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01 – Introduction to the Course

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

Lesson 002 What Does the Course Cover

02 – Introduction to Data Analytics

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Lesson 001 Introduction to the World of Business and Data

Lesson 002 Relevant Terms Explained

Lesson 003 Data Analyst Compared to Other Data Jobs

Lesson 004 Data Analyst Job Description

Lesson 005 Why Python

03 – Setting up the Environment

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Lesson 001 Introduction

Lesson 002 Programming Explained in a Few Minutes

01 – Introduction to the Course

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

Lesson 002 What Does the Course Cover

02 – Introduction to Data Analytics

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Lesson 001 Introduction to the World of Business and Data

Lesson 002 Relevant Terms Explained

Lesson 003 Data Analyst Compared to Other Data Jobs

Lesson 004 Data Analyst Job Description

Lesson 005 Why Python

03 – Setting up the Environment

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Lesson 001 Introduction

Lesson 002 Programming Explained in a Few Minutes

Lesson 004 Jupyter – Introduction

Lesson 005 Jupyter – Installing Anaconda

Lesson 006 Jupyter – Intro to Using Jupyter

Lesson 007 Jupyter – Working with Notebook Files

Lesson 008 Jupyter – Using Shortcuts

Lesson 009 Jupyter – Handling Error Messages

Lesson 010 Jupyter – Restarting the Kernel

04 – Python Basics

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Lesson 001 Python Variables

Lesson 007 Types of Data – Numbers and Boolean Values

Lesson 014 Types of Data – Strings

Lesson 021 Basic Python Syntax – Arithmetic Operators

Lesson 031 Basic Python Syntax – The Double Equality Sign

Lesson 034 Basic Python Syntax – Reassign Values

Lesson 040 Basic Python Syntax – Add Comments

Lesson 042 Basic Python Syntax – Line Continuation

Lesson 044 Basic Python Syntax – Indexing Elements

Lesson 048 Basic Python Syntax – Indentation

Lesson 051 Operators – Comparison Operators

Lesson 057 Operators – Logical and Identity Operators

Lesson 065 Conditional Statements – The IF Statement

Lesson 069 Conditional Statements – The ELSE Statement

Lesson 071 Conditional Statements – The ELIF Statement

Lesson 074 Conditional Statements – A Note on Boolean Values

Lesson 076 Functions – Defining a Function in Python

Lesson 077 Functions – Creating a Function with a Parameter

Lesson 080 Functions – Another Way to Define a Function

Lesson 082 Functions – Using a Function in Another Function

Lesson 084 Functions – Combining Conditional Statements and Functions

Lesson 086 Functions – Creating Functions That Contain a Few Arguments

Lesson 087 Functions – Notable Built-in Functions in Python

Lesson 098 Sequences – Lists

Lesson 105 Sequences – Using Methods

Lesson 111 Sequences – List Slicing

Lesson 119 Sequences – Tuples

Lesson 124 Sequences – Dictionaries

Lesson 132 Iteration – For Loops

Lesson 136 Iteration – While Loops and Incrementing

Lesson 138 Iteration – Create Lists with the range() Function

Lesson 143 Iteration – Use Conditional Statements and Loops Together

Lesson 147 Iteration – Conditional Statements, Functions, and Loops

Lesson 149 Iteration – Iterating over Dictionaries

05 – Fundamentals for Coding in Python

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Lesson 001 Object-Oriented Programming (OOP)

Lesson 002 Modules, Packages, and the Python Standard Library

Lesson 003 Importing Modules

Lesson 004 Introduction to Using NumPy and pandas

Lesson 005 What is Software Documentation

Lesson 006 The Python Documentation

06 – Mathematics for Python

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Lesson 001 What Is Ð° Matrix.decrypted

Lesson 001 What Is а Matrix

Lesson 002 Scalars and Vectors

Lesson 003 Linear Algebra and Geometry

Lesson 004 Arrays in Python

Lesson 005 What Is a Tensor

Lesson 006 Adding and Subtracting Matrices

Lesson 007 Errors When Adding Matrices

Lesson 008 Transpose

Lesson 009 Dot Product of Vectors

Lesson 010 Dot Product of Matrices

Lesson 011 Why is Linear Algebra Useful

07 – NumPy Basics

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Lesson 001 The NumPy Package and Why We Use It

Lesson 002 InstallingUpgrading NumPy

Lesson 003 Ndarray

Lesson 004 The NumPy Documentation

08 – Pandas – Basics

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Lesson 001 Introduction to the pandas Library

Lesson 002 Installing and Running pandas

Lesson 006 Introduction to pandas Series

Lesson 017 Working with Attributes in Python

Lesson 025 Using an Index in pandas

Lesson 031 Label-based vs Position-based Indexing

Lesson 034 More on Working with Indices in Python

Lesson 038 Using Methods in Python – Part I

Lesson 039 Using Methods in Python – Part II

Lesson 042 Parameters vs Arguments

Lesson 045 The pandas Documentation

Lesson 046 Introduction to pandas DataFrames

Lesson 047 Creating DataFrames from Scratch – Part I

Lesson 050 Creating DataFrames from Scratch – Part II

Lesson 054 Additional Notes on Using DataFrames

09 – Working with Text Files

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Lesson 001 Working with Files in Python – An Introduction

Lesson 002 File vs File Object, Read vs Parse

Lesson 003 Structured vs Semi-Structured and Unstructured Data

Lesson 004 Data Connectivity through Text Files

Lesson 005 Principles of Importing Data in Python

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

Lesson 007 Fixed-width Files

Lesson 008 Common Naming Conventions Used in Programming

Lesson 009 Importing Text Files in Python ( open() )

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

Lesson 011 Importing .csv Files with pandas – Part I

Lesson 012 Importing .csv Files with pandas – Part II

Lesson 013 Importing .csv Files with pandas – Part III

Lesson 014 Importing Data with the index_col Parameter

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

Lesson 016 Importing Data with NumPy – Partial Cleaning While Importing

Lesson 018 Importing .json Files

Lesson 019 Prelude to Working with Excel Files in Python

Lesson 020 Working with Excel Data (the .xlsx Format)

Lesson 021 An Important Exercise on Importing Data in Python

Lesson 022 Importing Data with the pandas’ Squeeze Method

Lesson 023 A Note on Importing Files in Jupyter

Lesson 024 Saving Your Data with pandas

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

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

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

Lesson 029 Working with Text Files – Conclusion

10 – Working with Text Data

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Lesson 001 Working with Text Data and Argument Specifiers

Lesson 005 Manipulating Python Strings

Lesson 011 Using Various Python String Methods – Part I

Lesson 027 Using Various Python String Methods – Part II

Lesson 033 String Accessors

Lesson 039 Using the .format() Method

11 – Must-Know Python Tools

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Lesson 001 Iterating Over Range Objects

Lesson 002 Nested For Loops – Introduction

Lesson 003 Triple Nested For Loops

Lesson 011 List Comprehensions

Lesson 017 Anonymous (Lambda) Functions

12 – Data GatheringData Collection

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Lesson 001 What is data gatheringdata collection

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

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Lesson 001 Overview of APIs

Lesson 002 GET and POST Requests

Lesson 003 Data Exchange Format for APIs JSON

Lesson 004 Introducing the Exchange Rates API

Lesson 005 Including Parameters in a GET Request

Lesson 006 More Functionalities of the Exchange Rates API

Lesson 007 Coding a Simple Currency Conversion Calculator

Lesson 008 iTunes API

Lesson 010 iTunes API Structuring and Exporting the Data

Lesson 011 Pagination GitHub API

14 – Data Cleaning and Data Preprocessing

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Lesson 001 Data Cleaning and Data Preprocessing

15 – pandas Series

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Lesson 002 .unique(), .nunique()

Lesson 009 Converting Series into Arrays

Lesson 010 .sort_values()

Lesson 015 Attribute and Method Chaining

Lesson 022 .sort_index()

16 – pandas DataFrames

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Lesson 001 A Revision to pandas DataFrames

Lesson 002 Common Attributes for Working with DataFrames

Lesson 003 Data Selection in pandas DataFrames

Lesson 004 Data Selection – Indexing with .iloc[]

Lesson 005 Data Selection – Indexing with .loc[]

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

17 – NumPy Fundamentals

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Lesson 001 Indexing in NumPy

Lesson 002 Assigning Values in NumPy

Lesson 003 Elementwise Properties of Arrays

Lesson 004 Types of Data Supported by NumPy

Lesson 005 Characteristics of NumPy Functions Part 1

Lesson 006 Characteristics of NumPy Functions Part 2

18 – NumPy DataTypes

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Lesson 001 ndarrays

Lesson 002 Arrays vs Lists

Lesson 003 Strings vs Object vs Number

19 – Working with Arrays

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Lesson 001 Basic Slicing in NumPy

Lesson 002 Stepwise Slicing in NumPy

Lesson 003 Conditional Slicing in NumPy

Lesson 004 Dimensions and the Squeeze Function

20 – Generating Data with NumPy

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Lesson 001 Arrays of 0s and 1s

Lesson 002 _like functions in NumPy

Lesson 003 A Non-Random Sequence of Numbers

Lesson 004 Random Generators and Seeds

Lesson 005 Basic Random Functions in NumPy

Lesson 006 Probability Distributions in NumPy

Lesson 007 Applications of Random Data in NumPy

21 – Statistics with NumPy

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Lesson 001 Using Statistical Functions in NumPy

Lesson 002 Minimal and Maximal Values in NumPy

Lesson 003 Statistical Order Functions in NumPy

Lesson 004 Averages and Variance in NumPy

Lesson 005 Covariance and Correlation in NumPy

Lesson 006 Histograms in NumPy (Part 1)

Lesson 007 Histograms in NumPy (Part 2)

Lesson 008 NAN Equivalent Functions in NumPy

22 – NumPy – Preprocessing

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Lesson 001 Checking for Missing Values in Ndarrays

Lesson 002 Substituting Missing Values in Ndarrays

Lesson 003 Reshaping Ndarrays

Lesson 004 Removing Values from Ndarrays

Lesson 005 Sorting Ndarrays

Lesson 006 Argument Sort in NumPy

Lesson 007 Argument Where in NumPy

Lesson 008 Shuffling Ndarrays

Lesson 009 Casting Ndarrays

Lesson 010 Striping Values from Ndarrays

Lesson 011 Stacking Ndarrays

Lesson 012 Concatenating Ndarrays

Lesson 013 Finding Unique Values in Ndarrays

23 – A Loan Data Example with NumPy

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Lesson 001 Setting Up Introduction to the Practical Example

Lesson 002 Setting Up Importing the Data Set

Lesson 003 Setting Up Checking for Incomplete Data

Lesson 004 Setting Up Splitting the Dataset

Lesson 005 Setting Up Creating Checkpoints

Lesson 006 Manipulating Text Data Issue Date

Lesson 007 Manipulating Text Data Loan Status and Term

Lesson 008 Manipulating Text Data Grade and Sub Grade

Lesson 009 Manipulating Text Data Verification Status & URL

Lesson 010 Manipulating Text Data State Address

Lesson 011 Manipulating Text Data Converting Strings and Creating a Checkpoint

Lesson 012 Manipulating Numeric Data Substitute Filler Values

Lesson 013 Manipulating Numeric Data Currency Change – The Exchange Rate

Lesson 014 Manipulating Numeric Data Currency Change – From USD to EUR

Lesson 015 Completing the Dataset

24 – The Absenteeism Exercise – Introduction

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Lesson 001 An Introduction to the Absenteeism Exercise

Lesson 002 The Absenteeism Exercise from a Business Perspective

Lesson 003 The Dataset

25 – Solution to the Absenteeism Exercise

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Lesson 001 How to Complete the Absenteeism Exercise

Lesson 002 Eyeball Your Data First

Lesson 003 Note Programming vs the Rest of the World

Lesson 004 Using a Statistical Approach to Solve Our Exercise

Lesson 005 Dropping the ‘ID’ Column

Lesson 006 Analysis of the ‘Reason for Absence’ Column

Lesson 007 Splitting the Reasons for Absence into Multiple Dummy Variables

Lesson 008 Working with Dummy Variables – A Statistical Perspective

Lesson 009 Grouping the Reason for Absence Columns

Lesson 010 Concatenating Columns in a pandas DataFrame

Lesson 011 Reordering Columns in a DataFrame

Lesson 013 Working on the ‘Date’ Column

Lesson 014 Extracting the Month Value from the ‘Date’ Column

Lesson 015 Creating the ‘Day of the Week’ Column

Lesson 016 Understanding the Meaning of 5 More Columns

Lesson 017 Modifying the ‘Education’ Column

Lesson 018 Final Remarks on the Absenteeism Exercise

26 – Data Visualization

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Lesson 001 What Is Data Visualization and Why Is It Important

Lesson 002 Why Learn Data Visualization

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

Lesson 004 Introduction into Colors and Color Theory

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

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

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

Lesson 008 Pie Chart – Introduction – General Theory and Dataset

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

Lesson 010 Pie Chart – Interpreting the Pie Chart

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

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

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

Lesson 014 Stacked Area Chart – Interpreting the Stacked Area Graph

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

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

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

Lesson 018 Line Chart – Interpretation

Lesson 019 Line Chart – How to Make a Good Line Chart

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

Lesson 021 Histogram – How to Create a Histogram Using Python

Lesson 022 Histogram – Interpreting the Histogram

Lesson 023 Histogram – Choosing the Number of Bins in a Histogram

Lesson 024 Histogram – How to Make a Good Histogram

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

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

Lesson 027 Scatter Plot – Interpreting the Scatter Plot

Lesson 028 Scatter Plot – How to Make a Good Scatter Plot

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

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

Lesson 031 Regression Plot – Interpreting the Regression Scatter Plot

Lesson 032 Regression Plot – How to Make a Good Regression Plot

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

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

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

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

27 – Conclusion

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Lesson 001 Conclusion

The Data Analyst Course: Complete Data Analyst Bootcamp

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