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
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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

Welcome to this exercise on Statistics with NumPy!

Download the *-Exercise.ipynb file, open it in Python and solve all the problems listed in the Python Notebook.

If you need any help, you can always refer to the *-Solution.ipynb file located in the same folder.

For simplicity, you can also click here once again and download all the resources for this exercise.

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