Python For Data Analysis – Beginner To Advanced Level

Last updated 4/2021
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 4.46 GB | Duration: 13h 48m
Hands-On Python Course for Data Analytics

What you’ll learn
Data Exploration & Manipulation using Python
Pandas Library
Data Analysis in Dataframe
Data Structures
Data Relationships
Functions
Working with Dates and Times Values
Series
Dictionaries
Tuples
Lists
…..and a lot more
Requirements
No course requirement or prerequisites
Description
Apply Coupon: 36E1379D6CBF97812139Python is the fastest growing Data Analytics Programming Languages. This course takes you from knowing nothing about Python to becoming an expert analyzing data in Python. You will also learn about standard Python which is relevant for anyone who needs to know Python for other purposes like Web Development, Software Development e.t.c. Knowing Python is incredibly important if you are looking into a career in any data related field.This course is designed to equip you with what you need to be successful learning Python:Hands-on code along structure.Work on multiple projects.Lots of practice exercises and task which solidifies your knowledge of each lessons.Quizzes on sections covered.Replicate real life scenarios and coding in Jupyter Notebook.IS THIS YOU ?Looking to work with data personally or professionally?Starting or transitioning into a career as a Data Analyst, Data Scientist, Business Analyst, Report Analyst, ETL Specialist, BI Consultant, Data Engineer, or any data related field? Then you need to learn Python. Also, if you are going into the field of Web Application & Internet development, Artificial Intelligence, Cybersecurity, Web Testing; it is imperative that you learn Python.Course Requirement or PrerequisitesThis course does not require any prior knowledge or specific academic background. The only requirement is having a laptop or desktop computer. All applications necessary for learning the course would be downloaded free from the internet.
Overview
Section 1: Introduction
Lecture 1 Course Introduction
Lecture 2 Course Structure
Lecture 3 Course Requirement/Prerequisite
Lecture 4 Application Download & Installation
Lecture 5 Install Python on Mac
Lecture 6 Install Python on Windows
Lecture 7 Adjusting Playback Rate
Lecture 8 Using Jupyter Notebook
Lecture 9 Python Import Files
Section 2: Data Types & Variables
Lecture 10 Section Introduction
Lecture 11 Python as a Programming Language
Lecture 12 Data Types
Lecture 13 Variables
Section 3: Operators and Numbers in Python
Lecture 14 Section Introduction
Lecture 15 Operators
Lecture 16 Using Numbers in Python
Section 4: Using Strings in Python
Lecture 17 Section Introduction
Lecture 18 String Data Type
Lecture 19 String Methods
Lecture 20 String Operators
Section 5: Slicing, Format Functions & Casting
Lecture 21 Section Introduction
Lecture 22 Slice
Lecture 23 Format Function
Lecture 24 Change Data Types Using Functions – Casting
Lecture 25 Project 1 – Create a Bill Payment System
Section 6: List – Data Structure
Lecture 26 Section Introduction
Lecture 27 List
Lecture 28 List Methods
Section 7: Control Flow
Lecture 29 Section Introduction
Lecture 30 IF Statement
Lecture 31 Project 2 – Guessing Game – Part I
Lecture 32 While Loops
Lecture 33 Project 2 – Guessing Game – Part II
Lecture 34 For Loops
Lecture 35 Break & Continue Statement
Section 8: Tuple – Data Structure
Lecture 36 Section Introduction
Lecture 37 Tuples
Section 9: Dictionaries – Data Structure
Lecture 38 Section Introduction
Lecture 39 Dictionaries
Lecture 40 Dictionary Methods
Lecture 41 Create a List inside a Dictionary
Lecture 42 Project 3 – Concert Tickets
Section 10: Functions
Lecture 43 Section Introduction
Lecture 44 Python’s Built-In Functions
Lecture 45 User Defined Functions
Lecture 46 Variable Scope
Lecture 47 Packing & Unpacking Data – ARGS
Lecture 48 Packing & Unpacking Data – KWARGS
Section 11: Series
Lecture 49 Section Introduction
Lecture 50 Introduction to Series
Lecture 51 Create Series from a List
Lecture 52 Create Series from a Tuple
Lecture 53 Create Series from a Dictionary
Lecture 54 Create Series from a CSV Dataset
Lecture 55 Head & Tail Methods on a Series
Lecture 56 Count & Describe Methods on a Series
Lecture 57 Sort_Values Method
Lecture 58 Inplace Parameter
Lecture 59 Sort_Index Method
Lecture 60 Retrieve Records from a Series by Index Position
Lecture 61 Retrieve Records from a Series by Index Label
Lecture 62 Use Get_Method to Retrieve Records from a Series
Lecture 63 Using Attributes on a Series
Section 12: Dataframe Part A
Lecture 64 Section Introduction
Lecture 65 Introduction to Dataframe
Lecture 66 Create Dataframe from a List
Lecture 67 Create Dataframe from a Dictionary of List
Lecture 68 Create Dataframe from an Imported File
Lecture 69 Retrieve Single Column from a Dataframe
Lecture 70 Retrieve Multiple Columns from a Dataframe
Lecture 71 Add a New Column to a Dataframe
Lecture 72 Delete Column(s) from a Dataframe
Lecture 73 Find the Sum of Null Values
Lecture 74 Drop Rows with Missing Values
Lecture 75 Replace Missing Value Using FILLNA Method
Lecture 76 Broadcasting Operation
Lecture 77 Count Unique Occurrences Using VALUE_COUNT Method
Lecture 78 Sort Values in Dataframe Using SORT_VALUES Method
Lecture 79 Sort Dataframe by Index Using SORT_INDEX Method
Lecture 80 Remove and Replace Missing Values
Lecture 81 Change Data Types Using ASTYPE Method
Section 13: Dataframe Part B
Lecture 82 Optimizing Dataset
Lecture 83 Refine Records By a Condition
Lecture 84 Refine Records Using Multiple Conditions – AND Condition
Lecture 85 Select Specific Columns After a Condition
Lecture 86 Refine Dataframe Using Multiple Conditions – OR Condition
Lecture 87 Use The ISIN Method to Retrieve Row Having Specific Values
Lecture 88 Return Null and Not Null Values Using ISNULL AND NOTNULL Method
Lecture 89 Return Values Within Range Using BETWEEN Method
Lecture 90 Retrieve Duplicate Records Using DUPLICATED Method
Lecture 91 Delete Duplicate Records Using DROP_DUPLICATES Method
Lecture 92 UNIQUE and NUNIQUE Methods
Section 14: Dataframe Part C
Lecture 93 Optimizing New Dataset
Lecture 94 SET_IDEX and RESET_INDEX Method
Lecture 95 Retrieve Rows by Index Label Using LOC[ ] Accessor
Lecture 96 Retrieve Rows by Index Position Using ILOC[ ] Accessor
Lecture 97 Return Specific Index Label Values
Lecture 98 Change Values in a Cell
Lecture 99 Change Values of Unique Groups
Lecture 100 Change Label or Column Name Using RENAME Method
Lecture 101 Delete Rows or Columns Using DROP Method
Lecture 102 Retrieve Random Sample From a Dataframe
Lecture 103 Retrieve Smallest or Largest Values
Lecture 104 Rank Values Using The RANK Method
Lecture 105 Create a Copy of Dataset
Section 15: Manipulating Text Data in a Dataframe
Lecture 106 Section Introduction
Lecture 107 Optimizing Text Dataset
Lecture 108 Change Text Case – Upper, Lower, Title, capitalize Method
Lecture 109 Remove White Spaces – Lstrip, Rstrip, Strip Method
Lecture 110 Replace Characters in a Column
Lecture 111 Filtering Dataframe for Specific Values – CONTAINS Method
Lecture 112 Split String Column – Part A
Lecture 113 Split String Column – Part B
Section 16: Multi Index in a Dataframe
Lecture 114 Section Introduction
Lecture 115 Create Multi-Index
Lecture 116 Sort Multi-Index
Lecture 117 Retrieve Rows from Multi-Index
Lecture 118 Stack & Unstack Method
Lecture 119 Aggregate Values Using PIVOT_TABLE Method
Section 17: Groupby Object
Lecture 120 Section Introduction
Lecture 121 Groupby Object I
Lecture 122 Groupby Object II
Lecture 123 GET_GROUP method
Lecture 124 Group by Multiple Columns
Lecture 125 Use The AGG Method to pass Different Operations
Lecture 126 For Loop & Groupby Object
Section 18: Data Relationship
Lecture 127 Section Introduction
Lecture 128 Data Relationship
Lecture 129 Normalization
Lecture 130 Introduction to JOIN
Lecture 131 Inner Join I
Lecture 132 Inner Join II
Lecture 133 Left Join
Lecture 134 Right Join
Lecture 135 Outer Join
Lecture 136 Merge More than 2 Dataframes
Lecture 137 Many to Many Data Relationship
Lecture 138 Left_On & Right_On
Lecture 139 Combine Dataframes Using PD.CONCAT
Section 19: Dates & Times Dataset
Lecture 140 Section Introduction
Lecture 141 Dates & Times
Lecture 142 Pandas Timestamp Object
Lecture 143 TO_DATETIME Method
Lecture 144 PD.DATE_RANGE Method
Lecture 145 PD.DATE_RANGE II
Lecture 146 DT.Accessor
Lecture 147 Format Datetime Objects with DT.STRFTIME Method
Lecture 148 DT.STRFTIME Method
Section 20: Import & Export Datasets
Lecture 149 Section Introduction
Lecture 150 Import Dataset from URL
Lecture 151 Export Dataset as Files from Pandas
Section 21: Conclusion
Lecture 152 Conclusion
Anyone who wants to learn Python from the beginning to becoming highly proficient.,Looking into a career in any data related field – Data Scientist, Data Analyst, Business Analyst, Database Administration, BI Analyst, Artificial Intelligence e.t.c.,Looking into a career in any data related field – Data Scientist, Data Analyst, Business Analyst, BI Developer, Report Analyst e.t.c.

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