Dec 31 2022 Data Science With Python (4-Course Bundle) BaDshaH LEARNING / e-learning - Tutorials 13:32 0 Last updated 12/2020MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 7.47 GB | Duration: 17h 37mLearn the data life cycle-from acquisition to processing to analysis-in Python What you'll learnEffectively pre-process data (structured or unstructured) before doing any analysis on the datasetPerform statistical analysis using in-built Python librariesLearn tricks and techniques that will be invaluable throughout your data science careerLearn how to deal with missing data and outliers to resolve data inconsistenciesEnhance your programming skills and master data exploration and visualization in PythonExplore and work with different plotting librariesWork with industry-standard tools like Matplotlib, Seaborn, and BokehGain knowledge on how to prepare data and feed it to machine learning algorithmsRequirementsBasic Python programming experience is required before undertaking the course.DescriptionIf you're a Python developer and looking to start your journey in data science, then this course is for you. This 5-course bundle takes you from zero experience to a complete understanding of key concepts, edge cases, and using Python for real-world application development. You'll move progressively from the basics to working with larger complex applications. After completing this course, you'll have the skills you need to dive into an existing application or start your own project.Course 1:In this course, you will gather data, prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, and more! This course will equip us with the tools and technologies, also we need to analyze the datasets using Python so that we can confidently jump into the field and enhance our skill set. The best part of this course is the takeaway code templates generated using the real-life dataset.Course 2:Next, you will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more.Course 3:You'll study different types of visualizations, compare them, and find out how to select a particular type of visualization using this comparison. You'll explore different plots, including custom creations. After you get a hang of the various visualization libraries, you'll learn to work with Matplotlib and Seaborn to simplify the process of creating visualizations. You'll also be introduced to advanced visualization techniques, such as geoplots and interactive plots. You'll learn how to make sense of geospatial data, create interactive visualizations that can be integrated into any webpage, and take any dataset to build beautiful and insightful visualizations.Course 4:This course will start you on your journey to mastering topics within machine learning. These skills will help you deliver the kind of state-of-the-art predictive models that are being used to deliver value to businesses across industries.OverviewSection 1: Data Wrangling with Python 3.xLecture 1 The Course OverviewLecture 2 Installing Anaconda Navigator on Windows/LinuxLecture 3 Importing and Parsing CSV in PythonLecture 4 Importing and Parsing JSON in PythonLecture 5 Scraping Data from Public Web – Part 1Lecture 6 Scraping Data from Public Web – Part 2Lecture 7 Importing and Parsing Excel Files – Part 1Lecture 8 Importing and Parsing Excel Files – Part 2Lecture 9 Manipulating PDF Files in Python – Part 1Lecture 10 Manipulating PDF Files in Python – Part 2Lecture 11 Difference between Relational and Non-Relational DatabasesLecture 12 Storing Data in SQLite DatabasesLecture 13 Storing Data in MongoDBLecture 14 Storing Data in ElasticsearchLecture 15 Comparative Study of Databases for StorageLecture 16 The Most Important Step in Data AnalysisLecture 17 Viewing/Inspecting DataFramesLecture 18 Renaming/Adding/Removing the DataFrame ColumnsLecture 19 Dropping Duplicate RowsLecture 20 Indexing DataFrame to Retrieve Specific Columns and RowsLecture 21 Merging/Concatenating/Joining DataFramesLecture 22 Dealing with Missing ValuesLecture 23 Filtering and Sorting of DataFrameLecture 24 Encoding/Mapping Existing Values – Part 1Lecture 25 Encoding/Mapping Existing Values – Part 2Lecture 26 Rescale/Standardize Column ValuesLecture 27 Common Cleaning OperationsLecture 28 Exporting Datasets for Future UseLecture 29 Different Uses of Packages (Pandas, NumPy, SciPy, and Matplotlib)Lecture 30 Types of Column Names/Features/Attributes in Structured DataLecture 31 Split-Apply-Combine (Performing Group By Operation)Lecture 32 Descriptive Statistics Using Python – Part 1Lecture 33 Descriptive Statistics Using Python – Part 2Lecture 34 Using VisualizationsLecture 35 Cool Visualization of Real-World Datasets of World Population EvolutionLecture 36 Visualizations in Python – Part 1Lecture 37 Visualizations in Python – Part 2Lecture 38 Exploring an Online Visualization Tool (RAWGraphs)Section 2: Exploratory Data Analysis with Pandas and Python 3.xLecture 39 The Course OverviewLecture 40 Basic Statistical MeasuresLecture 41 Variance and Standard DeviationLecture 42 Visualizing Statistical MeasuresLecture 43 Calculating PercentilesLecture 44 Quartiles and Box PlotsLecture 45 Finding Missing ValuesLecture 46 Dealing with Missing ValuesLecture 47 Hands-on with Dealing with Missing ValuesLecture 48 Case Study: Missing Data in Titanic DatasetLecture 49 What are Outliers?Lecture 50 Using Z-scores to Find OutliersLecture 51 Modified Z-scoresLecture 52 Using IQR to Detect OutliersLecture 53 Types of VariablesLecture 54 Introduction to Univariate AnalysisLecture 55 Skewness and KurtosisLecture 56 Univariate Analysis over Olympics DatasetLecture 57 Introduction to Bivariate AnalysisLecture 58 Correlation CoefficientLecture 59 Scatter Plots and HeatmapsLecture 60 Bivariate Analysis: Titanic DatasetLecture 61 Bivariate Analysis: Video Game SalesLecture 62 Introduction to Multivariate AnalysisLecture 63 Multivariate Analysis over Titanic DatasetLecture 64 Multivariate Analysis over Pokemon DatasetLecture 65 Simpson's ParadoxLecture 66 Correlation Is Not CausationLecture 67 Wine Data Analysis: Initial SetupLecture 68 Red Wine AnalysisLecture 69 White Wine AnalysisLecture 70 White Wine versus Red Wine: AnalysisSection 3: Data Visualization with PythonLecture 71 Course OverviewLecture 72 Installation and SetupLecture 73 IntroductionLecture 74 Overview of StatisticsLecture 75 NumPyLecture 76 pandasLecture 77 Lesson SummaryLecture 78 Lesson OverviewLecture 79 Comparison PlotsLecture 80 Relation PlotsLecture 81 Composition PlotsLecture 82 Distribution PlotsLecture 83 Geo PlotsLecture 84 What Makes a Good Visualization?Lecture 85 Lesson SummaryLecture 86 Lesson OverviewLecture 87 Overview of Plots in MatplotlibLecture 88 Basic Text and Legend FunctionsLecture 89 Basic PlotsLecture 90 LayoutsLecture 91 ImagesLecture 92 Writing Mathematical ExpressionsLecture 93 Lesson SummaryLecture 94 Lesson OverviewLecture 95 Controlling Figure AestheticsLecture 96 Color PalettesLecture 97 Interesting Plots in seabornLecture 98 Multi-plots in seabornLecture 99 Regression PlotsLecture 100 SquarifyLecture 101 Lesson SummaryLecture 102 Lesson OverviewLecture 103 Geoplotlib BasicsLecture 104 Tile ProvidersLecture 105 Custom LayersLecture 106 Lesson SummaryLecture 107 Lesson OverviewLecture 108 Bokeh BasicsLecture 109 Adding WidgetsLecture 110 Lesson SummarySection 4: Data Science Projects with PythonLecture 111 Course OverviewLecture 112 Installation and SetupLecture 113 Lesson OverviewLecture 114 Python and the Anaconda Package Management SystemLecture 115 Different Types of Data Science ProblemsLecture 116 Loading the Case Study Data with Jupyter and pandasLecture 117 Getting Familiar with Data and Performing Data CleaningLecture 118 Boolean MasksLecture 119 Data Quality Assurance and ExplorationLecture 120 Deep Dive: Categorical FeaturesLecture 121 Exploring the Financial History Features in the DatasetLecture 122 Lesson SummaryLecture 123 Lesson OverviewLecture 124 Exploring the Response Variable and Concluding the Initial ExplorationLecture 125 Introduction to Scikit-LearnLecture 126 Model Performance Metrics for Binary ClassificationLecture 127 True Positive Rate, False Positive Rate, and Confusion MatrixLecture 128 Obtaining Predicted Probabilities from a Trained Logistic Regression ModelLecture 129 Lesson SummaryLecture 130 Lesson OverviewLecture 131 Examining the Relationships between Features and the ResponseLecture 132 Finer Points of the F-test: Equivalence to t-test for Two Classes and CautionsLecture 133 Univariate Feature Selection: What It Does and Doesn't DoLecture 134 Generalized Linear Models (GLMs)Lecture 135 Lesson SummaryLecture 136 Lesson OverviewLecture 137 Estimating the Coefficients and Intercepts of Logistic RegressionLecture 138 Assumptions of Logistic RegressionLecture 139 How Many Features Should You Include?Lecture 140 Lasso (L1) and Ridge (L2) RegularizationLecture 141 Cross Validation: Choosing the Regularization Parameter and Other HyperparameterLecture 142 Reducing Overfitting on the Synthetic Data Classification ProblemLecture 143 Options for Logistic Regression in Scikit-LearnLecture 144 Lesson SummaryLecture 145 Lesson OverviewLecture 146 Decision TreesLecture 147 Training Decision Trees: Node ImpurityLecture 148 Using Decision Trees: Advantages and Predicted ProbabilitiesLecture 149 Random Forests: Ensembles of Decision TreesLecture 150 Fitting a Random ForestLecture 151 Lesson SummaryLecture 152 Lesson OverviewLecture 153 Review of Modeling ResultsLecture 154 Dealing with Missing dаta: Imputation StrategiesLecture 155 Cleaning the DatasetLecture 156 Mode and Random Imputation of PAY_1Lecture 157 A Predictive Model for PAY_1Lecture 158 Using the Imputation Model and Comparing it to Other MethodsLecture 159 Financial AnalysisLecture 160 Final Thoughts on Delivering the Predictive Model to the ClientLecture 161 Lesson SummaryThis course is for Python developers, data analysts, and IT professionals who want to progress in their careers as fully-fledged data scientists/analytics experts.,Also, anyone who wants to use data analytics/machine learning to enrich their current personal or professional projects will also benefit from the course.Homepagehttps://www.udemy.com/course/data-science-with-python-4-course-bundle/Download From Rapidgatorhttps://rapidgator.net/file/7f0793236518e6c11020798d6c566af0https://rapidgator.net/file/288d6c7dc9e42562609baf7624dde20ehttps://rapidgator.net/file/21e137cedb8239d58524f4f884490439https://rapidgator.net/file/8c7bb2dfc47a665a76c55f13614bb125https://rapidgator.net/file/e22a07d72b1057e1fbc2ef37de20a8c3https://rapidgator.net/file/ed3211413ed43d7a183bd0130b5703e8https://rapidgator.net/file/0f3aeabbb5ac81aac783978704a9ad74https://rapidgator.net/file/08a9af61e2e43876ccd56e947db9d1c9Download From 1DLhttps://1dl.net/wirjutgoocyohttps://1dl.net/jii6kpmg4sj4https://1dl.net/0jxq5smwiijshttps://1dl.net/lj7hetmc7gt7https://1dl.net/pp4prmrsqh6ahttps://1dl.net/qm3b393mp93hhttps://1dl.net/3p5w1afuov64https://1dl.net/8nibrwj7vohcDownload From Nitroflarehttps://nitroflare.com/view/0D7844CF338280Fhttps://nitroflare.com/view/78D3F3B6D79C086https://nitroflare.com/view/0D3133B76D04895https://nitroflare.com/view/FE23B39E4263BD7https://nitroflare.com/view/8387B0588A96C9Fhttps://nitroflare.com/view/C9F4FC43DD0AF1Fhttps://nitroflare.com/view/A4DC4C18100CDAFhttps://nitroflare.com/view/85DEC8CD36598BFTo Support My Work Buy Premium From My Links. Related News Complete Guide To Network Analysis With Wireshark 2.62023 CORE: Data Science and Machine LearningComplete Excel Course [Specially for Finance Professionals]Python and Machine Learning for Beginners: A complete easy guide to learning python and machine learning in one go!O&O DiskImage Professional / Server 18.2.201 Comments (0)Add comment Submit NEWEST RELEASES 19.05: Aiseesoft iPhone Unlocker 2.0.58 Multilingual Portable 19.05: Foxit PDF Editor Pro 13.1.1.22432 Multilingual Portable 19.05: 4K Stogram Pro 4.9.0 macOS 19.05: WinCatalog 2024.7.0.519 Multilingual Portable 18.05: MediaHuman YouTube To MP3 Converter 3.9.9.92 (0518) Multilingual (x64) Portable 18.05: MediaHuman YouTube Downloader 3.9.9.92 (0518) Multilingual (x64) Portable 18.05: Allavsoft Video Downloader Converter 3.27.0.8904 Multilingual Portable 18.05: FastStone Capture 10.5 Multilingual Portable 18.05: UniFab 2.0.2.1 (x64) Multilingual Portable 18.05: Windows 7 Ultimate SP1 Multilingual Preactivated May 2024 Recommended Filehosts Freinds Site