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[] Udemy - Python + SQL + Tableau Integrating Python, SQL, and Tableau

  • 收录时间:2020-12-22 11:42:59
  • 文件大小:3GB
  • 下载次数:4
  • 最近下载:2021-01-01 20:45:59
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文件列表

  1. 2. What is software integration/5. Further Details on APIs.mp4 116MB
  2. 2. What is software integration/3. Properties and Definitions Data Connectivity, APIs, and Endpoints.mp4 104MB
  3. 5. Preprocessing/30. Further Analysis of the DataFrame Next 5 Columns.srt 84MB
  4. 5. Preprocessing/11. Splitting a Column into Multiple Dummies.mp4 81MB
  5. 7. Installing MySQL and Getting Acquainted with the Interface/1. Installing MySQL.mp4 81MB
  6. 8. Connecting Python and SQL/10. Transferring Data from Jupyter to Workbench - Part I.mp4 76MB
  7. 5. Preprocessing/16. Grouping - Transforming Dummy Variables into Categorical Variables.mp4 75MB
  8. 2. What is software integration/1. Properties and Definitions Data, Servers, Clients, Requests and Responses.mp4 69MB
  9. 2. What is software integration/9. Definitions and Applications.mp4 64MB
  10. 5. Preprocessing/3. Data at a Glance.mp4 62MB
  11. 5. Preprocessing/7. Removing Irrelevant Data.mp4 62MB
  12. 2. What is software integration/7. Text Files as Means of Communication.mp4 60MB
  13. 9. Analyzing the Obtained data in Tableau/4. Analysis in Tableau Reasons vs Probability.mp4 59MB
  14. 8. Connecting Python and SQL/4. Creating a Database in MySQL.mp4 59MB
  15. 8. Connecting Python and SQL/11. Transferring Data from Jupyter to Workbench - Part II.mp4 58MB
  16. 5. Preprocessing/26. Exploring the Initial Date Column.mp4 57MB
  17. 9. Analyzing the Obtained data in Tableau/2. Analysis in Tableau Age vs Probability.mp4 56MB
  18. 1. Introduction/1. What Does the Course Cover.mp4 56MB
  19. 8. Connecting Python and SQL/3. Implementing the 'absenteeism_module' - Part II.mp4 54MB
  20. 6. Machine Learning/5. Train-test Split of the Data.mp4 53MB
  21. 8. Connecting Python and SQL/8. Creating the 'predicted_outputs' table in MySQL.mp4 52MB
  22. 6. Machine Learning/8. Interpreting the Logistic Regression Coefficients.mp4 52MB
  23. 4. What's next in the course/1. Up Ahead.mp4 52MB
  24. 3. Setting up the working environment/4. Installing Anaconda.mp4 51MB
  25. 6. Machine Learning/12. Testing the Machine Learning Model.mp4 49MB
  26. 5. Preprocessing/27. Using the Date Column to Extract the Appropriate Month Value.mp4 48MB
  27. 6. Machine Learning/2. Creating the Targets for the Logistic Regression.mp4 46MB
  28. 6. Machine Learning/16. Creating a Module for Later Use of the Model.mp4 45MB
  29. 6. Machine Learning/6. Training the Model and Assessing its Accuracy.mp4 42MB
  30. 6. Machine Learning/9. Omitting the dummy variables from the Standardization.mp4 41MB
  31. 3. Setting up the working environment/2. Why Python and why Jupyter.mp4 41MB
  32. 4. What's next in the course/3. Real-Life Example The Dataset.mp4 41MB
  33. 9. Analyzing the Obtained data in Tableau/6. Analysis in Tableau Transportation Expense vs Probability.mp4 41MB
  34. 5. Preprocessing/10. Examining the Reasons for Absence.mp4 41MB
  35. 6. Machine Learning/10. Interpreting the Important Predictors.mp4 40MB
  36. 6. Machine Learning/11. Simplifying the Model (Backward Elimination).mp4 40MB
  37. 5. Preprocessing/31. Further Analysis of the DaraFrame Education, Children, Pets.mp4 40MB
  38. 4. What's next in the course/2. Real-Life Example Absenteeism at Work.mp4 39MB
  39. 6. Machine Learning/7. Extracting the Intercept and Coefficients from a Logistic Regression.mp4 39MB
  40. 5. Preprocessing/17. Concatenating Columns in Python.mp4 39MB
  41. 6. Machine Learning/13. How to Save the Machine Learning Model and Prepare it for Future Deployment.mp4 37MB
  42. 7. Installing MySQL and Getting Acquainted with the Interface/4. Introduction to the MySQL Interface.mp4 37MB
  43. 7. Installing MySQL and Getting Acquainted with the Interface/4. Introduction to the MySQL Interface.srt 35MB
  44. 8. Connecting Python and SQL/12. Transferring Data from Jupyter to Workbench - Part III.mp4 33MB
  45. 5. Preprocessing/30. Further Analysis of the DataFrame Next 5 Columns.mp4 30MB
  46. 3. Setting up the working environment/6. The Jupyter Dashboard - Part 2.mp4 29MB
  47. 5. Preprocessing/28. Introducing Day of the Week.mp4 28MB
  48. 5. Preprocessing/4. A Note on Our Usage of Terms with Multiple Meanings.mp4 28MB
  49. 6. Machine Learning/1. Exploring the Problem from a Machine Learning Point of View.mp4 28MB
  50. 5. Preprocessing/23. Implementing Checkpoints in Coding.mp4 26MB
  51. 8. Connecting Python and SQL/2. Implementing the 'absenteeism_module' - Part I.mp4 25MB
  52. 8. Connecting Python and SQL/9. Running an SQL SELECT Statement from Python.mp4 25MB
  53. 5. Preprocessing/2. Data Sets in Python.mp4 23MB
  54. 5. Preprocessing/32. A Final Note on Preprocessing.mp4 22MB
  55. 8. Connecting Python and SQL/6. Creating a Connection and Cursor.mp4 21MB
  56. 6. Machine Learning/4. A Bit of Statistical Preprocessing.mp4 21MB
  57. 5. Preprocessing/6. Picking the Appropriate Approach for the Task at Hand.mp4 20MB
  58. 8. Connecting Python and SQL/5. Importing and Installing 'pymysql'.mp4 19MB
  59. 7. Installing MySQL and Getting Acquainted with the Interface/3. Setting Up a Connection.mp4 18MB
  60. 6. Machine Learning/3. Selecting the Inputs.mp4 17MB
  61. 5. Preprocessing/20. Changing Column Order in Pandas DataFrame.mp4 14MB
  62. 5. Preprocessing/15. Dummy Variables and Their Statistical Importance.mp4 14MB
  63. 3. Setting up the working environment/5. The Jupyter Dashboard - Part 1.mp4 13MB
  64. 3. Setting up the working environment/9. Installing sklearn.mp4 8MB
  65. 3. Setting up the working environment/1. Setting Up the Environment - An Introduction (Do Not Skip, Please)!.mp4 5MB
  66. 3. Setting up the working environment/7.1 Shortcuts-for-Jupyter.pdf 619KB
  67. 5. Preprocessing/1.2 data_preprocessing_homework.pptx 304KB
  68. 5. Preprocessing/1.3 Absenteeism_data.csv 32KB
  69. 6. Machine Learning/1.1 Absenteeism_preprocessed.csv 29KB
  70. 5. Preprocessing/1.1 df_preprocessed.csv 29KB
  71. 7. Installing MySQL and Getting Acquainted with the Interface/1. Installing MySQL.srt 11KB
  72. 2. What is software integration/5. Further Details on APIs.srt 10KB
  73. 9. Analyzing the Obtained data in Tableau/2. Analysis in Tableau Age vs Probability.srt 10KB
  74. 5. Preprocessing/11. Splitting a Column into Multiple Dummies.srt 10KB
  75. 5. Preprocessing/16. Grouping - Transforming Dummy Variables into Categorical Variables.srt 10KB
  76. 9. Analyzing the Obtained data in Tableau/4. Analysis in Tableau Reasons vs Probability.srt 9KB
  77. 3. Setting up the working environment/4. Installing Anaconda.srt 9KB
  78. 2. What is software integration/3. Properties and Definitions Data Connectivity, APIs, and Endpoints.srt 9KB
  79. 5. Preprocessing/26. Exploring the Initial Date Column.srt 8KB
  80. 6. Machine Learning/2. Creating the Targets for the Logistic Regression.srt 8KB
  81. 6. Machine Learning/5. Train-test Split of the Data.srt 8KB
  82. 8. Connecting Python and SQL/4. Creating a Database in MySQL.srt 8KB
  83. 5. Preprocessing/27. Using the Date Column to Extract the Appropriate Month Value.srt 8KB
  84. 6. Machine Learning/8. Interpreting the Logistic Regression Coefficients.srt 8KB
  85. 5. Preprocessing/7. Removing Irrelevant Data.srt 8KB
  86. 8. Connecting Python and SQL/10. Transferring Data from Jupyter to Workbench - Part I.srt 8KB
  87. 3. Setting up the working environment/6. The Jupyter Dashboard - Part 2.srt 8KB
  88. 8. Connecting Python and SQL/3. Implementing the 'absenteeism_module' - Part II.srt 7KB
  89. 8. Connecting Python and SQL/11. Transferring Data from Jupyter to Workbench - Part II.srt 7KB
  90. 6. Machine Learning/10. Interpreting the Important Predictors.srt 7KB
  91. 6. Machine Learning/6. Training the Model and Assessing its Accuracy.srt 7KB
  92. 9. Analyzing the Obtained data in Tableau/6. Analysis in Tableau Transportation Expense vs Probability.srt 7KB
  93. 5. Preprocessing/3. Data at a Glance.srt 7KB
  94. 2. What is software integration/9. Definitions and Applications.srt 7KB
  95. 6. Machine Learning/12. Testing the Machine Learning Model.srt 7KB
  96. 3. Setting up the working environment/2. Why Python and why Jupyter.srt 6KB
  97. 6. Machine Learning/7. Extracting the Intercept and Coefficients from a Logistic Regression.srt 6KB
  98. 8. Connecting Python and SQL/8. Creating the 'predicted_outputs' table in MySQL.srt 6KB
  99. 2. What is software integration/1. Properties and Definitions Data, Servers, Clients, Requests and Responses.srt 6KB
  100. 5. Preprocessing/10. Examining the Reasons for Absence.srt 6KB
  101. 5. Preprocessing/31. Further Analysis of the DaraFrame Education, Children, Pets.srt 6KB
  102. 6. Machine Learning/16. Creating a Module for Later Use of the Model.srt 6KB
  103. 6. Machine Learning/13. How to Save the Machine Learning Model and Prepare it for Future Deployment.srt 5KB
  104. 2. What is software integration/7. Text Files as Means of Communication.srt 5KB
  105. 1. Introduction/1. What Does the Course Cover.srt 5KB
  106. 4. What's next in the course/1. Up Ahead.srt 5KB
  107. 6. Machine Learning/11. Simplifying the Model (Backward Elimination).srt 5KB
  108. 6. Machine Learning/9. Omitting the dummy variables from the Standardization.srt 5KB
  109. 5. Preprocessing/17. Concatenating Columns in Python.srt 5KB
  110. 8. Connecting Python and SQL/2. Implementing the 'absenteeism_module' - Part I.srt 5KB
  111. 6. Machine Learning/1. Exploring the Problem from a Machine Learning Point of View.srt 5KB
  112. 5. Preprocessing/28. Introducing Day of the Week.srt 4KB
  113. 6. Machine Learning/4. A Bit of Statistical Preprocessing.srt 4KB
  114. 5. Preprocessing/4. A Note on Our Usage of Terms with Multiple Meanings.srt 4KB
  115. 4. What's next in the course/3. Real-Life Example The Dataset.srt 4KB
  116. 5. Preprocessing/2. Data Sets in Python.srt 4KB
  117. 4. What's next in the course/2. Real-Life Example Absenteeism at Work.srt 4KB
  118. 3. Setting up the working environment/5. The Jupyter Dashboard - Part 1.srt 4KB
  119. 5. Preprocessing/23. Implementing Checkpoints in Coding.srt 4KB
  120. 8. Connecting Python and SQL/9. Running an SQL SELECT Statement from Python.srt 4KB
  121. 6. Machine Learning/3. Selecting the Inputs.srt 4KB
  122. 8. Connecting Python and SQL/6. Creating a Connection and Cursor.srt 3KB
  123. 8. Connecting Python and SQL/5. Importing and Installing 'pymysql'.srt 3KB
  124. 8. Connecting Python and SQL/12. Transferring Data from Jupyter to Workbench - Part III.srt 3KB
  125. 7. Installing MySQL and Getting Acquainted with the Interface/3. Setting Up a Connection.srt 3KB
  126. 5. Preprocessing/5. ARTICLE - A Brief Overview of Regression Analysis.html 3KB
  127. 5. Preprocessing/6. Picking the Appropriate Approach for the Task at Hand.srt 3KB
  128. 5. Preprocessing/1. What to Expect from the Next Couple of Sections.html 3KB
  129. 7. Installing MySQL and Getting Acquainted with the Interface/2. Installing MySQL on macOS and Unix systems.html 3KB
  130. 5. Preprocessing/32. A Final Note on Preprocessing.srt 3KB
  131. 5. Preprocessing/14. ARTICLE - Dummy Variables Reasoning.html 2KB
  132. 6. Machine Learning/14. ARTICLE - More about 'pickling'.html 2KB
  133. 5. Preprocessing/20. Changing Column Order in Pandas DataFrame.srt 2KB
  134. 10. Bonus lecture/1. Bonus Lecture Next Steps.html 2KB
  135. 3. Setting up the working environment/9. Installing sklearn.srt 2KB
  136. 5. Preprocessing/15. Dummy Variables and Their Statistical Importance.srt 2KB
  137. 3. Setting up the working environment/1. Setting Up the Environment - An Introduction (Do Not Skip, Please)!.srt 1KB
  138. 4. What's next in the course/5. Important Notice Regarding Datasets.html 1KB
  139. 5. Preprocessing/29. EXERCISE - Removing Columns.html 1KB
  140. 5. Preprocessing/33. A Note on Exporting Your Data as a .csv File.html 883B
  141. 5. Preprocessing/8. EXERCISE - Removing Irrelevant Data.html 873B
  142. 9. Analyzing the Obtained data in Tableau/5. EXERCISE - Transportation Expense vs Probability.html 553B
  143. 3. Setting up the working environment/11. Installing Packages - Solution.html 546B
  144. 5. Preprocessing/22. SOLUTION - Changing Column Order in Pandas DataFrame.html 471B
  145. 9. Analyzing the Obtained data in Tableau/3. EXERCISE - Reasons vs Probability.html 390B
  146. 9. Analyzing the Obtained data in Tableau/1. EXERCISE - Age vs Probability.html 385B
  147. 8. Connecting Python and SQL/1. Are you sure you're all set.html 336B
  148. 8. Connecting Python and SQL/7. EXERCISE - Create 'df_new_obs'.html 322B
  149. 3. Setting up the working environment/7. Jupyter Shortcuts.html 316B
  150. 3. Setting up the working environment/10. Installing Packages - Exercise.html 291B
  151. 6. Machine Learning/15. EXERCISE - Saving the Model (and Scaler).html 284B
  152. 6. Machine Learning/11.1 Logistic Regression prior to Backward Elimination.html 226B
  153. 6. Machine Learning/9.1 Logistic Regression prior to Custom Scaler.html 219B
  154. 6. Machine Learning/15.1 Logistic Regression with Comments.html 210B
  155. 6. Machine Learning/15.2 Logistic Regression.html 196B
  156. 5. Preprocessing/29.2 Preprocessing - df_reason_date_mod.html 191B
  157. 5. Preprocessing/18. EXERCISE - Concatenating Columns in Python.html 189B
  158. 5. Preprocessing/29.1 Removing Columns.html 188B
  159. 5. Preprocessing/23.1 Implementing Checkpoints in Coding.html 176B
  160. 5. Preprocessing/32.1 Exercises and Solutions.html 170B
  161. 5. Preprocessing/21. EXERCISE - Changing Column Order in Pandas DataFrame.html 167B
  162. 5. Preprocessing/32.2 Preprocessing - Lectures.html 167B
  163. 2. What is software integration/10. Definitions and Applications.html 164B
  164. 2. What is software integration/2. Properties and Definitions Data, Servers, Clients, Requests and Responses.html 164B
  165. 2. What is software integration/4. Properties and Definitions Data Connectivity, APIs, and Endpoints.html 164B
  166. 2. What is software integration/6. Further Details on APIs.html 164B
  167. 2. What is software integration/8. Text Files as Means of Communication.html 164B
  168. 3. Setting up the working environment/3. Why Python and why Jupyter.html 164B
  169. 3. Setting up the working environment/8. The Jupyter Dashboard.html 164B
  170. 4. What's next in the course/4. Real-Life Example The Dataset.html 164B
  171. 5. Preprocessing/32.3 Preprocessing - df_preprocessed.html 156B
  172. 8. Connecting Python and SQL/12.1 Integration.html 154B
  173. 5. Preprocessing/19. SOLUTION - Concatenating Columns in Python.html 142B
  174. 5. Preprocessing/24. EXERCISE - Implementing Checkpoints in Coding.html 137B
  175. 1. Introduction/1.1 Course Resources - Complete Package.html 134B
  176. 8. Connecting Python and SQL/1.1 5 Files Needed to Deploy the Model.html 134B
  177. 5. Preprocessing/12. EXERCISE - Splitting a Column into Multiple Dummies.html 130B
  178. [Tutorialsplanet.NET].url 128B
  179. 5. Preprocessing/25. SOLUTION - Implementing Checkpoint in Coding.html 117B
  180. 5. Preprocessing/13. SOLUTION - Splitting a Column into Multiple Dummies.html 116B
  181. 5. Preprocessing/9. SOLUTION - Removing Irrelevant Data.html 113B