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    Python Mastery For Data, Statistics & Statistical Modeling

    Posted By: ELK1nG
    Python Mastery For Data, Statistics & Statistical Modeling

    Python Mastery For Data, Statistics & Statistical Modeling
    Published 11/2023
    MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
    Language: English | Size: 7.04 GB | Duration: 28h 7m

    Python Mastery for Data Science & Statistical Modeling: Basics to Advanced Applications in Data Analysis, Visualization

    What you'll learn

    Solid grasp of Python programming for Data Science & Statistics

    Practical experience through hands-on projects and case studies

    Ability to apply Statistical Modeling techniques using Python

    Understanding of real-world applications in Data Analysis and Machine Learning

    Requirements

    No prior knowledge or experience is required. Everything is explained from absolute basics.

    Description

    Unlock the world of data science and statistical modeling with our comprehensive course, Python for Data Science & Statistical Modeling. Whether you're a novice or looking to enhance your skills, this course provides a structured pathway to mastering Python for data science and delving into the fascinating world of statistical modeling.Module 1: Python Fundamentals for Data ScienceDive into the foundations of Python for data science, where you'll learn the essentials that form the basis of your data journey.Session 1: Introduction to Python & Data ScienceSession 2: Python Syntax & Control FlowSession 3: Data Structures in PythonSession 4: Introduction to Numpy & Pandas for Data ManipulationModule 2: Data Science Essentials with PythonExplore the core components of data science using Python, including exploratory data analysis, visualization, and machine learning.Session 5: Exploratory Data Analysis with Pandas & NumpySession 6: Data Visualization with Matplotlib, Seaborn & BokehSession 7: Introduction to Scikit-Learn for Machine Learning in PythonModule 3: Mastering Probability, Statistics & Machine LearningGain in-depth knowledge of probability, statistics, and their seamless integration with Python's powerful machine learning capabilities.Session 8: Difference between Probability and StatisticsSession 9: Set Theory and Probability ModelsSession 10: Random Variables and DistributionsSession 11: Expectation, Variance, and MomentsModule 4: Practical Statistical Modeling with PythonApply your understanding of probability and statistics to build statistical models and explore their real-world applications.Session 12: Probability and Statistical Modeling in PythonSession 13: Estimation Techniques & Maximum Likelihood EstimateSession 14: Logistic Regression and KL-DivergenceSession 15: Connecting Probability, Statistics & Machine Learning in PythonModule 5: Statistical Modeling Made EasySimplify statistical modeling with Python, covering summary statistics, hypothesis testing, correlation, and more.Session 16: Overview of Summary Statistics in PythonSession 17: Introduction to Hypothesis TestingSession 18: Null and Alternate Hypothesis with PythonSession 19: Correlation and Covariance in PythonModule 6: Implementing Statistical ModelsDelve deeper into implementing statistical models with Python, including linear regression, multiple regression, and custom models.Session 20: Linear Regression and CoefficientsSession 21: Testing for Correlation in PythonSession 22: Multiple Regression and F-TestSession 23: Building Custom Statistical Models with Python AlgorithmsModule 7: Capstone Projects & Real-World ApplicationsPut your skills to the test with hands-on projects, case studies, and real-world applications.Session 24: Mini-projects integrating Python, Data Science & StatisticsSession 25: Case Study 1: Real-world applications of Statistical ModelsSession 26: Case Study 2: Python-based Data Analysis & VisualizationModule 8: Conclusion & Next StepsWrap up your journey with a recap of key concepts and guidance on advancing your data science career.Session 27: Recap & Summary of Key ConceptsSession 28: Continuing Your Learning Path in Data Science & PythonJoin us on this transformative learning adventure, where you'll gain the skills and knowledge to excel in data science, statistical modeling, and Python. Enroll now and embark on your path to data-driven success!Who Should Take This Course?Aspiring Data ScientistsData AnalystsBusiness AnalystsStudents pursuing a career in data-related fieldsAnyone interested in harnessing Python for data insightsWhy This Course?In today's data-driven world, proficiency in Python and statistical modeling is a highly sought-after skillset. This course empowers you with the knowledge and practical experience needed to excel in data analysis, visualization, and modeling using Python. Whether you're aiming to kickstart your career, enhance your current role, or simply explore the world of data, this course provides the foundation you need.  What You Will Learn:This course is structured to take you from Python fundamentals to advanced statistical modeling, equipping you with the skills to:Master Python syntax and data structures for effective data manipulationExplore exploratory data analysis techniques using Pandas and NumpyCreate compelling data visualizations using Matplotlib, Seaborn, and BokehDive into Scikit-Learn for machine learning in PythonUnderstand key concepts in probability and statisticsApply statistical modeling techniques in real-world scenariosBuild custom statistical models using Python algorithmsPerform hypothesis testing and correlation analysisImplement linear and multiple regression modelsWork on hands-on projects and real-world case studiesKeywords:Python for Data Science, Statistical Modeling, Data Analysis, Data Visualization, Machine Learning, Pandas, Numpy, Matplotlib, Seaborn, Bokeh, Scikit-Learn, Probability, Statistics, Hypothesis Testing, Regression Analysis, Data Insights, Python Syntax, Data Manipulation

    Overview

    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