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Python Mastery For Data, Statistics & Statistical Modeling - BaDshaH - 11-23-2023
![[Image: Kxf-QUb-GKVc8cqf-Ih-ZVC12-Q2wf-LBTdl-Vx.jpg]](https://i.postimg.cc/rsMTxQRV/Kxf-QUb-GKVc8cqf-Ih-ZVC12-Q2wf-LBTdl-Vx.jpg)
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
[b]What you'll learn[/b]
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
[b]Requirements[/b]
No prior knowledge or experience is required. Everything is explained from absolute basics.
[b]Description[/b]
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
ython 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 ManipulationOverview
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
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