掌握Python数据、统计和统计建模课程

本课程将帮助您掌握Python数据科学和统计建模的基础知识到高级应用。无论您是初学者还是希望提升技能，本课程都提供了一个结构化的学习路径，通过动手项目和案例研究，让您获得实践经验。Python Mastery For Data, Statistics & Statistical Modeling

在本课程中，您将学习如何扎实掌握数据科学和统计领域的Python编程，使用Python进行数据操作，探索性数据分析技术，创建数据可视化，以及应用统计建模技术，了解数据分析和机器学习中的实际应用。

不需要先验知识或经验，本课程将从绝对基础开始解释，让您轻松上手。课程内容涵盖了Python语法和数据结构的有效操作，使用Pandas和Numpy进行数据探索，以及使用Matplotlib、Seaborn和Bokeh创建引人注目的数据可视化。此外，您还将深入研究Python中的Scikit-Learn机器学习，了解概率和统计的关键概念，并在现实场景中应用统计建模技术。

通过本课程，您将能够使用Python算法构建自定义统计模型，执行假设检验和相关性分析，实施线性和多元回归模型，并参与实践项目和真实世界案例研究。

总之，本课程将为您提供使用Python进行数据分析、可视化和建模所需的知识和实践经验。无论您的目标是开启职业生涯、提升当前角色，还是探索数据世界，本课程都能够为您提供所需的基础。

课程目录：
Section 1: Python for Data Science and Data Analysis

Lecture 1 Link to the Python codes for the projects and the data

Lecture 2 Introduction: About the Tutor and AI Sciences

Lecture 3 Introduction: Introduction To Instructor

Lecture 4 Introduction: Focus of the Course-Part 1

Lecture 5 Introduction: Focus of the Course- Part 2

Lecture 6 Basics of Programming: Understanding the Algorithm

Lecture 7 Basics of Programming: FlowCharts and Pseudocodes

Lecture 8 Basics of Programming: Example of Algorithms- Making Tea Problem

Lecture 9 Basics of Programming: Example of Algorithms-Searching Minimun

Lecture 10 Basics of Programming: Example of Algorithms-Searching Minimun Quiz

Lecture 11 Basics of Programming: Example of Algorithms-Sorting Problem

Lecture 12 Basics of Programming: Example of Algorithms-Searching Minimun Solution

Lecture 13 Basics of Programming: Sorting Problem in Python

Lecture 14 Why Python and Jupyter Notebook: Why Python

Lecture 15 Why Python and Jupyter Notebook: Why Jupyter Notebooks

Lecture 16 Installation of Anaconda and IPython Shell: Installing Python and Jupyter Anaconda

Lecture 17 Installation of Anaconda and IPython Shell: Your First Python Code- Hello World

Lecture 18 Installation of Anaconda and IPython Shell: Coding in IPython Shell

Lecture 19 Variable and Operator: Variables

Lecture 20 Variable and Operator: Operators

Lecture 21 Variable and Operator: Variable Name Quiz

Lecture 22 Variable and Operator: Bool Data Type in Python

Lecture 23 Variable and Operator: Comparison in Python

Lecture 24 Variable and Operator: Combining Comparisons in Python

Lecture 25 Variable and Operator: Combining Comparisons Quiz

Lecture 26 Python Useful function: Python Function- Round

Lecture 27 Python Useful function: Python Function- Round Quiz

Lecture 28 Python Useful function: Python Function- Round Solution

Lecture 29 Python Useful function: Python Function- Divmod

Lecture 30 Python Useful function: Python Function- Is instance and PowFunctions

Lecture 31 Python Useful function: Python Function- Input

Lecture 32 Control Flow in Python: If Python Condition

Lecture 33 Control Flow in Python: if Elif Else Python Conditions

Lecture 34 Control Flow in Python: if Elif Else Python Conditions Quiz

Lecture 35 Control Flow in Python: if Elif Else Python Conditions Solution

Lecture 36 Control Flow in Python: More on if Elif Else Python Conditions

Lecture 37 Control Flow in Python: More on if Elif Else Python Conditions Quiz

Lecture 38 Control Flow in Python: More on if Elif Else Python Conditions Solution

Lecture 39 Control Flow in Python: Indentations

Lecture 40 Control Flow in Python: Indentations Quiz

Lecture 41 Control Flow in Python: Indentations Solution

Lecture 42 Control Flow in Python: Comments and Problem Solving Practice With If

Lecture 43 Control Flow in Python: While Loop

Lecture 44 Control Flow in Python: While Loop break Continue

Lecture 45 Control Flow in Python: While Loop break Continue Quiz

Lecture 46 Control Flow in Python: While Loop break Continue Solution

Lecture 47 Control Flow in Python: For Loop

Lecture 48 Control Flow in Python: For Loop Quiz

Lecture 49 Control Flow in Python: For Loop Solution

Lecture 50 Control Flow in Python: Else In For Loop

Lecture 51 Control Flow in Python: Loops Practice-Sorting Problem

Lecture 52 Function and Module in Python: Functions in Python

Lecture 53 Function and Module in Python: DocString

Lecture 54 Function and Module in Python: Input Arguments

Lecture 55 Function and Module in Python: Multiple Input Arguments

Lecture 56 Function and Module in Python: Multiple Input Arguments Quiz

Lecture 57 Function and Module in Python: Multiple Input Arguments Solution

Lecture 58 Function and Module in Python: Ordering Multiple Input Arguments

Lecture 59 Function and Module in Python: Output Arguments and Return Statement

Lecture 60 Function and Module in Python: Function Practice-Output Arguments and Return Statement

Lecture 61 Function and Module in Python: Variable Number of Input Arguments

Lecture 62 Function and Module in Python: Variable Number of Input Arguments Quiz

Lecture 63 Function and Module in Python: Variable Number of Input Arguments Solution

Lecture 64 Function and Module in Python: Variable Number of Input Arguments as Dictionary

Lecture 65 Function and Module in Python: Variable Number of Input Arguments as Dictionary Quiz

Lecture 66 Function and Module in Python: Variable Number of Input Arguments as Dictionary Solution

Lecture 67 Function and Module in Python: Default Values in Python

Lecture 68 Function and Module in Python: Modules in Python

Lecture 69 Function and Module in Python: Making Modules in Python

Lecture 70 Function and Module in Python: Function Practice-Sorting List in Python

Lecture 71 String in Python: Strings

Lecture 72 String in Python: Multi Line Strings

Lecture 73 String in Python: Indexing Strings

Lecture 74 String in Python: Indexing Strings Quiz

Lecture 75 String in Python: Indexing Strings Solution

Lecture 76 String in Python: String Methods

Lecture 77 String in Python: String Methods Quiz

Lecture 78 String in Python: String Methods Solution

Lecture 79 String in Python: String Escape Sequences

Lecture 80 String in Python: String Escape Sequences Quiz

Lecture 81 String in Python: String Escape Sequences Solution

Lecture 82 Data Structure: Introduction to Data Structure

Lecture 83 Data Structure: Defining and Indexing

Lecture 84 Data Structure: Insertion and Deletion

Lecture 85 Data Structure: Insertion and Deletion Quiz

Lecture 86 Data Structure: Insertion and Deletion Solution

Lecture 87 Data Structure: Python Practice-Insertion and Deletion

Lecture 88 Data Structure: Python Practice-Insertion and Deletion Quiz

Lecture 89 Data Structure: Python Practice-Insertion and Deletion Solution

Lecture 90 Data Structure: Deep Copy or Reference Slicing

Lecture 91 Data Structure: Deep Copy or Reference Slicing Quiz

Lecture 92 Data Structure: Deep Copy or Reference Slicing Solution

Lecture 93 Data Structure: Exploring Methods Using TAB Completion

Lecture 94 Data Structure: Data Structure Abstract Ways

Lecture 95 Data Structure: Data Structure Practice

Lecture 96 Data Structure: Data Structure Practice Quiz

Lecture 97 Data Structure: Data Structure Practice Solution

Section 2: Mastering Probability & Statistic Python (Theory & Projects)

Lecture 98 Link to the Python codes for the projects and the data

Lecture 99 Introduction: Introduction to Instructor and AISciences

Lecture 100 Introduction: Introduction To Instructor

Lecture 101 Introduction: Focus of the Course

Lecture 102 Probability vs Statistics: Probability vs Statistics

Lecture 103 Sets: Definition of Set

Lecture 104 Sets: Cardinality of a Set

Lecture 105 Sets: Subsets PowerSet UniversalSet

Lecture 106 Sets: Python Practice Subsets

Lecture 107 Sets: PowerSets Solution

Lecture 108 Sets: Operations

Lecture 109 Sets: Operations Exercise 01

Lecture 110 Sets: Operations Solution 01

Lecture 111 Sets: Operations Exercise 02

Lecture 112 Sets: Operations Solution 02

Lecture 113 Sets: Operations Exercise 03

Lecture 114 Sets: Operations Solution 03

Lecture 115 Sets: Python Practice Operations

Lecture 116 Sets: VennDiagrams Operations

Lecture 117 Sets: Homework

Lecture 118 Experiment: Random Experiment

Lecture 119 Experiment: Outcome and Sample Space

Lecture 120 Experiment: Outcome and Sample Space Exercise 01

Lecture 121 Experiment: Outcome and Sample Space Solution 01

Lecture 122 Experiment: Event

Lecture 123 Experiment: Event Exercise 01

Lecture 124 Experiment: Event Solution 01

Lecture 125 Experiment: Event Exercise 02

Lecture 126 Experiment: Event Solution 02

Lecture 127 Experiment: Recap and Homework

Lecture 128 Probability Model: Probability Model

Lecture 129 Probability Model: Probability Axioms

Lecture 130 Probability Model: Probability Axioms Derivations

Lecture 131 Probability Model: Probability Axioms Derivations Exercise 01

Lecture 132 Probability Model: Probability Axioms Derivations Solution 01

Lecture 133 Probability Model: Probablility Models Example

Lecture 134 Probability Model: Probablility Models More Examples

Lecture 135 Probability Model: Probablility Models Continous

Lecture 136 Probability Model: Conditional Probability

Lecture 137 Probability Model: Conditional Probability Example

Lecture 138 Probability Model: Conditional Probability Formula

Lecture 139 Probability Model: Conditional Probability in Machine Learning

Lecture 140 Probability Model: Conditional Probability Total Probability Theorem

Lecture 141 Probability Model: Probablility Models Independence

Lecture 142 Probability Model: Probablility Models Conditional Independence

Lecture 143 Probability Model: Probablility Models Conditional Independence Exercise 01

Lecture 144 Probability Model: Probablility Models Conditional Independence Solution 01

Lecture 145 Probability Model: Probablility Models BayesRule

Lecture 146 Probability Model: Probablility Models towards Random Variables

Lecture 147 Probability Model: HomeWork

Lecture 148 Random Variables: Introduction

Lecture 149 Random Variables: Random Variables Examples

Lecture 150 Random Variables: Random Variables Examples Exercise 01

Lecture 151 Random Variables: Random Variables Examples Solution 01

Lecture 152 Random Variables: Bernulli Random Variables

Lecture 153 Random Variables: Bernulli Trail Python Practice

Lecture 154 Random Variables: Bernulli Trail Python Practice Exercise 01

Lecture 155 Random Variables: Bernulli Trail Python Practice Solution 01

Lecture 156 Random Variables: Geometric Random Variable

Lecture 157 Random Variables: Geometric Random Variable Normalization Proof Optional

Lecture 158 Random Variables: Geometric Random Variable Python Practice

Lecture 159 Random Variables: Binomial Random Variables

Lecture 160 Random Variables: Binomial Python Practice

Lecture 161 Random Variables: Random Variables in Real DataSets

Lecture 162 Random Variables: Random Variables in Real DataSets Exercise 01

Lecture 163 Random Variables: Random Variables in Real DataSets Solution 01

Lecture 164 Random Variables: Homework

Lecture 165 Continous Random Variables: Zero Probability to Individual Values

Lecture 166 Continous Random Variables: Zero Probability to Individual Values Exercise 01

Lecture 167 Continous Random Variables: Zero Probability to Individual Values Solution 01

Lecture 168 Continous Random Variables: Probability Density Functions

Lecture 169 Continous Random Variables: Probability Density Functions Exercise 01

Lecture 170 Continous Random Variables: Probability Density Functions Solution 01

Lecture 171 Continous Random Variables: Uniform Distribution

Lecture 172 Continous Random Variables: Uniform Distribution Exercise 01

Lecture 173 Continous Random Variables: Uniform Distribution Solution 01

Lecture 174 Continous Random Variables: Uniform Distribution Python

Lecture 175 Continous Random Variables: Exponential

Lecture 176 Continous Random Variables: Exponential Exercise 01

Lecture 177 Continous Random Variables: Exponential Solution 01

Lecture 178 Continous Random Variables: Exponential Python

Lecture 179 Continous Random Variables: Gaussian Random Variables

Lecture 180 Continous Random Variables: Gaussian Random Variables Exercise 01

Lecture 181 Continous Random Variables: Gaussian Random Variables Solution 01

Lecture 182 Continous Random Variables: Gaussian Python

Lecture 183 Continous Random Variables: Transformation of Random Variables

Lecture 184 Continous Random Variables: Homework

Lecture 185 Expectations: Definition

Lecture 186 Expectations: Sample Mean

Lecture 187 Expectations: Law of Large Numbers

Lecture 188 Expectations: Law of Large Numbers Famous Distributions

Lecture 189 Expectations: Law of Large Numbers Famous Distributions Python

Lecture 190 Expectations: Variance

Lecture 191 Expectations: Homework

Lecture 192 Project Bayes Classifier: Project Bayes Classifier From Scratch

Lecture 193 Multiple Random Variables: Joint Distributions

Lecture 194 Multiple Random Variables: Joint Distributions Exercise 01

Lecture 195 Multiple Random Variables: Joint Distributions Solution 01

Lecture 196 Multiple Random Variables: Joint Distributions Exercise 02

Lecture 197 Multiple Random Variables: Joint Distributions Solution 02

Lecture 198 Multiple Random Variables: Joint Distributions Exercise 03

Lecture 199 Multiple Random Variables: Joint Distributions Solution 03

Lecture 200 Multiple Random Variables: Multivariate Gaussian

Lecture 201 Multiple Random Variables: Conditioning Independence

Lecture 202 Multiple Random Variables: Classification

Lecture 203 Multiple Random Variables: Naive Bayes Classification

Lecture 204 Multiple Random Variables: Regression

Lecture 205 Multiple Random Variables: Curse of Dimensionality

Lecture 206 Multiple Random Variables: Homework

Lecture 207 Optional Estimation: Parametric Distributions

Lecture 208 Optional Estimation: MLE

Lecture 209 Optional Estimation: LogLiklihood

Lecture 210 Optional Estimation: MAP

Lecture 211 Optional Estimation: Logistic Regression

Lecture 212 Optional Estimation: Ridge Regression

Lecture 213 Optional Estimation: DNN

Lecture 214 Mathematical Derivations for Math Lovers: Permutations

Lecture 215 Mathematical Derivations for Math Lovers: Combinations

Lecture 216 Mathematical Derivations for Math Lovers: Binomial Random Variable

Lecture 217 Mathematical Derivations for Math Lovers: Logistic Regression Formulation

Lecture 218 Mathematical Derivations for Math Lovers: Logistic Regression Derivation

Lecture 219 THANK YOU

Section 3: Statistics: Statistical Modeling Made Easy for ALL

Lecture 220 Link to the Python codes for the projects and the data

Lecture 221 Introduction: Course Introduction

Lecture 222 Introduction: AI Sciences

Lecture 223 Introduction: Course Outline

Lecture 224 Summary Statistics: Module Intoduction

Lecture 225 Summary Statistics: Overview

Lecture 226 Summary Statistics: Summary Statistics

Lecture 227 Summary Statistics: Average Types

Lecture 228 Summary Statistics: Mean

Lecture 229 Summary Statistics: Median

Lecture 230 Summary Statistics: Median Example

Lecture 231 Summary Statistics: Mode

Lecture 232 Summary Statistics: Case Study For Average

Lecture 233 Summary Statistics: IQR

Lecture 234 Summary Statistics: Variance

Lecture 235 Summary Statistics: Standard Deviation

Lecture 236 Summary Statistics: Averages in Python

Lecture 237 Summary Statistics: Std Deviation and Variance in Python

Lecture 238 Summary Statistics: IQR in Python

Lecture 239 Hypothesis Testing: Module Introduction

Lecture 240 Hypothesis Testing: Hypothesis Testing Overview

Lecture 241 Hypothesis Testing: Terminologies in Hypothesis Testing

Lecture 242 Hypothesis Testing: Null Hypothesis

Lecture 243 Hypothesis Testing: Alternate Hypothesis

Lecture 244 Hypothesis Testing: Test Statistics

Lecture 245 Hypothesis Testing: P-Value

Lecture 246 Hypothesis Testing: Critical Value

Lecture 247 Hypothesis Testing: Level of Significance

Lecture 248 Hypothesis Testing: Case Study 1

Lecture 249 Hypothesis Testing: Case Study 2

Lecture 250 Hypothesis Testing: Calculations for Python

Lecture 251 Hypothesis Testing: Steps of Hypothesis Testing

Lecture 252 Hypothesis Testing: Code Outcomes

Lecture 253 Hypothesis Testing: Calculation of Z in Python

Lecture 254 Hypothesis Testing: Norm Function

Lecture 255 Hypothesis Testing: P Value Python

Lecture 256 Correlation and Regression: Module Introduction

Lecture 257 Correlation and Regression: Covariance and Correlation

Lecture 258 Correlation and Regression: Correlation

Lecture 259 Correlation and Regression: Regression

Lecture 260 Correlation and Regression: Correlation and Covariance in Python

Lecture 261 Correlation and Regression: Entering Input

Lecture 262 Correlation and Regression: Linear Regression Results

Lecture 263 Multiple Regression: Module Overview

Lecture 264 Multiple Regression: Motivation for Multiple Regression

Lecture 265 Multiple Regression: Formula for MR

Lecture 266 Multiple Regression: Preparing the Data

Lecture 267 Multiple Regression: Multiple Regression in Python

Beginners in Python and Data Science,Python Enthusiasts looking to apply skills in Data Analysis,Aspiring Data Scientists seeking a strong foundation,Professionals aiming to enhance their statistical modeling skills