Course Content
Subtitle Guide – Hướng dẫn thêm phụ đề
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01 – Introduction to the Course
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001 A Practical Example – What Will You Learn in This Course
04:46
002 Download All Resources
003 FAQ
02 – Introduction to Data Analytics
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001 Introduction to the World of Business and Data
02:26
002 Relevant Terms Explained
05:45
003 Data Analyst Compared to Other Data Jobs
02:28
004 Data Analyst Job Description
05:42
005 Why Python
05:08
03 – Setting up the Environment
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001 Introduction
01:24
002 Programming Explained in a Few Minutes
05:04
003 Jupyter – Introduction
03:29
004 Jupyter – Installing Anaconda
04:00
005 Jupyter – Intro to Using Jupyter
03:11
006 Jupyter – Working with Notebook Files
06:09
007 Jupyter – Using Shortcuts
03:07
008 Jupyter – Handling Error Messages
05:52
009 Jupyter – Restarting the Kernel
02:18
04 – Python Basics
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001 Python Variables
03:37
002 Types of Data – Numbers and Boolean Values
03:05
003 Types of Data – Strings
05:40
004 Basic Python Syntax – Arithmetic Operators
03:23
005 Basic Python Syntax – The Double Equality Sign
01:33
006 Basic Python Syntax – Reassign Values
01:08
007 Basic Python Syntax – Add Comments
01:34
008 Basic Python Syntax – Line Continuation
00:49
009 Basic Python Syntax – Indexing Elements
01:18
010 Basic Python Syntax – Indentation
01:44
011 Operators – Comparison Operators
02:10
012 Operators – Logical and Identity Operators
05:36
013 Conditional Statements – The IF Statement
03:01
014 Conditional Statements – The ELSE Statement
02:45
015 Conditional Statements – The ELIF Statement
05:34
016 Conditional Statements – A Note on Boolean Values
02:14
017 Functions – Defining a Function in Python
02:02
018 Functions – Creating a Function with a Parameter
03:49
019 Functions – Another Way to Define a Function
02:36
020 Functions – Using a Function in Another Function
01:49
021 Functions – Combining Conditional Statements and Functions
03:06
022 Functions – Creating Functions That Contain a Few Arguments
01:17
023 Functions – Notable Built-in Functions in Python
03:56
024 Sequences – Lists
04:02
025 Sequences – Using Methods
03:19
026 Sequences – List Slicing
04:31
027 Sequences – Tuples
03:11
028 Sequences – Dictionaries
04:04
029 Iteration – For Loops
02:56
030 Iteration – While Loops and Incrementing
02:26
031 Iteration – Create Lists with the range() Function
03:49
032 Iteration – Use Conditional Statements and Loops Together
03:11
033 Iteration – Conditional Statements, Functions, and Loops
02:27
034 Iteration – Iterating over Dictionaries
03:07
05 – Fundamentals for Coding in Python
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001 Object-Oriented Programming (OOP)
05:00
002 Modules, Packages, and the Python Standard Library
04:24
003 Importing Modules
03:25
004 Introduction to Using NumPy and pandas
09:09
005 What is Software Documentation
03:58
006 The Python Documentation
06:23
06 – Mathematics for Python
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002 Scalars and Vectors
02:58
003 Linear Algebra and Geometry
03:06
004 Arrays in Python
05:09
005 What Is a Tensor
03:00
006 Adding and Subtracting Matrices
03:36
007 Errors When Adding Matrices
02:01
008 Transpose
05:13
009 Dot Product of Vectors
03:48
010 Dot Product of Matrices
08:23
011 Why is Linear Algebra Useful
10:10
07 – NumPy Basics
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001 The NumPy Package and Why We Use It
04:03
002 InstallingUpgrading NumPy
02:01
003 Ndarray
03:06
004 The NumPy Documentation
04:43
005 NumPy Basics – Exercise
08 – Pandas – Basics
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001 Introduction to the pandas Library
05:41
002 Installing and Running pandas
05:57
003 Introduction to pandas Series
08:41
004 Working with Attributes in Python
05:22
005 Using an Index in pandas
04:00
006 Label-based vs Position-based Indexing
04:31
007 More on Working with Indices in Python
05:37
008 Using Methods in Python – Part I
04:55
009 Using Methods in Python – Part II
02:36
010 Parameters vs Arguments
04:35
011 the pandas Documentation
09:54
012 Introduction to pandas DataFrames
05:23
013 Creating DataFrames from Scratch – Part I
05:56
014 Creating DataFrames from Scratch – Part II
05:03
015 Additional Notes on Using DataFrames
01:58
016 pandas Basics – Conclusion
09 – Working with Text Files
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001 Working with Files in Python – An Introduction
03:46
002 File vs File Object, Read vs Parse
02:52
003 Structured vs Semi-Structured and Unstructured Data
03:10
004 Data Connectivity through Text Files
03:06
005 Principles of Importing Data in Python
04:50
006 More on Text Files (.txt vs .csv)
04:33
007 Fixed-width Files
01:26
008 Common Naming Conventions Used in Programming
03:49
009 Importing Text Files in Python ( open() )
09:00
010 Importing Text Files in Python ( with open() )
04:53
011 Importing .csv Files with pandas – Part I
05:35
012 Importing .csv Files with pandas – Part II
02:37
013 Importing .csv Files with pandas – Part III
05:57
014 Importing Data with the index_col Parameter
02:35
015 Importing Data with NumPy – .loadtxt() vs genfromtxt()
10:44
016 Importing Data with NumPy – Partial Cleaning While Importing
07:21
017 Importing Data with NumPy – Exercise
018 Importing .json Files
05:15
019 Prelude to Working with Excel Files in Python
03:40
020 Working with Excel Data (the .xlsx Format)
01:55
021 An Important Exercise on Importing Data in Python
05:44
022 Importing Data with the pandas’ Squeeze Parameter
02:37
023 A Note on Importing Files in Jupyter
03:10
024 Saving Your Data with pandas
03:11
025 Saving Your Data with NumPy – np.save()
05:23
026 Saving Your Data with NumPy – np.savez()
05:12
027 Saving Your Data with NumPy – np.savetxt()
03:58
028 Saving Your Data with NumPy – Exercise
029 Working with Text Files – Conclusion
00:42
10 – Working with Text Data
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001 Working with Text Data and Argument Specifiers
09:18
002 Manipulating Python Strings
04:13
003 Using Various Python String Methods – Part I
06:51
004 Using Various Python String Methods – Part II
06:44
005 String Accessors
04:50
006 Using the .format() Method
09:03
11 – Must-Know Python Tools
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001 Iterating Over Range Objects
04:17
002 Nested For Loops – Introduction
05:59
003 Triple Nested For Loops
05:37
004 List Comprehensions
08:30
005 Anonymous (Lambda) Functions
07:00
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12 – Data GatheringData Collection
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001 What is data gatheringdata collection
06:32
13 – APIs (POST requests are not needed for this course)
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001 Overview of APIs
03:10
002 GET and POST Requests
02:35
003 Data Exchange Format for APIs JSON
02:24
004 Introducing the Exchange Rates API
04:57
005 Including Parameters in a GET Request
03:18
006 More Functionalities of the Exchange Rates API
04:39
007 Coding a Simple Currency Conversion Calculator
04:52
008 iTunes API
04:41
009 iTunes API Homework
010 iTunes API Structuring and Exporting the Data
02:10
011 Pagination GitHub API
04:21
012 APIs Exercise
14 – Data Cleaning and Data Preprocessing
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001 Data Cleaning and Data Preprocessing
05:27
15 – pandas Series
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001 .unique(), .nunique()
03:49
002 Converting Series into Arrays
05:29
003 .sort_values()
03:58
004 Attribute and Method Chaining
04:21
005 .sort_index()
03:59
16 – pandas DataFrames
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001 A Revision to pandas DataFrames
05:05
002 Common Attributes for Working with DataFrames
04:15
003 Data Selection in pandas DataFrames
06:55
004 Data Selection – Indexing with .iloc[]
05:56
005 Data Selection – Indexing with .loc[]
04:02
006 A Few Comments on Using .loc[] and .iloc[]
11:40
17 – NumPy Fundamentals
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001 Indexing in NumPy
05:52
002 Assigning Values in NumPy
04:16
003 Elementwise Properties of Arrays
04:29
004 Types of Data Supported by NumPy
05:56
005 Characteristics of NumPy Functions Part 1
04:43
006 Characteristics of NumPy Functions Part 2
03:31
007 NumPy Fundamentals – Exercise
18 – NumPy DataTypes
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001 ndarrays
09:52
002 Arrays vs Lists
06:55
003 Strings vs Object vs Number
07:14
004 NumPy DataTypes – Exercise
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19 – Working with Arrays
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001 Basic Slicing in NumPy
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002 Stepwise Slicing in NumPy
04:58
003 Conditional Slicing in NumPy
04:51
004 Dimensions and the Squeeze Function
06:52
005 Working with Arrays – Exercise
00:00
20 – Generating Data with NumPy
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001 Arrays of 0s and 1s
05:32
002 _like functions in NumPy
03:13
003 A Non-Random Sequence of Numbers
05:02
004 Random Generators and Seeds
05:21
005 Basic Random Functions in NumPy
03:56
006 Probability Distributions in NumPy
05:19
007 Applications of Random Data in NumPy
04:09
008 Generating Data with NumPy – Exercise
21 – Statistics with NumPy
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001 Using Statistical Functions in NumPy
07:45
002 Minimal and Maximal Values in NumPy
06:02
003 Statistical Order Functions in NumPy
06:26
004 Averages and Variance in NumPy
04:17
005 Covariance and Correlation in NumPy
02:59
006 Histograms in NumPy (Part 1)
07:36
007 Histograms in NumPy (Part 2)
04:15
008 NAN Equivalent Functions in NumPy
03:09
009 Statistics with NumPy – Exercise
22 – NumPy – Preprocessing
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001 Checking for Missing Values in Ndarrays
09:23
002 Substituting Missing Values in Ndarrays
08:29
003 Reshaping Ndarrays
06:31
004 Removing Values from Ndarrays
04:20
005 Sorting Ndarrays
09:45
006 Argument Sort in NumPy
05:48
007 Argument Where in NumPy
11:13
008 Shuffling Ndarrays
06:51
009 Casting Ndarrays
06:14
010 Striping Values from Ndarrays
04:43
011 Stacking Ndarrays
10:31
012 Concatenating Ndarrays
06:28
013 Finding Unique Values in Ndarrays
05:04
23 – A Loan Data Example with NumPy
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001 Setting Up Introduction to the Practical Example
04:50
002 Setting Up Importing the Data Set
04:10
003 Setting Up Checking for Incomplete Data
04:35
004 Setting Up Splitting the Dataset
05:27
005 Setting Up Creating Checkpoints
02:50
006 Manipulating Text Data Issue Date
05:27
007 Manipulating Text Data Loan Status and Term
07:08
008 Manipulating Text Data Grade and Sub Grade
08:54
009 Manipulating Text Data Verification Status & URL
05:20
010 Manipulating Text Data State Address
06:02
011 Manipulating Text Data Converting Strings and Creating a Checkpoint
03:28
012 Manipulating Numeric Data Substitute Filler Values
07:51
013 Manipulating Numeric Data Currency Change – The Exchange Rate
06:32
014 Manipulating Numeric Data Currency Change – From USD to EUR
08:22
015 Completing the Dataset
06:46
24 – The Absenteeism Exercise – Introduction
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001 An Introduction to the Absenteeism Exercise
01:11
002 The Absenteeism Exercise from a Business Perspective
02:19
003 The Dataset
01:34
25 – Solution to the Absenteeism Exercise
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001 How to Complete the Absenteeism Exercise
01:57
002 Eyeball Your Data First
05:53
003 Note Programming vs the Rest of the World
03:28
004 Using a Statistical Approach to Solve Our Exercise
02:17
005 Dropping the ‘ID’ Column
06:27
006 Analysis of the ‘Reason for Absence’ Column
05:04
007 Splitting the Reasons for Absence into Multiple Dummy Variables
08:37
008 Working with Dummy Variables – A Statistical Perspective
01:28
009 Grouping the Reason for Absence Columns
08:35
010 Concatenating Columns in a pandas DataFrame
04:35
011 Reordering Columns in a DataFrame
01:43
012 Working on the ‘Date’ Column
07:49
013 Extracting the Month Value from the ‘Date’ Column
07:00
014 Creating the ‘Day of the Week’ Column
03:36
015 Understanding the Meaning of 5 More Columns
03:17
016 Modifying the ‘Education’ Column
04:38
017 Final Remarks on the Absenteeism Exercise
01:41
26 – Data Visualization
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001 What Is Data Visualization and Why Is It Important
04:31
002 Why Learn Data Visualization
06:08
003 Choosing the Right Visualization – What Are Some Popular Approaches and Framewor
06:58
004 Introduction into Colors and Color Theory
08:56
005 Bar Chart – Introduction – General Theory and Getting to Know the Dataset
01:30
006 Bar Chart – How to Create a Bar Chart Using Python
11:27
007 Bar Chart – Interpreting the Bar Graph. How to Make a Good Bar Graph
02:50
008 Pie Chart – Introduction – General Theory and Dataset
04:04
009 Pie Chart – How to Create a Pie Chart Using Python
06:39
010 Pie Chart – Interpreting the Pie Chart
01:32
011 Pie Chart – Why You Should Never Create a Pie Graph
07:32
012 Stacked Area Chart – Introduction – General Theory. Getting to Know the Dataset
03:16
013 Stacked Area Chart – How to Create a Stacked Area Chart Using Python
07:48
014 Stacked Area Chart – Interpreting the Stacked Area Graph
02:30
015 Stacked Area Chart – How to Make a Good Stacked Area Chart
03:53
016 Line Chart – Introduction – General Theory. Getting to Know the Dataset
02:03
017 Line Chart – How to Create a Line Chart in Python
08:05
018 Line Chart – Interpretation
03:11
019 Line Chart – How to Make a Good Line Chart
06:30
020 Histogram – Introduction – General Theory. Getting to Know the Dataset
04:39
021 Histogram – How to Create a Histogram Using Python
05:43
022 Histogram – Interpreting the Histogram
02:11
023 Histogram – Choosing the Number of Bins in a Histogram
05:28
024 Histogram – How to Make a Good Histogram
04:43
025 Scatter Plot – Introduction – General Theory. Getting to Know the Dataset
02:29
026 Scatter Plot – How to Create a Scatter Plot Using Python
08:39
027 Scatter Plot – Interpreting the Scatter Plot
02:42
028 Scatter Plot – How to Make a Good Scatter Plot
02:57
029 Regression Plot – Introduction – General Theory. Getting to Know the Dataset
03:03
030 Regression Plot – How to Create a Regression Scatter Plot Using Python
07:08
031 Regression Plot – Interpreting the Regression Scatter Plot
04:36
032 Regression Plot – How to Make a Good Regression Plot
03:14
033 Bar and Line Chart – Introduction – General Theory. Getting to Know the Dataset
03:10
034 Bar and Line Chart – How to Create a Combination Bar and Line Graph Using Python
07:40
035 Bar and Line Chart – Interpreting the Combination Bar and Line Graph
02:36
036 Bar and Line Chart – How to Make a Good Bar and Line Graph
04:04
037 Data Visualization – Exercise
27 – Conclusion
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001 Conclusion
02:22
The Data Analyst Course: Complete Data Analyst Bootcamp
About Lesson

In terms of using pandas and NumPy, as well as Python in general, at this stage of the course, you can say you have acquired enough knowledge and basic practical skills to be somehow independent while learning new related material. Yes, there are plenty of other tools that youll need support for. But surely, your current level allows you to free your mind from the fundamentals and focus on more complex topics such as the next one. It is about importing data in Python. Although the latter is not always a straightforward task, it is indeed a tool that everyone interested in doing data cleaning, preprocessing, or analysis with Python, must have under their belt.

Please allow me to use the occasion to remind you: you can re-watch a video, if necessary, or repeat an exercise that you have found challenging in this section. We stand behind the idea that practice is essential while learning new concepts. So, to help you advance we will challenge you with exercises in the sections to come, too.

Good luck and thank you for watching!

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