
External Link
Data Understanding
External Link
Data Cleaning
External Link
Data Preprocessing
External Link
Implementing Recommendation Systems
External Link
SKT_DS_Lab L1_ML-Unsupervised
1 Learning Materials
Basic level Program On Hierarchical Clustering
Implement Hierarchical Clustering on a small dataset.
External Link
Basic level Program On K-Means Clustering
Basic level Program On Case Study on DBSCAN Algorithm
Basic level Program On Gaussian Mixture Models (GMM)
Basic level Program On Association Rule Mining
SKT_DS_Lab L2_ML-Unsupervised
1 Learning Materials
Intermediate Level Programming on Hierarchical Clustering
Compare Hierarchical Clustering with DBSCAN on noisy data.
External Link
Intermediate level Program on K-Means Clustering
Intermediate Level Programming on DBSCAN Algorithm
Intermediate Level Programming on Gaussian Mixture Models (GMM)
Intermediate Level Programming on Association Rule Mining
SKT_DS_Lab L3_ML-Unsupervised
1 Learning Materials
Advance Level Programming on Hierarchical Clustering
Perform Hierarchical Clustering on a large dataset (e.g., customer segmentation)
External Link
Advance Level Programming on K-Means Clustering
Advance Level Programming on DBSCAN Algorithm
Advance Level Programming on Gaussian Mixture Models (GMM)
Advance Level Programming on Association Rule Mining
Course Instructor
TBOCWWB
137 Courses • 108145 Students
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