种子简介

种子名称： [Tutorialsplanet.NET] Udemy - Python + SQL + Tableau Integrating Python, SQL, and Tableau
文件类型： 视频
文件数目： 63个文件
文件大小： 2.66 GB
收录时间： 2020-11-14 16:42
已经下载： 3次
资源热度： 204
最近下载： 2024-12-15 01:29

下载BT种子文件

下载Torrent文件(.torrent) 立即下载

磁力链接下载

magnet:?xt=urn:btih:96149b89ae33831bbd12a1e7257942890722450c&dn=[Tutorialsplanet.NET] Udemy - Python + SQL + Tableau Integrating Python, SQL, and Tableau 复制链接到迅雷、QQ旋风进行下载，或者使用百度云离线下载。

喜欢这个种子的人也喜欢

种子包含的文件

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

1. Introduction/1. What Does the Course Cover.mp456.19MB

2. What is software integration/1. Properties and Definitions Data, Servers, Clients, Requests and Responses.mp469.16MB

2. What is software integration/3. Properties and Definitions Data Connectivity, APIs, and Endpoints.mp4104.22MB

2. What is software integration/5. Further Details on APIs.mp4115.66MB

2. What is software integration/7. Text Files as Means of Communication.mp460.42MB

2. What is software integration/9. Definitions and Applications.mp463.7MB

3. Setting up the working environment/1. Setting Up the Environment - An Introduction (Do Not Skip, Please)!.mp45.34MB

3. Setting up the working environment/2. Why Python and why Jupyter.mp441.07MB

3. Setting up the working environment/4. Installing Anaconda.mp451.01MB

3. Setting up the working environment/5. The Jupyter Dashboard - Part 1.mp413.47MB

3. Setting up the working environment/6. The Jupyter Dashboard - Part 2.mp428.65MB

3. Setting up the working environment/9. Installing sklearn.mp47.77MB

4. What's next in the course/1. Up Ahead.mp452.32MB

4. What's next in the course/2. Real-Life Example Absenteeism at Work.mp439.17MB

4. What's next in the course/3. Real-Life Example The Dataset.mp440.91MB

5. Preprocessing/10. Examining the Reasons for Absence.mp440.6MB

5. Preprocessing/11. Splitting a Column into Multiple Dummies.mp481.03MB

5. Preprocessing/15. Dummy Variables and Their Statistical Importance.mp413.8MB

5. Preprocessing/16. Grouping - Transforming Dummy Variables into Categorical Variables.mp474.6MB

5. Preprocessing/17. Concatenating Columns in Python.mp438.76MB

5. Preprocessing/2. Data Sets in Python.mp423.15MB

5. Preprocessing/20. Changing Column Order in Pandas DataFrame.mp414.05MB

5. Preprocessing/23. Implementing Checkpoints in Coding.mp425.7MB

5. Preprocessing/26. Exploring the Initial Date Column.mp457.3MB

5. Preprocessing/27. Using the Date Column to Extract the Appropriate Month Value.mp447.76MB

5. Preprocessing/28. Introducing Day of the Week.mp428MB

5. Preprocessing/3. Data at a Glance.mp461.83MB

5. Preprocessing/30. Further Analysis of the DataFrame Next 5 Columns.mp429.53MB

5. Preprocessing/31. Further Analysis of the DaraFrame Education, Children, Pets.mp439.54MB

5. Preprocessing/32. A Final Note on Preprocessing.mp421.62MB

5. Preprocessing/4. A Note on Our Usage of Terms with Multiple Meanings.mp427.9MB

5. Preprocessing/6. Picking the Appropriate Approach for the Task at Hand.mp420.22MB

5. Preprocessing/7. Removing Irrelevant Data.mp461.73MB

6. Machine Learning/1. Exploring the Problem from a Machine Learning Point of View.mp427.51MB

6. Machine Learning/10. Interpreting the Important Predictors.mp440.45MB

6. Machine Learning/11. Simplifying the Model (Backward Elimination).mp439.59MB

6. Machine Learning/12. Testing the Machine Learning Model.mp449.13MB

6. Machine Learning/13. How to Save the Machine Learning Model and Prepare it for Future Deployment.mp437.45MB

6. Machine Learning/16. Creating a Module for Later Use of the Model.mp444.58MB

6. Machine Learning/2. Creating the Targets for the Logistic Regression.mp445.83MB

6. Machine Learning/3. Selecting the Inputs.mp416.76MB

6. Machine Learning/4. A Bit of Statistical Preprocessing.mp420.6MB

6. Machine Learning/5. Train-test Split of the Data.mp452.7MB

6. Machine Learning/6. Training the Model and Assessing its Accuracy.mp441.64MB

6. Machine Learning/7. Extracting the Intercept and Coefficients from a Logistic Regression.mp438.82MB

6. Machine Learning/8. Interpreting the Logistic Regression Coefficients.mp452.36MB

6. Machine Learning/9. Omitting the dummy variables from the Standardization.mp441.19MB

7. Installing MySQL and Getting Acquainted with the Interface/1. Installing MySQL.mp480.95MB

7. Installing MySQL and Getting Acquainted with the Interface/3. Setting Up a Connection.mp417.57MB

7. Installing MySQL and Getting Acquainted with the Interface/4. Introduction to the MySQL Interface.mp437.22MB

8. Connecting Python and SQL/10. Transferring Data from Jupyter to Workbench - Part I.mp476.24MB

8. Connecting Python and SQL/11. Transferring Data from Jupyter to Workbench - Part II.mp458.22MB

8. Connecting Python and SQL/12. Transferring Data from Jupyter to Workbench - Part III.mp432.8MB

8. Connecting Python and SQL/2. Implementing the 'absenteeism_module' - Part I.mp425.45MB

8. Connecting Python and SQL/3. Implementing the 'absenteeism_module' - Part II.mp454.29MB

8. Connecting Python and SQL/4. Creating a Database in MySQL.mp458.95MB

8. Connecting Python and SQL/5. Importing and Installing 'pymysql'.mp419.03MB

8. Connecting Python and SQL/6. Creating a Connection and Cursor.mp420.97MB

8. Connecting Python and SQL/8. Creating the 'predicted_outputs' table in MySQL.mp452.45MB

8. Connecting Python and SQL/9. Running an SQL SELECT Statement from Python.mp425.43MB

9. Analyzing the Obtained data in Tableau/2. Analysis in Tableau Age vs Probability.mp456.48MB

9. Analyzing the Obtained data in Tableau/4. Analysis in Tableau Reasons vs Probability.mp459.32MB

9. Analyzing the Obtained data in Tableau/6. Analysis in Tableau Transportation Expense vs Probability.mp440.63MB