Last updated 4/2021 MP4 | Video: h264, 1280x720 | 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.
