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Subtitle Guide – Hướng dẫn thêm phụ đề

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Set up subtitle for video – Hướng dẫn thêm phụ đề

01 – Introduction to the Course

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001 A Practical Example – What Will You Learn in This Course

002 Download All Resources

02 – Introduction to Data Analytics

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001 Introduction to the World of Business and Data

002 Relevant Terms Explained

003 Data Analyst Compared to Other Data Jobs

004 Data Analyst Job Description

03 – Setting up the Environment

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001 Introduction

002 Programming Explained in a Few Minutes

003 Jupyter – Introduction

004 Jupyter – Installing Anaconda

005 Jupyter – Intro to Using Jupyter

006 Jupyter – Working with Notebook Files

007 Jupyter – Using Shortcuts

008 Jupyter – Handling Error Messages

009 Jupyter – Restarting the Kernel

04 – Python Basics

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001 Python Variables

002 Types of Data – Numbers and Boolean Values

003 Types of Data – Strings

004 Basic Python Syntax – Arithmetic Operators

005 Basic Python Syntax – The Double Equality Sign

006 Basic Python Syntax – Reassign Values

007 Basic Python Syntax – Add Comments

008 Basic Python Syntax – Line Continuation

009 Basic Python Syntax – Indexing Elements

010 Basic Python Syntax – Indentation

011 Operators – Comparison Operators

012 Operators – Logical and Identity Operators

013 Conditional Statements – The IF Statement

014 Conditional Statements – The ELSE Statement

015 Conditional Statements – The ELIF Statement

016 Conditional Statements – A Note on Boolean Values

017 Functions – Defining a Function in Python

018 Functions – Creating a Function with a Parameter

019 Functions – Another Way to Define a Function

020 Functions – Using a Function in Another Function

021 Functions – Combining Conditional Statements and Functions

022 Functions – Creating Functions That Contain a Few Arguments

023 Functions – Notable Built-in Functions in Python

024 Sequences – Lists

025 Sequences – Using Methods

026 Sequences – List Slicing

027 Sequences – Tuples

028 Sequences – Dictionaries

029 Iteration – For Loops

030 Iteration – While Loops and Incrementing

031 Iteration – Create Lists with the range() Function

032 Iteration – Use Conditional Statements and Loops Together

033 Iteration – Conditional Statements, Functions, and Loops

034 Iteration – Iterating over Dictionaries

05 – Fundamentals for Coding in Python

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001 Object-Oriented Programming (OOP)

002 Modules, Packages, and the Python Standard Library

003 Importing Modules

004 Introduction to Using NumPy and pandas

005 What is Software Documentation

006 The Python Documentation

06 – Mathematics for Python

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002 Scalars and Vectors

003 Linear Algebra and Geometry

004 Arrays in Python

005 What Is a Tensor

006 Adding and Subtracting Matrices

007 Errors When Adding Matrices

009 Dot Product of Vectors

010 Dot Product of Matrices

011 Why is Linear Algebra Useful

07 – NumPy Basics

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001 The NumPy Package and Why We Use It

002 InstallingUpgrading NumPy

004 The NumPy Documentation

005 NumPy Basics – Exercise

08 – Pandas – Basics

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001 Introduction to the pandas Library

002 Installing and Running pandas

003 Introduction to pandas Series

004 Working with Attributes in Python

005 Using an Index in pandas

006 Label-based vs Position-based Indexing

007 More on Working with Indices in Python

008 Using Methods in Python – Part I

009 Using Methods in Python – Part II

010 Parameters vs Arguments

011 the pandas Documentation

012 Introduction to pandas DataFrames

013 Creating DataFrames from Scratch – Part I

014 Creating DataFrames from Scratch – Part II

015 Additional Notes on Using DataFrames

016 pandas Basics – Conclusion

09 – Working with Text Files

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001 Working with Files in Python – An Introduction

002 File vs File Object, Read vs Parse

003 Structured vs Semi-Structured and Unstructured Data

004 Data Connectivity through Text Files

005 Principles of Importing Data in Python

006 More on Text Files (.txt vs .csv)

007 Fixed-width Files

008 Common Naming Conventions Used in Programming

009 Importing Text Files in Python ( open() )

010 Importing Text Files in Python ( with open() )

011 Importing .csv Files with pandas – Part I

012 Importing .csv Files with pandas – Part II

013 Importing .csv Files with pandas – Part III

014 Importing Data with the index_col Parameter

015 Importing Data with NumPy – .loadtxt() vs genfromtxt()

016 Importing Data with NumPy – Partial Cleaning While Importing

017 Importing Data with NumPy – Exercise

018 Importing .json Files

019 Prelude to Working with Excel Files in Python

020 Working with Excel Data (the .xlsx Format)

021 An Important Exercise on Importing Data in Python

022 Importing Data with the pandas’ Squeeze Parameter

023 A Note on Importing Files in Jupyter

024 Saving Your Data with pandas

025 Saving Your Data with NumPy – np.save()

026 Saving Your Data with NumPy – np.savez()

027 Saving Your Data with NumPy – np.savetxt()

028 Saving Your Data with NumPy – Exercise

029 Working with Text Files – Conclusion

10 – Working with Text Data

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001 Working with Text Data and Argument Specifiers

002 Manipulating Python Strings

003 Using Various Python String Methods – Part I

004 Using Various Python String Methods – Part II

005 String Accessors

006 Using the .format() Method

11 – Must-Know Python Tools

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001 Iterating Over Range Objects

002 Nested For Loops – Introduction

003 Triple Nested For Loops

004 List Comprehensions

005 Anonymous (Lambda) Functions

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12 – Data GatheringData Collection

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001 What is data gatheringdata collection

13 – APIs (POST requests are not needed for this course)

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001 Overview of APIs

002 GET and POST Requests

003 Data Exchange Format for APIs JSON

004 Introducing the Exchange Rates API

005 Including Parameters in a GET Request

006 More Functionalities of the Exchange Rates API

007 Coding a Simple Currency Conversion Calculator

009 iTunes API Homework

010 iTunes API Structuring and Exporting the Data

011 Pagination GitHub API

012 APIs Exercise

14 – Data Cleaning and Data Preprocessing

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001 Data Cleaning and Data Preprocessing

15 – pandas Series

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001 .unique(), .nunique()

002 Converting Series into Arrays

003 .sort_values()

004 Attribute and Method Chaining

005 .sort_index()

16 – pandas DataFrames

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001 A Revision to pandas DataFrames

002 Common Attributes for Working with DataFrames

003 Data Selection in pandas DataFrames

004 Data Selection – Indexing with .iloc[]

005 Data Selection – Indexing with .loc[]

006 A Few Comments on Using .loc[] and .iloc[]

17 – NumPy Fundamentals

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001 Indexing in NumPy

002 Assigning Values in NumPy

003 Elementwise Properties of Arrays

004 Types of Data Supported by NumPy

005 Characteristics of NumPy Functions Part 1

006 Characteristics of NumPy Functions Part 2

007 NumPy Fundamentals – Exercise

18 – NumPy DataTypes

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002 Arrays vs Lists

003 Strings vs Object vs Number

004 NumPy DataTypes – Exercise

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19 – Working with Arrays

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001 Basic Slicing in NumPy

002 Stepwise Slicing in NumPy

003 Conditional Slicing in NumPy

004 Dimensions and the Squeeze Function

005 Working with Arrays – Exercise

20 – Generating Data with NumPy

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001 Arrays of 0s and 1s

002 _like functions in NumPy

003 A Non-Random Sequence of Numbers

004 Random Generators and Seeds

005 Basic Random Functions in NumPy

006 Probability Distributions in NumPy

007 Applications of Random Data in NumPy

008 Generating Data with NumPy – Exercise

21 – Statistics with NumPy

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001 Using Statistical Functions in NumPy

002 Minimal and Maximal Values in NumPy

003 Statistical Order Functions in NumPy

004 Averages and Variance in NumPy

005 Covariance and Correlation in NumPy

006 Histograms in NumPy (Part 1)

007 Histograms in NumPy (Part 2)

008 NAN Equivalent Functions in NumPy

009 Statistics with NumPy – Exercise

22 – NumPy – Preprocessing

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001 Checking for Missing Values in Ndarrays

002 Substituting Missing Values in Ndarrays

003 Reshaping Ndarrays

004 Removing Values from Ndarrays

005 Sorting Ndarrays

006 Argument Sort in NumPy

007 Argument Where in NumPy

008 Shuffling Ndarrays

009 Casting Ndarrays

010 Striping Values from Ndarrays

011 Stacking Ndarrays

012 Concatenating Ndarrays

013 Finding Unique Values in Ndarrays

23 – A Loan Data Example with NumPy

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001 Setting Up Introduction to the Practical Example

002 Setting Up Importing the Data Set

003 Setting Up Checking for Incomplete Data

004 Setting Up Splitting the Dataset

005 Setting Up Creating Checkpoints

006 Manipulating Text Data Issue Date

007 Manipulating Text Data Loan Status and Term

008 Manipulating Text Data Grade and Sub Grade

009 Manipulating Text Data Verification Status & URL

010 Manipulating Text Data State Address

011 Manipulating Text Data Converting Strings and Creating a Checkpoint

012 Manipulating Numeric Data Substitute Filler Values

013 Manipulating Numeric Data Currency Change – The Exchange Rate

014 Manipulating Numeric Data Currency Change – From USD to EUR

015 Completing the Dataset

24 – The Absenteeism Exercise – Introduction

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001 An Introduction to the Absenteeism Exercise

002 The Absenteeism Exercise from a Business Perspective

003 The Dataset

25 – Solution to the Absenteeism Exercise

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001 How to Complete the Absenteeism Exercise

002 Eyeball Your Data First

003 Note Programming vs the Rest of the World

004 Using a Statistical Approach to Solve Our Exercise

005 Dropping the ‘ID’ Column

006 Analysis of the ‘Reason for Absence’ Column

007 Splitting the Reasons for Absence into Multiple Dummy Variables

008 Working with Dummy Variables – A Statistical Perspective

009 Grouping the Reason for Absence Columns

010 Concatenating Columns in a pandas DataFrame

011 Reordering Columns in a DataFrame

012 Working on the ‘Date’ Column

013 Extracting the Month Value from the ‘Date’ Column

014 Creating the ‘Day of the Week’ Column

015 Understanding the Meaning of 5 More Columns

016 Modifying the ‘Education’ Column

017 Final Remarks on the Absenteeism Exercise

26 – Data Visualization

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001 What Is Data Visualization and Why Is It Important

002 Why Learn Data Visualization

003 Choosing the Right Visualization – What Are Some Popular Approaches and Framewor

004 Introduction into Colors and Color Theory

005 Bar Chart – Introduction – General Theory and Getting to Know the Dataset

006 Bar Chart – How to Create a Bar Chart Using Python

007 Bar Chart – Interpreting the Bar Graph. How to Make a Good Bar Graph

008 Pie Chart – Introduction – General Theory and Dataset

009 Pie Chart – How to Create a Pie Chart Using Python

010 Pie Chart – Interpreting the Pie Chart

011 Pie Chart – Why You Should Never Create a Pie Graph

012 Stacked Area Chart – Introduction – General Theory. Getting to Know the Dataset

013 Stacked Area Chart – How to Create a Stacked Area Chart Using Python

014 Stacked Area Chart – Interpreting the Stacked Area Graph

015 Stacked Area Chart – How to Make a Good Stacked Area Chart

016 Line Chart – Introduction – General Theory. Getting to Know the Dataset

017 Line Chart – How to Create a Line Chart in Python

018 Line Chart – Interpretation

019 Line Chart – How to Make a Good Line Chart

020 Histogram – Introduction – General Theory. Getting to Know the Dataset

021 Histogram – How to Create a Histogram Using Python

022 Histogram – Interpreting the Histogram

023 Histogram – Choosing the Number of Bins in a Histogram

024 Histogram – How to Make a Good Histogram

025 Scatter Plot – Introduction – General Theory. Getting to Know the Dataset

026 Scatter Plot – How to Create a Scatter Plot Using Python

027 Scatter Plot – Interpreting the Scatter Plot

028 Scatter Plot – How to Make a Good Scatter Plot

029 Regression Plot – Introduction – General Theory. Getting to Know the Dataset

030 Regression Plot – How to Create a Regression Scatter Plot Using Python

031 Regression Plot – Interpreting the Regression Scatter Plot

032 Regression Plot – How to Make a Good Regression Plot

033 Bar and Line Chart – Introduction – General Theory. Getting to Know the Dataset

034 Bar and Line Chart – How to Create a Combination Bar and Line Graph Using Python

035 Bar and Line Chart – Interpreting the Combination Bar and Line Graph

036 Bar and Line Chart – How to Make a Good Bar and Line Graph

037 Data Visualization – Exercise

27 – Conclusion

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The Data Analyst Course: Complete Data Analyst Bootcamp

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