[Tutorialsplanet.NET] Udemy - Python + SQL + Tableau Integrating Python, SQL, and Tableau

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[Tutorialsplanet.NET] Udemy - Python + SQL + Tableau Integrating Python, SQL, and Tableau

磁力链接/BT种子简介

种子哈希：96149b89ae33831bbd12a1e7257942890722450c
文件大小： 2.77G
已经下载：818次
下载速度：极快
收录时间：2021-03-22
最近下载：2026-05-23
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文件列表

  • 2. What is software integration/5. Further Details on APIs.mp4 121.3 MB
  • 2. What is software integration/3. Properties and Definitions Data Connectivity, APIs, and Endpoints.mp4 109.3 MB
  • 5. Preprocessing/30. Further Analysis of the DataFrame Next 5 Columns.srt 88.3 MB
  • 5. Preprocessing/11. Splitting a Column into Multiple Dummies.mp4 85.0 MB
  • 7. Installing MySQL and Getting Acquainted with the Interface/1. Installing MySQL.mp4 84.9 MB
  • 8. Connecting Python and SQL/10. Transferring Data from Jupyter to Workbench - Part I.mp4 79.9 MB
  • 5. Preprocessing/16. Grouping - Transforming Dummy Variables into Categorical Variables.mp4 78.2 MB
  • 2. What is software integration/1. Properties and Definitions Data, Servers, Clients, Requests and Responses.mp4 72.5 MB
  • 2. What is software integration/9. Definitions and Applications.mp4 66.8 MB
  • 5. Preprocessing/3. Data at a Glance.mp4 64.8 MB
  • 5. Preprocessing/7. Removing Irrelevant Data.mp4 64.7 MB
  • 2. What is software integration/7. Text Files as Means of Communication.mp4 63.4 MB
  • 9. Analyzing the Obtained data in Tableau/4. Analysis in Tableau Reasons vs Probability.mp4 62.2 MB
  • 8. Connecting Python and SQL/4. Creating a Database in MySQL.mp4 61.8 MB
  • 8. Connecting Python and SQL/11. Transferring Data from Jupyter to Workbench - Part II.mp4 61.1 MB
  • 5. Preprocessing/26. Exploring the Initial Date Column.mp4 60.1 MB
  • 9. Analyzing the Obtained data in Tableau/2. Analysis in Tableau Age vs Probability.mp4 59.2 MB
  • 1. Introduction/1. What Does the Course Cover.mp4 58.9 MB
  • 8. Connecting Python and SQL/3. Implementing the 'absenteeism_module' - Part II.mp4 56.9 MB
  • 6. Machine Learning/5. Train-test Split of the Data.mp4 55.3 MB
  • 8. Connecting Python and SQL/8. Creating the 'predicted_outputs' table in MySQL.mp4 55.0 MB
  • 6. Machine Learning/8. Interpreting the Logistic Regression Coefficients.mp4 54.9 MB
  • 4. What's next in the course/1. Up Ahead.mp4 54.9 MB
  • 3. Setting up the working environment/4. Installing Anaconda.mp4 53.5 MB
  • 6. Machine Learning/12. Testing the Machine Learning Model.mp4 51.5 MB
  • 5. Preprocessing/27. Using the Date Column to Extract the Appropriate Month Value.mp4 50.1 MB
  • 6. Machine Learning/2. Creating the Targets for the Logistic Regression.mp4 48.1 MB
  • 6. Machine Learning/16. Creating a Module for Later Use of the Model.mp4 46.7 MB
  • 6. Machine Learning/6. Training the Model and Assessing its Accuracy.mp4 43.7 MB
  • 6. Machine Learning/9. Omitting the dummy variables from the Standardization.mp4 43.2 MB
  • 3. Setting up the working environment/2. Why Python and why Jupyter.mp4 43.1 MB
  • 4. What's next in the course/3. Real-Life Example The Dataset.mp4 42.9 MB
  • 9. Analyzing the Obtained data in Tableau/6. Analysis in Tableau Transportation Expense vs Probability.mp4 42.6 MB
  • 5. Preprocessing/10. Examining the Reasons for Absence.mp4 42.6 MB
  • 6. Machine Learning/10. Interpreting the Important Predictors.mp4 42.4 MB
  • 6. Machine Learning/11. Simplifying the Model (Backward Elimination).mp4 41.5 MB
  • 5. Preprocessing/31. Further Analysis of the DaraFrame Education, Children, Pets.mp4 41.5 MB
  • 4. What's next in the course/2. Real-Life Example Absenteeism at Work.mp4 41.1 MB
  • 6. Machine Learning/7. Extracting the Intercept and Coefficients from a Logistic Regression.mp4 40.7 MB
  • 5. Preprocessing/17. Concatenating Columns in Python.mp4 40.6 MB
  • 6. Machine Learning/13. How to Save the Machine Learning Model and Prepare it for Future Deployment.mp4 39.3 MB
  • 7. Installing MySQL and Getting Acquainted with the Interface/4. Introduction to the MySQL Interface.mp4 39.0 MB
  • 7. Installing MySQL and Getting Acquainted with the Interface/4. Introduction to the MySQL Interface.srt 36.6 MB
  • 8. Connecting Python and SQL/12. Transferring Data from Jupyter to Workbench - Part III.mp4 34.4 MB
  • 5. Preprocessing/30. Further Analysis of the DataFrame Next 5 Columns.mp4 31.0 MB
  • 3. Setting up the working environment/6. The Jupyter Dashboard - Part 2.mp4 30.0 MB
  • 5. Preprocessing/28. Introducing Day of the Week.mp4 29.4 MB
  • 5. Preprocessing/4. A Note on Our Usage of Terms with Multiple Meanings.mp4 29.3 MB
  • 6. Machine Learning/1. Exploring the Problem from a Machine Learning Point of View.mp4 28.8 MB
  • 5. Preprocessing/23. Implementing Checkpoints in Coding.mp4 27.0 MB
  • 8. Connecting Python and SQL/2. Implementing the 'absenteeism_module' - Part I.mp4 26.7 MB
  • 8. Connecting Python and SQL/9. Running an SQL SELECT Statement from Python.mp4 26.7 MB
  • 5. Preprocessing/2. Data Sets in Python.mp4 24.3 MB
  • 5. Preprocessing/32. A Final Note on Preprocessing.mp4 22.7 MB
  • 8. Connecting Python and SQL/6. Creating a Connection and Cursor.mp4 22.0 MB
  • 6. Machine Learning/4. A Bit of Statistical Preprocessing.mp4 21.6 MB
  • 5. Preprocessing/6. Picking the Appropriate Approach for the Task at Hand.mp4 21.2 MB
  • 8. Connecting Python and SQL/5. Importing and Installing 'pymysql'.mp4 20.0 MB
  • 7. Installing MySQL and Getting Acquainted with the Interface/3. Setting Up a Connection.mp4 18.4 MB
  • 6. Machine Learning/3. Selecting the Inputs.mp4 17.6 MB
  • 5. Preprocessing/20. Changing Column Order in Pandas DataFrame.mp4 14.7 MB
  • 5. Preprocessing/15. Dummy Variables and Their Statistical Importance.mp4 14.5 MB
  • 3. Setting up the working environment/5. The Jupyter Dashboard - Part 1.mp4 14.1 MB
  • 3. Setting up the working environment/9. Installing sklearn.mp4 8.1 MB
  • 3. Setting up the working environment/1. Setting Up the Environment - An Introduction (Do Not Skip, Please)!.mp4 5.6 MB
  • 3. Setting up the working environment/7.1 Shortcuts-for-Jupyter.pdf 634.0 kB
  • 5. Preprocessing/1.2 data_preprocessing_homework.pptx 310.9 kB
  • 5. Preprocessing/1.3 Absenteeism_data.csv 32.8 kB
  • 6. Machine Learning/1.1 Absenteeism_preprocessed.csv 29.8 kB
  • 5. Preprocessing/1.1 df_preprocessed.csv 29.8 kB
  • 7. Installing MySQL and Getting Acquainted with the Interface/1. Installing MySQL.srt 11.7 kB
  • 2. What is software integration/5. Further Details on APIs.srt 10.6 kB
  • 9. Analyzing the Obtained data in Tableau/2. Analysis in Tableau Age vs Probability.srt 10.3 kB
  • 5. Preprocessing/11. Splitting a Column into Multiple Dummies.srt 10.2 kB
  • 5. Preprocessing/16. Grouping - Transforming Dummy Variables into Categorical Variables.srt 10.2 kB
  • 9. Analyzing the Obtained data in Tableau/4. Analysis in Tableau Reasons vs Probability.srt 9.7 kB
  • 3. Setting up the working environment/4. Installing Anaconda.srt 9.1 kB
  • 2. What is software integration/3. Properties and Definitions Data Connectivity, APIs, and Endpoints.srt 8.8 kB
  • 5. Preprocessing/26. Exploring the Initial Date Column.srt 8.7 kB
  • 6. Machine Learning/2. Creating the Targets for the Logistic Regression.srt 8.6 kB
  • 6. Machine Learning/5. Train-test Split of the Data.srt 8.5 kB
  • 8. Connecting Python and SQL/4. Creating a Database in MySQL.srt 8.2 kB
  • 5. Preprocessing/27. Using the Date Column to Extract the Appropriate Month Value.srt 8.0 kB
  • 6. Machine Learning/8. Interpreting the Logistic Regression Coefficients.srt 8.0 kB
  • 5. Preprocessing/7. Removing Irrelevant Data.srt 8.0 kB
  • 8. Connecting Python and SQL/10. Transferring Data from Jupyter to Workbench - Part I.srt 8.0 kB
  • 3. Setting up the working environment/6. The Jupyter Dashboard - Part 2.srt 7.8 kB
  • 8. Connecting Python and SQL/3. Implementing the 'absenteeism_module' - Part II.srt 7.6 kB
  • 8. Connecting Python and SQL/11. Transferring Data from Jupyter to Workbench - Part II.srt 7.5 kB
  • 6. Machine Learning/10. Interpreting the Important Predictors.srt 7.4 kB
  • 6. Machine Learning/6. Training the Model and Assessing its Accuracy.srt 7.3 kB
  • 9. Analyzing the Obtained data in Tableau/6. Analysis in Tableau Transportation Expense vs Probability.srt 7.2 kB
  • 5. Preprocessing/3. Data at a Glance.srt 7.2 kB
  • 2. What is software integration/9. Definitions and Applications.srt 6.9 kB
  • 6. Machine Learning/12. Testing the Machine Learning Model.srt 6.7 kB
  • 3. Setting up the working environment/2. Why Python and why Jupyter.srt 6.6 kB
  • 6. Machine Learning/7. Extracting the Intercept and Coefficients from a Logistic Regression.srt 6.5 kB
  • 8. Connecting Python and SQL/8. Creating the 'predicted_outputs' table in MySQL.srt 6.0 kB
  • 2. What is software integration/1. Properties and Definitions Data, Servers, Clients, Requests and Responses.srt 6.0 kB
  • 5. Preprocessing/10. Examining the Reasons for Absence.srt 6.0 kB
  • 5. Preprocessing/31. Further Analysis of the DaraFrame Education, Children, Pets.srt 5.8 kB
  • 6. Machine Learning/16. Creating a Module for Later Use of the Model.srt 5.8 kB
  • 6. Machine Learning/13. How to Save the Machine Learning Model and Prepare it for Future Deployment.srt 5.6 kB
  • 2. What is software integration/7. Text Files as Means of Communication.srt 5.6 kB
  • 1. Introduction/1. What Does the Course Cover.srt 5.6 kB
  • 4. What's next in the course/1. Up Ahead.srt 5.5 kB
  • 6. Machine Learning/11. Simplifying the Model (Backward Elimination).srt 5.3 kB
  • 6. Machine Learning/9. Omitting the dummy variables from the Standardization.srt 5.1 kB
  • 5. Preprocessing/17. Concatenating Columns in Python.srt 5.1 kB
  • 8. Connecting Python and SQL/2. Implementing the 'absenteeism_module' - Part I.srt 4.8 kB
  • 6. Machine Learning/1. Exploring the Problem from a Machine Learning Point of View.srt 4.7 kB
  • 5. Preprocessing/28. Introducing Day of the Week.srt 4.4 kB
  • 6. Machine Learning/4. A Bit of Statistical Preprocessing.srt 4.2 kB
  • 5. Preprocessing/4. A Note on Our Usage of Terms with Multiple Meanings.srt 4.2 kB
  • 4. What's next in the course/3. Real-Life Example The Dataset.srt 4.2 kB
  • 5. Preprocessing/2. Data Sets in Python.srt 4.0 kB
  • 4. What's next in the course/2. Real-Life Example Absenteeism at Work.srt 3.9 kB
  • 3. Setting up the working environment/5. The Jupyter Dashboard - Part 1.srt 3.8 kB
  • 5. Preprocessing/23. Implementing Checkpoints in Coding.srt 3.8 kB
  • 8. Connecting Python and SQL/9. Running an SQL SELECT Statement from Python.srt 3.7 kB
  • 6. Machine Learning/3. Selecting the Inputs.srt 3.6 kB
  • 8. Connecting Python and SQL/6. Creating a Connection and Cursor.srt 3.6 kB
  • 8. Connecting Python and SQL/5. Importing and Installing 'pymysql'.srt 3.4 kB
  • 8. Connecting Python and SQL/12. Transferring Data from Jupyter to Workbench - Part III.srt 3.4 kB
  • 7. Installing MySQL and Getting Acquainted with the Interface/3. Setting Up a Connection.srt 3.3 kB
  • 5. Preprocessing/5. ARTICLE - A Brief Overview of Regression Analysis.html 2.9 kB
  • 5. Preprocessing/6. Picking the Appropriate Approach for the Task at Hand.srt 2.9 kB
  • 5. Preprocessing/1. What to Expect from the Next Couple of Sections.html 2.9 kB
  • 7. Installing MySQL and Getting Acquainted with the Interface/2. Installing MySQL on macOS and Unix systems.html 2.7 kB
  • 5. Preprocessing/32. A Final Note on Preprocessing.srt 2.6 kB
  • 5. Preprocessing/14. ARTICLE - Dummy Variables Reasoning.html 2.4 kB
  • 6. Machine Learning/14. ARTICLE - More about 'pickling'.html 2.2 kB
  • 5. Preprocessing/20. Changing Column Order in Pandas DataFrame.srt 1.9 kB
  • 10. Bonus lecture/1. Bonus Lecture Next Steps.html 1.9 kB
  • 3. Setting up the working environment/9. Installing sklearn.srt 1.8 kB
  • 5. Preprocessing/15. Dummy Variables and Their Statistical Importance.srt 1.7 kB
  • 3. Setting up the working environment/1. Setting Up the Environment - An Introduction (Do Not Skip, Please)!.srt 1.3 kB
  • 4. What's next in the course/5. Important Notice Regarding Datasets.html 1.3 kB
  • 5. Preprocessing/29. EXERCISE - Removing Columns.html 1.2 kB
  • 5. Preprocessing/33. A Note on Exporting Your Data as a .csv File.html 883 Bytes
  • 5. Preprocessing/8. EXERCISE - Removing Irrelevant Data.html 873 Bytes
  • 9. Analyzing the Obtained data in Tableau/5. EXERCISE - Transportation Expense vs Probability.html 553 Bytes
  • 3. Setting up the working environment/11. Installing Packages - Solution.html 546 Bytes
  • 5. Preprocessing/22. SOLUTION - Changing Column Order in Pandas DataFrame.html 471 Bytes
  • 9. Analyzing the Obtained data in Tableau/3. EXERCISE - Reasons vs Probability.html 390 Bytes
  • 9. Analyzing the Obtained data in Tableau/1. EXERCISE - Age vs Probability.html 385 Bytes
  • 8. Connecting Python and SQL/1. Are you sure you're all set.html 336 Bytes
  • 8. Connecting Python and SQL/7. EXERCISE - Create 'df_new_obs'.html 322 Bytes
  • 3. Setting up the working environment/7. Jupyter Shortcuts.html 316 Bytes
  • 3. Setting up the working environment/10. Installing Packages - Exercise.html 291 Bytes
  • 6. Machine Learning/15. EXERCISE - Saving the Model (and Scaler).html 284 Bytes
  • 6. Machine Learning/11.1 Logistic Regression prior to Backward Elimination.html 226 Bytes
  • 6. Machine Learning/9.1 Logistic Regression prior to Custom Scaler.html 219 Bytes
  • 6. Machine Learning/15.1 Logistic Regression with Comments.html 210 Bytes
  • 6. Machine Learning/15.2 Logistic Regression.html 196 Bytes
  • 5. Preprocessing/29.2 Preprocessing - df_reason_date_mod.html 191 Bytes
  • 5. Preprocessing/18. EXERCISE - Concatenating Columns in Python.html 189 Bytes
  • 5. Preprocessing/29.1 Removing Columns.html 188 Bytes
  • 5. Preprocessing/23.1 Implementing Checkpoints in Coding.html 176 Bytes
  • 5. Preprocessing/32.1 Exercises and Solutions.html 170 Bytes
  • 5. Preprocessing/21. EXERCISE - Changing Column Order in Pandas DataFrame.html 167 Bytes
  • 5. Preprocessing/32.2 Preprocessing - Lectures.html 167 Bytes
  • 2. What is software integration/10. Definitions and Applications.html 164 Bytes
  • 2. What is software integration/2. Properties and Definitions Data, Servers, Clients, Requests and Responses.html 164 Bytes
  • 2. What is software integration/4. Properties and Definitions Data Connectivity, APIs, and Endpoints.html 164 Bytes
  • 2. What is software integration/6. Further Details on APIs.html 164 Bytes
  • 2. What is software integration/8. Text Files as Means of Communication.html 164 Bytes
  • 3. Setting up the working environment/3. Why Python and why Jupyter.html 164 Bytes
  • 3. Setting up the working environment/8. The Jupyter Dashboard.html 164 Bytes
  • 4. What's next in the course/4. Real-Life Example The Dataset.html 164 Bytes
  • 5. Preprocessing/32.3 Preprocessing - df_preprocessed.html 156 Bytes
  • 8. Connecting Python and SQL/12.1 Integration.html 154 Bytes
  • 5. Preprocessing/19. SOLUTION - Concatenating Columns in Python.html 142 Bytes
  • 5. Preprocessing/24. EXERCISE - Implementing Checkpoints in Coding.html 137 Bytes
  • 1. Introduction/1.1 Course Resources - Complete Package.html 134 Bytes
  • 8. Connecting Python and SQL/1.1 5 Files Needed to Deploy the Model.html 134 Bytes
  • 5. Preprocessing/12. EXERCISE - Splitting a Column into Multiple Dummies.html 130 Bytes
  • [Tutorialsplanet.NET].url 128 Bytes
  • 5. Preprocessing/25. SOLUTION - Implementing Checkpoint in Coding.html 117 Bytes
  • 5. Preprocessing/13. SOLUTION - Splitting a Column into Multiple Dummies.html 116 Bytes
  • 5. Preprocessing/9. SOLUTION - Removing Irrelevant Data.html 113 Bytes

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