The Data Analyst Course: Complete Data Analyst Bootcamp
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Subtitle Guide – Hướng dẫn thêm phụ đề
01 – Introduction to the Course
02 – Introduction to Data Analytics
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001 Introduction to the World of Business and Data
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002 Relevant Terms Explained
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003 Data Analyst Compared to Other Data Jobs
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004 Data Analyst Job Description
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005 Why Python
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03 – Setting up the Environment
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001 Introduction
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002 Programming Explained in a Few Minutes
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003 Jupyter - Introduction
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004 Jupyter - Installing Anaconda
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005 Jupyter - Intro to Using Jupyter
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006 Jupyter - Working with Notebook Files
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007 Jupyter - Using Shortcuts
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008 Jupyter - Handling Error Messages
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009 Jupyter - Restarting the Kernel
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04 – Python Basics
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001 Python Variables
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002 Types of Data - Numbers and Boolean Values
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003 Types of Data - Strings
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004 Basic Python Syntax - Arithmetic Operators
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005 Basic Python Syntax - The Double Equality Sign
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006 Basic Python Syntax - Reassign Values
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007 Basic Python Syntax - Add Comments
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008 Basic Python Syntax - Line Continuation
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009 Basic Python Syntax - Indexing Elements
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010 Basic Python Syntax - Indentation
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011 Operators - Comparison Operators
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012 Operators - Logical and Identity Operators
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013 Conditional Statements - The IF Statement
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014 Conditional Statements - The ELSE Statement
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015 Conditional Statements - The ELIF Statement
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016 Conditional Statements - A Note on Boolean Values
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017 Functions - Defining a Function in Python
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018 Functions - Creating a Function with a Parameter
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019 Functions - Another Way to Define a Function
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020 Functions - Using a Function in Another Function
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021 Functions - Combining Conditional Statements and Functions
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022 Functions - Creating Functions That Contain a Few Arguments
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023 Functions - Notable Built-in Functions in Python
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024 Sequences - Lists
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025 Sequences - Using Methods
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026 Sequences - List Slicing
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027 Sequences - Tuples
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028 Sequences - Dictionaries
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029 Iteration - For Loops
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030 Iteration - While Loops and Incrementing
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031 Iteration - Create Lists with the range() Function
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032 Iteration - Use Conditional Statements and Loops Together
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033 Iteration - Conditional Statements, Functions, and Loops
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034 Iteration - Iterating over Dictionaries
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05 – Fundamentals for Coding in Python
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001 Object-Oriented Programming (OOP)
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002 Modules, Packages, and the Python Standard Library
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003 Importing Modules
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004 Introduction to Using NumPy and pandas
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005 What is Software Documentation
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006 The Python Documentation
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06 – Mathematics for Python
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002 Scalars and Vectors
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003 Linear Algebra and Geometry
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004 Arrays in Python
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005 What Is a Tensor
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006 Adding and Subtracting Matrices
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007 Errors When Adding Matrices
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008 Transpose
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009 Dot Product of Vectors
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010 Dot Product of Matrices
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011 Why is Linear Algebra Useful
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07 – NumPy Basics
08 – Pandas – Basics
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001 Introduction to the pandas Library
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002 Installing and Running pandas
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003 Introduction to pandas Series
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004 Working with Attributes in Python
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005 Using an Index in pandas
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006 Label-based vs Position-based Indexing
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007 More on Working with Indices in Python
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008 Using Methods in Python - Part I
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009 Using Methods in Python - Part II
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010 Parameters vs Arguments
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011 the pandas Documentation
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012 Introduction to pandas DataFrames
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013 Creating DataFrames from Scratch - Part I
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014 Creating DataFrames from Scratch - Part II
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015 Additional Notes on Using DataFrames
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016 pandas Basics - Conclusion
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09 – Working with Text Files
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001 Working with Files in Python - An Introduction
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002 File vs File Object, Read vs Parse
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003 Structured vs Semi-Structured and Unstructured Data
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004 Data Connectivity through Text Files
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005 Principles of Importing Data in Python
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006 More on Text Files (.txt vs .csv)
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007 Fixed-width Files
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008 Common Naming Conventions Used in Programming
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009 Importing Text Files in Python ( open() )
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010 Importing Text Files in Python ( with open() )
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011 Importing .csv Files with pandas - Part I
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012 Importing .csv Files with pandas - Part II
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013 Importing .csv Files with pandas - Part III
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014 Importing Data with the index_col Parameter
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015 Importing Data with NumPy - .loadtxt() vs genfromtxt()
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016 Importing Data with NumPy - Partial Cleaning While Importing
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017 Importing Data with NumPy - Exercise
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018 Importing .json Files
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019 Prelude to Working with Excel Files in Python
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020 Working with Excel Data (the .xlsx Format)
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021 An Important Exercise on Importing Data in Python
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022 Importing Data with the pandas' Squeeze Parameter
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023 A Note on Importing Files in Jupyter
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024 Saving Your Data with pandas
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025 Saving Your Data with NumPy - np.save()
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026 Saving Your Data with NumPy - np.savez()
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027 Saving Your Data with NumPy - np.savetxt()
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028 Saving Your Data with NumPy - Exercise
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029 Working with Text Files - Conclusion
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10 – Working with Text Data
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001 Working with Text Data and Argument Specifiers
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002 Manipulating Python Strings
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003 Using Various Python String Methods - Part I
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004 Using Various Python String Methods - Part II
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005 String Accessors
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006 Using the .format() Method
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11 – Must-Know Python Tools
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001 Iterating Over Range Objects
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002 Nested For Loops - Introduction
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003 Triple Nested For Loops
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004 List Comprehensions
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005 Anonymous (Lambda) Functions
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12 – Data GatheringData Collection
13 – APIs (POST requests are not needed for this course)
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001 Overview of APIs
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002 GET and POST Requests
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003 Data Exchange Format for APIs JSON
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004 Introducing the Exchange Rates API
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005 Including Parameters in a GET Request
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006 More Functionalities of the Exchange Rates API
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007 Coding a Simple Currency Conversion Calculator
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008 iTunes API
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009 iTunes API Homework
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010 iTunes API Structuring and Exporting the Data
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011 Pagination GitHub API
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012 APIs Exercise
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14 – Data Cleaning and Data Preprocessing
15 – pandas Series
16 – pandas DataFrames
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001 A Revision to pandas DataFrames
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002 Common Attributes for Working with DataFrames
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003 Data Selection in pandas DataFrames
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004 Data Selection - Indexing with .iloc[]
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005 Data Selection - Indexing with .loc[]
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006 A Few Comments on Using .loc[] and .iloc[]
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17 – NumPy Fundamentals
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001 Indexing in NumPy
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002 Assigning Values in NumPy
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003 Elementwise Properties of Arrays
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004 Types of Data Supported by NumPy
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005 Characteristics of NumPy Functions Part 1
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006 Characteristics of NumPy Functions Part 2
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007 NumPy Fundamentals - Exercise
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18 – NumPy DataTypes
19 – Working with Arrays
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001 Basic Slicing in NumPy
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002 Stepwise Slicing in NumPy
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003 Conditional Slicing in NumPy
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004 Dimensions and the Squeeze Function
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005 Working with Arrays - Exercise
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20 – Generating Data with NumPy
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001 Arrays of 0s and 1s
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002 _like functions in NumPy
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003 A Non-Random Sequence of Numbers
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004 Random Generators and Seeds
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005 Basic Random Functions in NumPy
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006 Probability Distributions in NumPy
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007 Applications of Random Data in NumPy
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008 Generating Data with NumPy - Exercise
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21 – Statistics with NumPy
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001 Using Statistical Functions in NumPy
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002 Minimal and Maximal Values in NumPy
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003 Statistical Order Functions in NumPy
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004 Averages and Variance in NumPy
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005 Covariance and Correlation in NumPy
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006 Histograms in NumPy (Part 1)
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007 Histograms in NumPy (Part 2)
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008 NAN Equivalent Functions in NumPy
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009 Statistics with NumPy - Exercise
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22 – NumPy – Preprocessing
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001 Checking for Missing Values in Ndarrays
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002 Substituting Missing Values in Ndarrays
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003 Reshaping Ndarrays
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004 Removing Values from Ndarrays
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005 Sorting Ndarrays
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006 Argument Sort in NumPy
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007 Argument Where in NumPy
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008 Shuffling Ndarrays
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009 Casting Ndarrays
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010 Striping Values from Ndarrays
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011 Stacking Ndarrays
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012 Concatenating Ndarrays
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013 Finding Unique Values in Ndarrays
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23 – A Loan Data Example with NumPy
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001 Setting Up Introduction to the Practical Example
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002 Setting Up Importing the Data Set
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003 Setting Up Checking for Incomplete Data
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004 Setting Up Splitting the Dataset
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005 Setting Up Creating Checkpoints
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006 Manipulating Text Data Issue Date
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007 Manipulating Text Data Loan Status and Term
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008 Manipulating Text Data Grade and Sub Grade
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009 Manipulating Text Data Verification Status & URL
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010 Manipulating Text Data State Address
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011 Manipulating Text Data Converting Strings and Creating a Checkpoint
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012 Manipulating Numeric Data Substitute Filler Values
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013 Manipulating Numeric Data Currency Change – The Exchange Rate
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014 Manipulating Numeric Data Currency Change - From USD to EUR
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015 Completing the Dataset
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24 – The Absenteeism Exercise – Introduction
25 – Solution to the Absenteeism Exercise
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001 How to Complete the Absenteeism Exercise
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002 Eyeball Your Data First
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003 Note Programming vs the Rest of the World
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004 Using a Statistical Approach to Solve Our Exercise
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005 Dropping the 'ID' Column
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006 Analysis of the 'Reason for Absence' Column
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007 Splitting the Reasons for Absence into Multiple Dummy Variables
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008 Working with Dummy Variables - A Statistical Perspective
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009 Grouping the Reason for Absence Columns
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010 Concatenating Columns in a pandas DataFrame
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011 Reordering Columns in a DataFrame
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012 Working on the 'Date' Column
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013 Extracting the Month Value from the 'Date' Column
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014 Creating the 'Day of the Week' Column
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015 Understanding the Meaning of 5 More Columns
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016 Modifying the 'Education' Column
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017 Final Remarks on the Absenteeism Exercise
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26 – Data Visualization
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001 What Is Data Visualization and Why Is It Important
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002 Why Learn Data Visualization
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003 Choosing the Right Visualization – What Are Some Popular Approaches and Framewor
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004 Introduction into Colors and Color Theory
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005 Bar Chart - Introduction - General Theory and Getting to Know the Dataset
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006 Bar Chart - How to Create a Bar Chart Using Python
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007 Bar Chart – Interpreting the Bar Graph. How to Make a Good Bar Graph
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008 Pie Chart - Introduction - General Theory and Dataset
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009 Pie Chart - How to Create a Pie Chart Using Python
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010 Pie Chart – Interpreting the Pie Chart
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011 Pie Chart - Why You Should Never Create a Pie Graph
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012 Stacked Area Chart - Introduction - General Theory. Getting to Know the Dataset
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013 Stacked Area Chart - How to Create a Stacked Area Chart Using Python
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014 Stacked Area Chart - Interpreting the Stacked Area Graph
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015 Stacked Area Chart - How to Make a Good Stacked Area Chart
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016 Line Chart - Introduction - General Theory. Getting to Know the Dataset
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017 Line Chart - How to Create a Line Chart in Python
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018 Line Chart - Interpretation
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019 Line Chart - How to Make a Good Line Chart
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020 Histogram - Introduction - General Theory. Getting to Know the Dataset
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021 Histogram - How to Create a Histogram Using Python
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022 Histogram – Interpreting the Histogram
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023 Histogram – Choosing the Number of Bins in a Histogram
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024 Histogram - How to Make a Good Histogram
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025 Scatter Plot - Introduction - General Theory. Getting to Know the Dataset
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026 Scatter Plot - How to Create a Scatter Plot Using Python
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027 Scatter Plot – Interpreting the Scatter Plot
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028 Scatter Plot - How to Make a Good Scatter Plot
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029 Regression Plot - Introduction - General Theory. Getting to Know the Dataset
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030 Regression Plot - How to Create a Regression Scatter Plot Using Python
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031 Regression Plot – Interpreting the Regression Scatter Plot
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032 Regression Plot - How to Make a Good Regression Plot
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033 Bar and Line Chart - Introduction - General Theory. Getting to Know the Dataset
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034 Bar and Line Chart - How to Create a Combination Bar and Line Graph Using Python
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035 Bar and Line Chart – Interpreting the Combination Bar and Line Graph
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036 Bar and Line Chart – How to Make a Good Bar and Line Graph
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037 Data Visualization - Exercise
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27 – Conclusion
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