Python Mastery For Data, Statistics & Statistical Modeling

Published 11/2023
MP4 | Video: h264, 1920×1080 | 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

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