Nov 23 2023 Python Mastery For Data, Statistics & Statistical Modeling BaDshaH LEARNING / e-learning - Tutorials 07:45 0 Published 11/2023MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHzLanguage: English | Size: 7.04 GB | Duration: 28h 7mPython Mastery for Data Science & Statistical Modeling: Basics to Advanced Applications in Data Analysis, Visualization What you'll learnSolid grasp of Python programming for Data Science & StatisticsPractical experience through hands-on projects and case studiesAbility to apply Statistical Modeling techniques using PythonUnderstanding of real-world applications in Data Analysis and Machine LearningRequirementsNo prior knowledge or experience is required. Everything is explained from absolute basics.DescriptionUnlock 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 ManipulationOverviewSection 1: Python for Data Science and Data AnalysisLecture 1 Link to the Python codes for the projects and the dataLecture 2 Introduction: About the Tutor and AI SciencesLecture 3 Introduction: Introduction To InstructorLecture 4 Introduction: Focus of the Course-Part 1Lecture 5 Introduction: Focus of the Course- Part 2Lecture 6 Basics of Programming: Understanding the AlgorithmLecture 7 Basics of Programming: FlowCharts and PseudocodesLecture 8 Basics of Programming: Example of Algorithms- Making Tea ProblemLecture 9 Basics of Programming: Example of Algorithms-Searching MinimunLecture 10 Basics of Programming: Example of Algorithms-Searching Minimun QuizLecture 11 Basics of Programming: Example of Algorithms-Sorting ProblemLecture 12 Basics of Programming: Example of Algorithms-Searching Minimun SolutionLecture 13 Basics of Programming: Sorting Problem in PythonLecture 14 Why Python and Jupyter Notebook: Why PythonLecture 15 Why Python and Jupyter Notebook: Why Jupyter NotebooksLecture 16 Installation of Anaconda and IPython Shell: Installing Python and Jupyter AnacondaLecture 17 Installation of Anaconda and IPython Shell: Your First Python Code- Hello WorldLecture 18 Installation of Anaconda and IPython Shell: Coding in IPython ShellLecture 19 Variable and Operator: VariablesLecture 20 Variable and Operator: OperatorsLecture 21 Variable and Operator: Variable Name QuizLecture 22 Variable and Operator: Bool Data Type in PythonLecture 23 Variable and Operator: Comparison in PythonLecture 24 Variable and Operator: Combining Comparisons in PythonLecture 25 Variable and Operator: Combining Comparisons QuizLecture 26 Python Useful function: Python Function- RoundLecture 27 Python Useful function: Python Function- Round QuizLecture 28 Python Useful function: Python Function- Round SolutionLecture 29 Python Useful function: Python Function- DivmodLecture 30 Python Useful function: Python Function- Is instance and PowFunctionsLecture 31 Python Useful function: Python Function- InputLecture 32 Control Flow in Python: If Python ConditionLecture 33 Control Flow in Python: if Elif Else Python ConditionsLecture 34 Control Flow in Python: if Elif Else Python Conditions QuizLecture 35 Control Flow in Python: if Elif Else Python Conditions SolutionLecture 36 Control Flow in Python: More on if Elif Else Python ConditionsLecture 37 Control Flow in Python: More on if Elif Else Python Conditions QuizLecture 38 Control Flow in Python: More on if Elif Else Python Conditions SolutionLecture 39 Control Flow in Python: IndentationsLecture 40 Control Flow in Python: Indentations QuizLecture 41 Control Flow in Python: Indentations SolutionLecture 42 Control Flow in Python: Comments and Problem Solving Practice With IfLecture 43 Control Flow in Python: While LoopLecture 44 Control Flow in Python: While Loop break ContinueLecture 45 Control Flow in Python: While Loop break Continue QuizLecture 46 Control Flow in Python: While Loop break Continue SolutionLecture 47 Control Flow in Python: For LoopLecture 48 Control Flow in Python: For Loop QuizLecture 49 Control Flow in Python: For Loop SolutionLecture 50 Control Flow in Python: Else In For LoopLecture 51 Control Flow in Python: Loops Practice-Sorting ProblemLecture 52 Function and Module in Python: Functions in PythonLecture 53 Function and Module in Python: DocStringLecture 54 Function and Module in Python: Input ArgumentsLecture 55 Function and Module in Python: Multiple Input ArgumentsLecture 56 Function and Module in Python: Multiple Input Arguments QuizLecture 57 Function and Module in Python: Multiple Input Arguments SolutionLecture 58 Function and Module in Python: Ordering Multiple Input ArgumentsLecture 59 Function and Module in Python: Output Arguments and Return StatementLecture 60 Function and Module in Python: Function Practice-Output Arguments and Return StatementLecture 61 Function and Module in Python: Variable Number of Input ArgumentsLecture 62 Function and Module in Python: Variable Number of Input Arguments QuizLecture 63 Function and Module in Python: Variable Number of Input Arguments SolutionLecture 64 Function and Module in Python: Variable Number of Input Arguments as DictionaryLecture 65 Function and Module in Python: Variable Number of Input Arguments as Dictionary QuizLecture 66 Function and Module in Python: Variable Number of Input Arguments as Dictionary SolutionLecture 67 Function and Module in Python: Default Values in PythonLecture 68 Function and Module in Python: Modules in PythonLecture 69 Function and Module in Python: Making Modules in PythonLecture 70 Function and Module in Python: Function Practice-Sorting List in PythonLecture 71 String in Python: StringsLecture 72 String in Python: Multi Line StringsLecture 73 String in Python: Indexing StringsLecture 74 String in Python: Indexing Strings QuizLecture 75 String in Python: Indexing Strings SolutionLecture 76 String in Python: String MethodsLecture 77 String in Python: String Methods QuizLecture 78 String in Python: String Methods SolutionLecture 79 String in Python: String Escape SequencesLecture 80 String in Python: String Escape Sequences QuizLecture 81 String in Python: String Escape Sequences SolutionLecture 82 Data Structure: Introduction to Data StructureLecture 83 Data Structure: Defining and IndexingLecture 84 Data Structure: Insertion and DeletionLecture 85 Data Structure: Insertion and Deletion QuizLecture 86 Data Structure: Insertion and Deletion SolutionLecture 87 Data Structure: Python Practice-Insertion and DeletionLecture 88 Data Structure: Python Practice-Insertion and Deletion QuizLecture 89 Data Structure: Python Practice-Insertion and Deletion SolutionLecture 90 Data Structure: Deep Copy or Reference SlicingLecture 91 Data Structure: Deep Copy or Reference Slicing QuizLecture 92 Data Structure: Deep Copy or Reference Slicing SolutionLecture 93 Data Structure: Exploring Methods Using TAB CompletionLecture 94 Data Structure: Data Structure Abstract WaysLecture 95 Data Structure: Data Structure PracticeLecture 96 Data Structure: Data Structure Practice QuizLecture 97 Data Structure: Data Structure Practice SolutionSection 2: Mastering Probability & Statistic Python (Theory & Projects)Lecture 98 Link to the Python codes for the projects and the dataLecture 99 Introduction: Introduction to Instructor and AISciencesLecture 100 Introduction: Introduction To InstructorLecture 101 Introduction: Focus of the CourseLecture 102 Probability vs Statistics: Probability vs StatisticsLecture 103 Sets: Definition of SetLecture 104 Sets: Cardinality of a SetLecture 105 Sets: Subsets PowerSet UniversalSetLecture 106 Sets: Python Practice SubsetsLecture 107 Sets: PowerSets SolutionLecture 108 Sets: OperationsLecture 109 Sets: Operations Exercise 01Lecture 110 Sets: Operations Solution 01Lecture 111 Sets: Operations Exercise 02Lecture 112 Sets: Operations Solution 02Lecture 113 Sets: Operations Exercise 03Lecture 114 Sets: Operations Solution 03Lecture 115 Sets: Python Practice OperationsLecture 116 Sets: VennDiagrams OperationsLecture 117 Sets: HomeworkLecture 118 Experiment: Random ExperimentLecture 119 Experiment: Outcome and Sample SpaceLecture 120 Experiment: Outcome and Sample Space Exercise 01Lecture 121 Experiment: Outcome and Sample Space Solution 01Lecture 122 Experiment: EventLecture 123 Experiment: Event Exercise 01Lecture 124 Experiment: Event Solution 01Lecture 125 Experiment: Event Exercise 02Lecture 126 Experiment: Event Solution 02Lecture 127 Experiment: Recap and HomeworkLecture 128 Probability Model: Probability ModelLecture 129 Probability Model: Probability AxiomsLecture 130 Probability Model: Probability Axioms DerivationsLecture 131 Probability Model: Probability Axioms Derivations Exercise 01Lecture 132 Probability Model: Probability Axioms Derivations Solution 01Lecture 133 Probability Model: Probablility Models ExampleLecture 134 Probability Model: Probablility Models More ExamplesLecture 135 Probability Model: Probablility Models ContinousLecture 136 Probability Model: Conditional ProbabilityLecture 137 Probability Model: Conditional Probability ExampleLecture 138 Probability Model: Conditional Probability FormulaLecture 139 Probability Model: Conditional Probability in Machine LearningLecture 140 Probability Model: Conditional Probability Total Probability TheoremLecture 141 Probability Model: Probablility Models IndependenceLecture 142 Probability Model: Probablility Models Conditional IndependenceLecture 143 Probability Model: Probablility Models Conditional Independence Exercise 01Lecture 144 Probability Model: Probablility Models Conditional Independence Solution 01Lecture 145 Probability Model: Probablility Models BayesRuleLecture 146 Probability Model: Probablility Models towards Random VariablesLecture 147 Probability Model: HomeWorkLecture 148 Random Variables: IntroductionLecture 149 Random Variables: Random Variables ExamplesLecture 150 Random Variables: Random Variables Examples Exercise 01Lecture 151 Random Variables: Random Variables Examples Solution 01Lecture 152 Random Variables: Bernulli Random VariablesLecture 153 Random Variables: Bernulli Trail Python PracticeLecture 154 Random Variables: Bernulli Trail Python Practice Exercise 01Lecture 155 Random Variables: Bernulli Trail Python Practice Solution 01Lecture 156 Random Variables: Geometric Random VariableLecture 157 Random Variables: Geometric Random Variable Normalization Proof OptionalLecture 158 Random Variables: Geometric Random Variable Python PracticeLecture 159 Random Variables: Binomial Random VariablesLecture 160 Random Variables: Binomial Python PracticeLecture 161 Random Variables: Random Variables in Real DataSetsLecture 162 Random Variables: Random Variables in Real DataSets Exercise 01Lecture 163 Random Variables: Random Variables in Real DataSets Solution 01Lecture 164 Random Variables: HomeworkLecture 165 Continous Random Variables: Zero Probability to Individual ValuesLecture 166 Continous Random Variables: Zero Probability to Individual Values Exercise 01Lecture 167 Continous Random Variables: Zero Probability to Individual Values Solution 01Lecture 168 Continous Random Variables: Probability Density FunctionsLecture 169 Continous Random Variables: Probability Density Functions Exercise 01Lecture 170 Continous Random Variables: Probability Density Functions Solution 01Lecture 171 Continous Random Variables: Uniform DistributionLecture 172 Continous Random Variables: Uniform Distribution Exercise 01Lecture 173 Continous Random Variables: Uniform Distribution Solution 01Lecture 174 Continous Random Variables: Uniform Distribution PythonLecture 175 Continous Random Variables: ExponentialLecture 176 Continous Random Variables: Exponential Exercise 01Lecture 177 Continous Random Variables: Exponential Solution 01Lecture 178 Continous Random Variables: Exponential PythonLecture 179 Continous Random Variables: Gaussian Random VariablesLecture 180 Continous Random Variables: Gaussian Random Variables Exercise 01Lecture 181 Continous Random Variables: Gaussian Random Variables Solution 01Lecture 182 Continous Random Variables: Gaussian PythonLecture 183 Continous Random Variables: Transformation of Random VariablesLecture 184 Continous Random Variables: HomeworkLecture 185 Expectations: DefinitionLecture 186 Expectations: Sample MeanLecture 187 Expectations: Law of Large NumbersLecture 188 Expectations: Law of Large Numbers Famous DistributionsLecture 189 Expectations: Law of Large Numbers Famous Distributions PythonLecture 190 Expectations: VarianceLecture 191 Expectations: HomeworkLecture 192 Project Bayes Classifier: Project Bayes Classifier From ScratchLecture 193 Multiple Random Variables: Joint DistributionsLecture 194 Multiple Random Variables: Joint Distributions Exercise 01Lecture 195 Multiple Random Variables: Joint Distributions Solution 01Lecture 196 Multiple Random Variables: Joint Distributions Exercise 02Lecture 197 Multiple Random Variables: Joint Distributions Solution 02Lecture 198 Multiple Random Variables: Joint Distributions Exercise 03Lecture 199 Multiple Random Variables: Joint Distributions Solution 03Lecture 200 Multiple Random Variables: Multivariate GaussianLecture 201 Multiple Random Variables: Conditioning IndependenceLecture 202 Multiple Random Variables: ClassificationLecture 203 Multiple Random Variables: Naive Bayes ClassificationLecture 204 Multiple Random Variables: RegressionLecture 205 Multiple Random Variables: Curse of DimensionalityLecture 206 Multiple Random Variables: HomeworkLecture 207 Optional Estimation: Parametric DistributionsLecture 208 Optional Estimation: MLELecture 209 Optional Estimation: LogLiklihoodLecture 210 Optional Estimation: MAPLecture 211 Optional Estimation: Logistic RegressionLecture 212 Optional Estimation: Ridge RegressionLecture 213 Optional Estimation: DNNLecture 214 Mathematical Derivations for Math Lovers: PermutationsLecture 215 Mathematical Derivations for Math Lovers: CombinationsLecture 216 Mathematical Derivations for Math Lovers: Binomial Random VariableLecture 217 Mathematical Derivations for Math Lovers: Logistic Regression FormulationLecture 218 Mathematical Derivations for Math Lovers: Logistic Regression DerivationLecture 219 THANK YOUSection 3: Statistics: Statistical Modeling Made Easy for ALLLecture 220 Link to the Python codes for the projects and the dataLecture 221 Introduction: Course IntroductionLecture 222 Introduction: AI SciencesLecture 223 Introduction: Course OutlineLecture 224 Summary Statistics: Module IntoductionLecture 225 Summary Statistics: OverviewLecture 226 Summary Statistics: Summary StatisticsLecture 227 Summary Statistics: Average TypesLecture 228 Summary Statistics: MeanLecture 229 Summary Statistics: MedianLecture 230 Summary Statistics: Median ExampleLecture 231 Summary Statistics: ModeLecture 232 Summary Statistics: Case Study For AverageLecture 233 Summary Statistics: IQRLecture 234 Summary Statistics: VarianceLecture 235 Summary Statistics: Standard DeviationLecture 236 Summary Statistics: Averages in PythonLecture 237 Summary Statistics: Std Deviation and Variance in PythonLecture 238 Summary Statistics: IQR in PythonLecture 239 Hypothesis Testing: Module IntroductionLecture 240 Hypothesis Testing: Hypothesis Testing OverviewLecture 241 Hypothesis Testing: Terminologies in Hypothesis TestingLecture 242 Hypothesis Testing: Null HypothesisLecture 243 Hypothesis Testing: Alternate HypothesisLecture 244 Hypothesis Testing: Test StatisticsLecture 245 Hypothesis Testing: P-ValueLecture 246 Hypothesis Testing: Critical ValueLecture 247 Hypothesis Testing: Level of SignificanceLecture 248 Hypothesis Testing: Case Study 1Lecture 249 Hypothesis Testing: Case Study 2Lecture 250 Hypothesis Testing: Calculations for PythonLecture 251 Hypothesis Testing: Steps of Hypothesis TestingLecture 252 Hypothesis Testing: Code OutcomesLecture 253 Hypothesis Testing: Calculation of Z in PythonLecture 254 Hypothesis Testing: Norm FunctionLecture 255 Hypothesis Testing: P Value PythonLecture 256 Correlation and Regression: Module IntroductionLecture 257 Correlation and Regression: Covariance and CorrelationLecture 258 Correlation and Regression: CorrelationLecture 259 Correlation and Regression: RegressionLecture 260 Correlation and Regression: Correlation and Covariance in PythonLecture 261 Correlation and Regression: Entering InputLecture 262 Correlation and Regression: Linear Regression ResultsLecture 263 Multiple Regression: Module OverviewLecture 264 Multiple Regression: Motivation for Multiple RegressionLecture 265 Multiple Regression: Formula for MRLecture 266 Multiple Regression: Preparing the DataLecture 267 Multiple Regression: Multiple Regression in PythonBeginners 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 skillsHomepagehttps://www.udemy.com/course/python-mastery-for-data-statistics-statistical-modeling/https://rapidgator.net/file/cdc9954745cc06457a77bb82c94dc7e1https://rapidgator.net/file/3eebf2ba5bcc5692a0a6b6a585246879https://rapidgator.net/file/0b26e58ec39ccda002ffb508f6d10ddbhttps://rapidgator.net/file/5cbb94c24542d13d132abd685399024dhttps://rapidgator.net/file/678b689d8e48300a7c1c729764a65198https://rapidgator.net/file/3e6c26f3496aa0506183c4af7fe0a0afhttps://rapidgator.net/file/b70e693337d7e2f2f51f8416fd65c943https://rapidgator.net/file/063fd3f7af8c744b1ba970bdeb5759a5https://nitroflare.com/view/E954F860943D63Ehttps://nitroflare.com/view/B3A41E8524B9E71https://nitroflare.com/view/9ABF678077D311Ahttps://nitroflare.com/view/F0ED3FF85D725D3https://nitroflare.com/view/93B9BBB2FCAB24Dhttps://nitroflare.com/view/0FB84A30AA1527Ehttps://nitroflare.com/view/2E72269A2F1127Ahttps://nitroflare.com/view/25535E06C3BADB3 Related News Udemy - Complete 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