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Python and Data Science from Scratch With RealLife Exercises

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Python Data Science with Python programming, NumPy, Pandas, Matplotlib and dive into Data Science with Python Projects


1 - Data Science Python is Easy to Learn with Python Python Data Science Pandas
  • 1 - Python Is The New King and Pandas and Numpy Are So Cute
  • 2 - FAQ regarding Data Science Numpy Pandas.html
  • 3 - Project Files and Course Documents for Python Data Science Course.html
  • 4 - FAQ regarding Python Numpy Pandas.html

  • 2 - Data Science Setting Up Python for Mac and Windows
  • 5 - Installing Anaconda Distribution For MAC
  • 6 - Installing Anaconda Distribution For Windows
  • 7 - Installing Python and PyCharm For MAC
  • 8 - Installing Python and PyCharm For Windows
  • 9 - Installing Jupyter Notebook For MAC
  • 10 - Installing Jupyter Notebook For Windows

  • 3 - If there are variables there is Python
  • 11 - What is a variable in Python

  • 4 - Math is not so confusing with Python 3 now
  • 12 - Numbers and Math Operators with example

  • 5 - Strings in Python Programming
  • 13 - String Operations and Useful String Methods
  • 14 - Data Type Conversion
  • 15 - Exercise Company Email Generator

  • 6 - Conditionals in Python
  • 16 - Conditionals in Python
  • 17 - bool Function in Python
  • 18 - Comparison and Logical Operators in Python
  • 19 - If Statements in Python
  • 20 - Exercise Calculator in Python
  • 21 - Exercise User Login in Python

  • 7 - Loops in Python
  • 5 - Data Science Python Quiz.html
  • 6 - Python Data Science Quiz.html
  • 22 - Loops in Python
  • 23 - While Loops in Python
  • 24 - For Loops in Python
  • 25 - Range Function in Python
  • 26 - Control Statements in Python
  • 27 - Exercise Perfect Numbers in Python
  • 28 - Exercise User Login with Loops in Python

  • 8 - Functions in Python Bootcamp
  • 29 - Functions in Python Programming
  • 30 - Create A New Function and Function Calls
  • 31 - Return Statement in Python
  • 32 - Lambda Functions in Python
  • 33 - Exercise Finding Prime Number

  • 9 - Modules in Python 3
  • 34 - Logic of Using Modules in Python
  • 35 - How It is Work in Python
  • 36 - Create A New Module in Python
  • 37 - Exercise Number Game in Python

  • 10 - Lists in Data Science Python
  • 38 - Lists and List Operations in Python
  • 39 - List Methods in Python
  • 40 - List Comprehensions in Python
  • 41 - Data Science Python Exercise Fibonacci Numbers
  • 42 - Data Science Ptyhon Exercise Merging Name and Surname

  • 11 - Tuples in Python Programming
  • 43 - Tuples

  • 12 - Data Science Python Dictionaries
  • 44 - Dictionaries in Python Data Science
  • 45 - Dictionary Comprehensions in Python Data Science
  • 46 - Data Science Python Exercise Letter Counter
  • 47 - Data Science Python Exercise Word Counter

  • 13 - Exceptions in Python Programming
  • 48 - What is Exception
  • 49 - Exception Handling in Python Programming
  • 50 - Exercise if Number

  • 14 - Python Programming Files
  • 51 - Python Programming Files
  • 52 - File Operations in Python Data Science
  • 53 - Chelsea.txt
  • 53 - Exercise Team Building
  • 54 - Exercise Overlap

  • 15 - Python 3 Sets
  • 55 - Sets and Set Operations and Methods
  • 56 - Set Comprehensions in Python Programming

  • 16 - Object Oriented Programming OOP
  • 57 - Logic of OOP
  • 58 - Constructor in OOP
  • 59 - Methods in OOP
  • 60 - Inheritance in OOP
  • 61 - Overriding and Overloading in OOP

  • 17 - Data Science Python Project Project
  • 62 - Project Remote Controller Application

  • 18 - Python For Data Science In Foreign Lands Data Science
  • 15 - Python Data Science Quiz.html
  • 63 - What Is Data Science
  • 64 - Data Literacy

  • 19 - Using Numpy for Data Manipulation
  • 65 - Introduction to NumPy Library
  • 66 - Notebook Project Files Link regarding NumPy Python Programming Language Library.html
  • 67 - The Power of NumPy
  • 68 - 6 Article Advice And Links about Numpy Numpy Pyhon.html
  • 69 - Creating NumPy Array with The Array Function
  • 70 - Creating NumPy Array with Zeros Function
  • 71 - Creating NumPy Array with Ones Function
  • 72 - Creating NumPy Array with Full Function
  • 73 - Creating NumPy Array with Arange Function
  • 74 - Creating NumPy Array with Eye Function
  • 75 - Creating NumPy Array with Linspace Function
  • 76 - Creating NumPy Array with Random Function
  • 77 - Properties of NumPy Array
  • 78 - Reshaping a NumPy Array Reshape Function
  • 79 - Identifying the Largest Element of a Numpy Array
  • 80 - Detecting Least Element of Numpy Array Min Ar
  • 81 - Concatenating Numpy Arrays Concatenate Functio
  • 82 - Splitting OneDimensional Numpy Arrays The Split
  • 83 - Splitting TwoDimensional Numpy Arrays Split
  • 84 - Sorting Numpy Arrays Sort Function
  • 85 - Indexing Numpy Arrays
  • 86 - Slicing OneDimensional Numpy Arrays
  • 87 - Slicing TwoDimensional Numpy Arrays
  • 88 - Assigning Value to OneDimensional Arrays
  • 89 - Assigning Value to TwoDimensional Array
  • 90 - Fancy Indexing of OneDimensional Arrrays
  • 91 - Fancy Indexing of TwoDimensional Arrrays
  • 92 - Combining Fancy Index with Normal Indexing
  • 93 - Combining Fancy Index with Normal Slicing
  • 94 - Operations with Comparison Operators
  • 95 - Arithmetic Operations in Numpy
  • 96 - Statistical Operations in Numpy
  • 97 - Solving SecondDegree Equations with NumPy

  • 20 - Optional Recap Exercises and Bonus Info from the Numpy Library
  • 98 - What is Numpy
  • 99 - Array and Features in Numpy
  • 100 - Array Operators in Numpy
  • 101 - Indexing and Slicing in Numpy
  • 102 - Numpy Exercises

  • 21 - Pandas Using Pandas for Data Manipulation
  • 103 - Introduction to Pandas Library
  • 104 - Pandas Project Files Link.html
  • 105 - Creating a Pandas Series with a List
  • 106 - Creating a Pandas Series with a Dictionary
  • 107 - Creating Pandas Series with NumPy Array
  • 108 - Object Types in Series
  • 109 - Examining the Primary Features of the Pandas Series
  • 110 - Most Applied Methods on Pandas Series
  • 111 - Indexing and Slicing Pandas Series
  • 112 - Creating Pandas DataFrame with List
  • 113 - Creating Pandas DataFrame with NumPy Array
  • 114 - Creating Pandas DataFrame with Dictionary
  • 115 - Examining the Properties of Pandas DataFrames
  • 116 - Element Selection Operations in Pandas DataFrames Lesson 1
  • 117 - Element Selection Operations in Pandas DataFrames Lesson 2
  • 118 - Top Level Element Selection in Pandas DataFrames Lesson 1
  • 119 - Top Level Element Selection in Pandas DataFrames Lesson 2
  • 120 - Top Level Element Selection in Pandas DataFrames Lesson 3
  • 121 - Element Selection with Conditional Operations in Pandas Data Frames
  • 122 - Adding Columns to Pandas Data Frames
  • 123 - Removing Rows and Columns from Pandas Data frames
  • 124 - Null Values in Pandas Dataframes
  • 125 - Dropping Null Values Dropna Function
  • 126 - Filling Null Values Fillna Function
  • 127 - Setting Index in Pandas DataFrames
  • 128 - MultiIndex and Index Hierarchy in Pandas DataFrames
  • 129 - Element Selection in MultiIndexed DataFrames
  • 130 - Selecting Elements Using the xs Function in MultiIndexed DataFrames
  • 131 - Concatenating Pandas Dataframes Concat Function
  • 132 - Merge Pandas Dataframes Merge Function Lesson 1
  • 133 - Merge Pandas Dataframes Merge Function Lesson 2
  • 134 - Merge Pandas Dataframes Merge Function Lesson 3
  • 135 - Merge Pandas Dataframes Merge Function Lesson 4
  • 136 - Joining Pandas Dataframes Join Function
  • 137 - Loading a Dataset from the Seaborn Library
  • 138 - Examining the Data Set 1
  • 139 - Aggregation Functions in Pandas DataFrames
  • 140 - Examining the Data Set 2
  • 141 - Coordinated Use of Grouping and Aggregation Functions in Pandas Dataframes
  • 142 - Advanced Aggregation Functions Aggregate Function
  • 143 - Advanced Aggregation Functions Filter Function
  • 144 - Advanced Aggregation Functions Transform Function
  • 145 - Advanced Aggregation Functions Apply Function
  • 146 - Examining the Data Set 3
  • 147 - Pivot Tables in Pandas Library
  • 148 - Accessing and Making Files Available
  • 149 - Data Entry with Csv and Txt Files
  • 150 - Data Entry with Excel Files
  • 151 - Outputting as an CSV Extension
  • 152 - Outputting as an Excel File

  • 22 - Optional Recap Exercises and Bonus Info from the Pandas Library
  • 20 - DATA SCIENCE Quiz.html
  • 153 - What is Pandas
  • 154 - Series and Features in Pandas
  • 155 - Data Frame attributes and Methods Part I in Pandas
  • 156 - Data Frame attributes and Methods Part II in Pandas
  • 157 - Data Frame attributes and Methods Part III in Pandas
  • 158 - Multi Index in Pandas
  • 159 - Groupby Operations in Pandas
  • 160 - Missing Data and Data Munging Part I in Pandas
  • 161 - Missing Data and Data Munging Part II in Pandas
  • 162 - How We Deal with Missing Data
  • 163 - Combining Data Frames Part I in Pandas
  • 164 - Combining Data Frames Part II in Pandas
  • 165 - Sales.rar
  • 165 - Work with Dataset Files in Pandas

  • 23 - Data Visualization
  • 166 - What is Matplotlib
  • 167 - Using Matplotlib
  • 168 - Pyplot Pylab Matplotlib in Data visualization
  • 169 - Figure Subplot and Axes in Data visualization
  • 170 - Figure Customization in Data visualization
  • 171 - Plot Customization in Data visualization

  • 24 - Python For Data Science HandsOn Projects
  • 172 - Analyse Data With Different Data Sets Titanic Project
  • 172 - Project-I.rar
  • 173 - Titanic Project Answers
  • 173 - answers.zip
  • 174 - Project II Bike Sharing
  • 174 - Project-II.rar
  • 175 - Bike Sharing Project Answers
  • 175 - Project-II-answers.rar
  • 176 - Project III Housing and Property Sales
  • 176 - Project-III.rar
  • 177 - Answer for Housing and Property Sales Project
  • 177 - answers.zip
  • 178 - Project IV English Premier League
  • 178 - Project-IV.rar
  • 179 - Answers for English Premier League Project
  • 179 - answers.zip

  • 25 - Extra
  • 180 - Python and Data Science from Scratch With RealLife Exercises.html
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