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Python for Data Analysis & Data Science

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Hands-On Python Course for Data Analytics - Beginner to Advanced Level


1 - Introduction
  • 1 -Course Introduction
  • 2 -Course Structure
  • 3 -Course RequirementPrerequisite
  • 4 -Udemy Review System
  • 5 -Application Download & Installation
  • 6 -Install Python on Mac
  • 7 -Install Python on Windows
  • 8 -Adjusting Playback Rate
  • 9 -Using Jupyter Notebook
  • 10 -Python Files.zip
  • 10 -Python Import Files

  • 2 - Data Types & Variables
  • 1 -Section Introduction
  • 2 -Python as a Programming Language
  • 3 -Data Types
  • 4 -Variables

  • 3 - Operators and Numbers in Python
  • 1 -Section Introduction
  • 2 -Operators
  • 3 -Using Numbers in Python

  • 4 - Using Strings in Python
  • 1 -Section Introduction
  • 2 -String Data Type
  • 3 -String Methods
  • 4 -String Operators

  • 5 - Slicing, Format Functions & Casting
  • 1 -Section Introduction
  • 2 -Slice
  • 3 -Format Function
  • 4 -Change Data Types Using Functions - Casting
  • 5 -Project 1 - Create a Bill Payment System

  • 6 - List - Data Structure
  • 1 -Section Introduction
  • 2 -List
  • 3 -List Methods

  • 7 - Control Flow
  • 1 -Section Introduction
  • 2 -IF Statement
  • 3 -Project 2 - Guessing Game - Part I
  • 4 -While Loops
  • 5 -Project 2 - Guessing Game - Part II
  • 6 -For Loops
  • 7 -Break & Continue Statement

  • 8 - Tuple - Data Structure
  • 1 -Section Introduction
  • 2 -Tuples

  • 9 - Dictionaries - Data Structure
  • 1 -Section Introduction
  • 2 -Dictionaries
  • 3 -Dictionary Methods
  • 4 -Create a List inside a Dictionary
  • 5 -Project 3 - Concert Tickets

  • 10 - Functions
  • 1 -Section Introduction
  • 2 -Pythons Built-In Functions
  • 3 -User Defined Functions
  • 4 -Variable Scope
  • 5 -Packing & Unpacking Data - ARGS
  • 6 -Packing & Unpacking Data - KWARGS

  • 11 - Series
  • 1 -Section Introduction
  • 2 -Introduction to Series
  • 3 -Create Series from a List
  • 4 -Create Series from a Tuple
  • 5 -Create Series from a Dictionary
  • 6 -Create Series from a CSV Dataset
  • 7 -Head & Tail Methods on a Series
  • 8 -Count & Describe Methods on a Series
  • 9 -Sort Values Method
  • 10 -Inplace Parameter
  • 11 -Sort Index Method
  • 12 -Retrieve Records from a Series by Index Position
  • 13 -Retrieve Records from a Series by Index Label
  • 14 -Use Get Method to Retrieve Records from a Series
  • 15 -Using Attributes on a Series

  • 12 - Dataframe Part A
  • 1 -Section Introduction
  • 2 -Introduction to Dataframe
  • 3 -Create Dataframe from a List
  • 4 -Create Dataframe from a Dictionary of List
  • 5 -Create Dataframe from an Imported File
  • 6 -Retrieve Single Column from a Dataframe
  • 7 -Retrieve Multiple Columns from a Dataframe
  • 8 -Add a New Column to a Dataframe
  • 9 -Delete Column(s) from a Dataframe
  • 10 -Find the Sum of Null Values
  • 11 -Drop Rows with Missing Values
  • 12 -Replace Missing Value Using FILLNA Method
  • 13 -Broadcasting Operation
  • 14 -Count Unique Occurrences Using VALUE COUNT Method
  • 15 -Sort Values in Dataframe Using SORT VALUES Method
  • 16 -Sort Dataframe by Index Using SORT INDEX Method
  • 17 -Remove and Replace Missing Values
  • 18 -Change Data Types Using ASTYPE Method

  • 13 - Dataframe Part B
  • 1 -Optimizing Dataset
  • 2 -Refine Records By a Condition
  • 3 -Refine Records Using Multiple Conditions - AND Condition
  • 4 -Select Specific Columns After a Condition
  • 5 -Refine Dataframe Using Multiple Conditions - OR Condition
  • 6 -Use The ISIN Method to Retrieve Row Having Specific Values
  • 7 -Return Null and Not Null Values Using ISNULL AND NOTNULL Method
  • 8 -Return Values Within Range Using BETWEEN Method
  • 9 -Retrieve Duplicate Records Using DUPLICATED Method
  • 10 -Delete Duplicate Records Using DROP DUPLICATES Method
  • 11 -UNIQUE and NUNIQUE Methods

  • 14 - Dataframe Part C
  • 1 -Optimizing New Dataset
  • 2 -SET IDEX and RESET INDEX Method
  • 3 -Retrieve Rows by Index Label Using LOC[ ] Accessor
  • 4 -Retrieve Rows by Index Position Using ILOC[ ] Accessor
  • 5 -Return Specific Index Label Values
  • 6 -Change Values in a Cell
  • 7 -Change Values of Unique Groups
  • 8 -Change Label or Column Name Using RENAME Method
  • 9 -Delete Rows or Columns Using DROP Method
  • 10 -Retrieve Random Sample From a Dataframe
  • 11 -Retrieve Smallest or Largest Values
  • 12 -Rank Values Using The RANK Method
  • 13 -Create a Copy of Dataset

  • 15 - Manipulating Text Data in a Dataframe
  • 1 -Section Introduction
  • 2 -Optimizing Text Dataset
  • 3 -Change Text Case - Upper, Lower, Title, capitalize Method
  • 4 -Remove White Spaces - Lstrip, Rstrip, Strip Method
  • 5 -Replace Characters in a Column
  • 6 -Filtering Dataframe for Specific Values - CONTAINS Method
  • 7 -Split String Column - Part A
  • 8 -Split String Column - Part B

  • 16 - Multi Index in a Dataframe
  • 1 -Section Introduction
  • 2 -Create Multi-Index
  • 3 -Sort Multi-Index
  • 4 -Retrieve Rows from Multi-Index
  • 5 -Stack & Unstack Method
  • 6 -Aggregate Values Using PIVOT TABLE Method

  • 17 - Groupby Object
  • 1 -Section Introduction
  • 2 -Groupby Object I
  • 3 -Groupby Object II
  • 4 -GET GROUP method
  • 5 -Group by Multiple Columns
  • 6 -Use The AGG Method to pass Different Operations
  • 7 -For Loop & Groupby Object

  • 18 - Data Relationship
  • 1 -Section Introduction
  • 2 -Data Relationship
  • 3 -Normalization
  • 4 -Introduction to JOIN
  • 5 -Inner Join I
  • 6 -Inner Join II
  • 7 -Left Join
  • 8 -Right Join
  • 9 -Outer Join
  • 10 -Merge More than 2 Dataframes
  • 11 -Many to Many Data Relationship
  • 12 -Left On & Right On
  • 13 -Combine Dataframes Using PD.CONCAT

  • 19 - Dates & Times Dataset
  • 1 -Section Introduction
  • 2 -Dates & Times
  • 3 -Pandas Timestamp Object
  • 4 -TO DATETIME Method
  • 5 -PD.DATE RANGE Method
  • 6 -PD.DATE RANGE II
  • 7 -DT.Accessor
  • 8 -Format Datetime Objects with DT.STRFTIME Method
  • 9 -DT.STRFTIME Method
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    مدت زمان: 814 دقیقه
    تاریخ انتشار: ۲۰ اردیبهشت ۱۴۰۴
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