GigaCourse Com ZeroToMastery Python for Business Data Analytics Intelligence |
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Name |
[GigaCourse.Com] ZeroToMastery - Python for Business Data Analytics & Intelligence |
DOWNLOAD Copy Link |
Total Size |
5.2 GB |
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Total Files |
257 |
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Last Seen |
2024-10-23 01:20 |
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Hash |
9D1ECF8DC53881E8990D6BD92F6FA1D5F3513E7A |
/0. Websites you may like/ |
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/1 Section 1 - Introduction/ |
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256.3 KB |
/1 Section 1 - Introduction/Introduction/ |
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26.3 MB |
/.../Python for Business Analytics & Intelligence/ |
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178.1 MB |
/.../Setting up the Course Material/ |
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36.2 MB |
/.../The Modern Day Business Analyst/ |
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11.5 MB |
/.../CASE STUDY - Catholic Schools & Standardized Tests (Briefing)/ |
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CASE STUDY - Catholic Schools & Standardized Tests (Briefing).mp4 |
3.7 MB |
/CHALLENGE - Introduction/ |
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36.5 MB |
/CHALLENGE - Solutions/ |
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117.6 MB |
/.../Common Support Region/ |
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14.9 MB |
/.../Matching - Game Plan/ |
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5.7 MB |
/Matching/ |
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8.8 MB |
/.../My Experience with Matching/ |
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5.7 MB |
/.../Python - Chi-square Loop/ |
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24.4 MB |
/.../Python - Chi-square Test/ |
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40.6 MB |
/.../Python - Cleaning and Preparing Dataset/ |
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30.2 MB |
/.../Python - Common Support Region Visualization/ |
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9.5 MB |
/.../Python - Comparing Means/ |
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21.0 MB |
/.../Python - Directory and Libraries/ |
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17.5 MB |
/.../Python - Education Variables/ |
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46.3 MB |
/.../Python - Loading Data/ |
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32.9 MB |
/.../Python - Logistic Regression and Debugging/ |
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97.2 MB |
/Python - Matching/ |
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45.6 MB |
/.../Python - Other Variables/ |
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25.0 MB |
/.../Python - Outcome Visualization/ |
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9.5 MB |
/.../Python - Preparing for Common Support Region/ |
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36.7 MB |
/.../Python - Race Variable Transformation/ |
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90.3 MB |
/.../Python - Robustness Check - Removing 1 confounder/ |
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30.9 MB |
/.../Python - Robustness Check - Repeated experiments/ |
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60.2 MB |
/.../Python - T-Test Loop/ |
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25.0 MB |
/Python - T-Test/ |
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27.5 MB |
/Robustness Checks/ |
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4.9 MB |
/.../The Curse of Dimensionality/ |
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5.5 MB |
/Unconfoundedness/ |
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5.5 MB |
/11 PART C_ SEGMENTATION/ |
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213.5 KB |
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/.../CASE STUDY - Online Shopping (Briefing)/ |
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1.6 MB |
/CHALLENGE - Introduction/ |
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11.5 MB |
/CHALLENGE - Solutions/ |
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40.3 MB |
/.../Python - Applying RFM Function/ |
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9.0 MB |
/.../Python - Creating Sales Variable/ |
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15.0 MB |
/.../Python - Customer Level Aggregation/ |
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22.6 MB |
/.../Python - Date Variable/ |
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12.0 MB |
/.../Python - Directory and Libraries/ |
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10.2 MB |
/.../Python - Loading Data/ |
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22.2 MB |
/.../Python - Monetary Variable/ |
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5.5 MB |
/Python - Quartiles/ |
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24.1 MB |
/.../Python - RFM Function/ |
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11.9 MB |
/.../Python - RFM Score/ |
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6.9 MB |
/.../Python - Results Summary/ |
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15.7 MB |
/.../Python - Tidying up Dataframe/ |
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12.7 MB |
/.../RFM - Game Plan/ |
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2.4 MB |
/RFM Model/ |
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8.4 MB |
/.../Value Based Segmentation/ |
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5.0 MB |
/.../AIC and BIC/ |
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6.1 MB |
/.../CASE STUDY - Credit Cards #1 (Briefing)/ |
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2.8 MB |
/CHALLENGE - Introduction/ |
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38.6 MB |
/CHALLENGE - Solutions/ |
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226.9 MB |
/Clustering/ |
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4.9 MB |
/.../Gaussian Mixture - Game Plan/ |
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2.9 MB |
/.../Gaussian Mixture Model/ |
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8.4 MB |
/.../My Experience with Segmentation/ |
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7.6 MB |
/.../Python - Cluster Prediction and Assignment/ |
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13.5 MB |
/.../Python - Directory and Data/ |
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15.5 MB |
/.../Python - Gaussian Mixture Model/ |
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9.6 MB |
/Python - Interpretation/ |
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109.0 MB |
/.../Python - Load Data/ |
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21.4 MB |
/.../Python - Optimal Number of Clusters/ |
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30.7 MB |
/.../Python - Transform Character variables/ |
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14.9 MB |
/14 PART D_ PREDICTIVE ANALYTICS/ |
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What are Predictive Analytics and why are they important.pdf |
190.6 KB |
/.../CASE STUDY - Credit Cards #2 (Briefing)/ |
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1.2 MB |
/CHALLENGE - Introduction/ |
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35.7 MB |
/.../CHALLENGE - Solutions (Part 1)/ |
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57.1 MB |
/.../CHALLENGE - Solutions (Part 2)/ |
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51.4 MB |
/.../Ensemble Learning and Random Forest/ |
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4.6 MB |
/.../How Decision Trees Work/ |
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9.5 MB |
/Parameter Tuning/ |
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6.2 MB |
/.../Python - Classification Report and F1 score/ |
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26.2 MB |
/.../Python - Directory and Libraries/ |
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10.3 MB |
/.../Python - Feature Importance/ |
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20.5 MB |
/.../Python - Isolate X and Y/ |
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15.9 MB |
/.../Python - Loading Data/ |
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19.4 MB |
/.../Python - Parameter Grid/ |
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16.5 MB |
/.../Python - Parameter Tuning/ |
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45.9 MB |
/Python - Predictions/ |
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5.1 MB |
/.../Python - Random Forest Model/ |
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12.3 MB |
/.../Python - Summary Statistics/ |
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21.9 MB |
/.../Python - Training and Test Set/ |
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28.8 MB |
/.../Python - Transform Object into Numerical Variables/ |
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10.4 MB |
/.../Random Forest - Game Plan/ |
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3.1 MB |
/.../Random Forest Quirks/ |
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4.0 MB |
/15 Section 11 - Random Forest/ |
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0.1 KB |
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0.0 KB |
/.../Additive vs [TutFlix.ORG]. Multiplicative Seasonality/ |
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6.4 MB |
/.../CASE STUDY - Wikipedia (Briefing) [TutFlix.ORG]/ |
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2.5 MB |
/.../CHALLENGE - Introduction [TutFlix.ORG]/ |
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21.0 MB |
/.../CHALLENGE - Solutions (Part 1) [TutFlix.ORG]/ |
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59.4 MB |
/.../CHALLENGE - Solutions (Part 2) [TutFlix.ORG]/ |
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97.1 MB |
/.../CHALLENGE - Solutions (Part 3) [TutFlix.ORG]/ |
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66.8 MB |
/.../Cross-validation [TutFlix.ORG]/ |
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2.9 MB |
/.../Dynamic Holidays [TutFlix.ORG]/ |
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5.2 MB |
/.../Facebook Prophet - Game Plan [TutFlix.ORG]/ |
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3.4 MB |
/.../Facebook Prophet Model [TutFlix.ORG]/ |
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37.5 MB |
/.../Facebook Prophet Parameters [TutFlix.ORG]/ |
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5.5 MB |
/.../Facebook Prophet [TutFlix.ORG]/ |
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7.9 MB |
/.../Forecasting at Uber [TutFlix.ORG]/ |
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9.5 MB |
/.../Parameters to tune [TutFlix.ORG]/ |
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3.4 MB |
/.../Python - Accuracy Assessment [TutFlix.ORG]/ |
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20.5 MB |
/.../Python - Black Friday [TutFlix.ORG]/ |
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15.2 MB |
/.../Python - Combining Events and Preparing Dataframe [TutFlix.ORG]/ |
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15.7 MB |
/.../Python - Cross-validation [TutFlix.ORG]/ |
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51.1 MB |
/.../Python - Directory and Libraries [TutFlix.ORG]/ |
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14.1 MB |
/.../Python - Easter Holidays [TutFlix.ORG]/ |
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24.0 MB |
/.../Python - Forecasting [TutFlix.ORG]/ |
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22.3 MB |
/.../Python - Future Dataframe [TutFlix.ORG]/ |
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34.0 MB |
/.../Python - Loading Data [TutFlix.ORG]/ |
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11.8 MB |
/.../Python - Parameter Grid [TutFlix.ORG]/ |
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23.8 MB |
/.../Python - Parameter Tuning [TutFlix.ORG]/ |
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49.4 MB |
/.../Python - Regressor Coefficients [TutFlix.ORG]/ |
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8.1 MB |
/.../Python - Renaming Variables [TutFlix.ORG]/ |
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9.6 MB |
/.../Python - Training and Test Set [TutFlix.ORG]/ |
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11.4 MB |
/.../Python - Transforming Date Variable [TutFlix.ORG]/ |
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18.4 MB |
/.../Python - Visualization [TutFlix.ORG]/ |
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32.1 MB |
/.../Structural Time Series [TutFlix.ORG]/ |
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7.2 MB |
/.../Training and Test Set [TutFlix.ORG]/ |
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4.9 MB |
/Thank You!/ |
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93.3 MB |
/2 PART A_ STATISTICS/ |
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173.7 KB |
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0.1 KB |
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/Arithmetic Mean/ |
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7.3 MB |
/.../Basic Statistics - Game Plan/ |
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2.8 MB |
/.../CASE STUDY - Moneyball (Briefing)/ |
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1.5 MB |
/.../CASE STUDY - Moneyball/ |
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7.9 MB |
/Correlation/ |
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16.7 MB |
/.../EXERCISE - Python - Correlation/ |
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15.7 MB |
/.../EXERCISE - Python - Mean/ |
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12.2 MB |
/.../EXERCISE - Python - Median/ |
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13.3 MB |
/.../EXERCISE - Python - Mode/ |
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12.5 MB |
/.../EXERCISE - Python - Standard Deviation/ |
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4.4 MB |
/.../Median and Mode/ |
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4.9 MB |
/Python - Correlation/ |
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38.0 MB |
/.../Python - Directory, Libraries and Data/ |
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41.8 MB |
/Python - Mean/ |
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40.8 MB |
/Python - Median/ |
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18.3 MB |
/Python - Mode/ |
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16.3 MB |
/.../Python - Standard Deviation/ |
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19.5 MB |
/Standard Deviation/ |
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4.5 MB |
/.../CASE STUDY - Remote Work Predictions (Briefing)/ |
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2.4 MB |
/.../CASE STUDY - Wine Quality (Briefing)/ |
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5.6 MB |
/.../Chi-square test/ |
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6.2 MB |
/Confidence Interval/ |
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37.8 MB |
/.../EXERCISE - Python - Chi-square/ |
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18.4 MB |
/.../EXERCISE - Python - Confidence Interval/ |
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14.6 MB |
/.../EXERCISE - Python - Normal Distribution/ |
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28.3 MB |
/.../EXERCISE - Python - Shapiro-Wilks/ |
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10.7 MB |
/.../EXERCISE - Python - Standard Error/ |
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11.1 MB |
/.../EXERCISE - Python - T-test/ |
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24.0 MB |
/.../Intermediary Statistics - Game Plan/ |
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2.0 MB |
/Normal Distribution/ |
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7.5 MB |
/P-Value/ |
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15.2 MB |
/.../Powerposing and p-hacking/ |
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11.1 MB |
/.../Python - Chi-square test/ |
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49.4 MB |
/.../Python - Confidence Interval/ |
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42.4 MB |
/.../Python - Normal Distribution Visualization/ |
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38.6 MB |
/.../Python - Preparing Script and Loading Data/ |
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21.3 MB |
/.../Python - Shapiro-Wilks Test/ |
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43.9 MB |
/.../Python - Standard Error/ |
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27.1 MB |
/Python - T-test/ |
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59.5 MB |
/.../Shapiro-Wilks Test/ |
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4.8 MB |
/.../Standard Error of the Mean/ |
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6.2 MB |
/T-test/ |
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5.6 MB |
/Z-Score/ |
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6.1 MB |
/4 Section 3 - Intermediary Statistics/ |
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0.1 KB |
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0.0 KB |
/.../CASE STUDY - Diamonds (Briefing)/ |
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2.9 MB |
/.../Dummy Variable Trap/ |
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7.8 MB |
/.../EXERCISE - Python - Linear Regression/ |
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26.5 MB |
/.../Linear Regression - Game Plan/ |
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3.1 MB |
/.../Linear Regression Output/ |
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35.9 MB |
/Linear Regression/ |
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10.6 MB |
/.../Python - Adding Constant/ |
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9.0 MB |
/.../Python - Dummy Variable/ |
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15.3 MB |
/.../Python - Isolate X and Y/ |
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11.3 MB |
/.../Python - Linear Regression Model and Summary/ |
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17.4 MB |
/.../Python - Plotting Regression/ |
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22.6 MB |
/.../Python - Preparing Script and Loading Data/ |
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23.3 MB |
/.../Accuracy KPIs (Key Performance Indicators)/ |
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9.0 MB |
/.../CASE STUDY - Professors' Salary (Briefing)/ |
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1.6 MB |
/CHALLENGE - Introduction/ |
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19.5 MB |
/CHALLENGE - Solutions/ |
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68.2 MB |
/.../Multilinear Regression - Game Plan/ |
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3.5 MB |
/Outliers/ |
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4.9 MB |
/.../Python - Accuracy Assessment/ |
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22.7 MB |
/.../Python - Adding Constant/ |
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4.8 MB |
/.../Python - Categorical Variables/ |
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19.3 MB |
/.../Python - Correlation Matrix/ |
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12.5 MB |
/.../Python - Creating Dummy Variables/ |
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9.7 MB |
/.../Python - For Loop/ |
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18.5 MB |
/.../Python - Isolate X and Y/ |
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15.0 MB |
/.../Python - Model Predictions/ |
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6.1 MB |
/.../Python - Multilinear Regression/ |
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22.4 MB |
/.../Python - Plotting Continuous Variables/ |
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23.1 MB |
/.../Python - Preparing Script and Loading Data/ |
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28.1 MB |
/.../Python - Summary Statistics/ |
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10.0 MB |
/.../Python - Train and Test Split/ |
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8.7 MB |
/.../The Concept of Multilinear Regression/ |
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3.0 MB |
/.../Training and Test Set/ |
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1.8 MB |
/.../Under and Over Fitting/ |
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2.9 MB |
/.../CASE STUDY - Spam Emails (Briefing)/ |
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2.8 MB |
/CHALLENGE - Introduction/ |
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27.3 MB |
/CHALLENGE - Solutions/ |
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69.6 MB |
/Confusion Matrix/ |
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16.0 MB |
/.../How to Read Logistic Regression Coefficients/ |
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7.1 MB |
/.../Logistic Regression - Game Plan/ |
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3.0 MB |
/Logistic Regression/ |
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8.8 MB |
/.../Python - Classification Report/ |
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16.7 MB |
/.../Python - Confusion Matrix/ |
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28.7 MB |
/.../Python - Correlation Matrix/ |
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12.4 MB |
/.../Python - Function to Read Coefficients/ |
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38.1 MB |
/.../Python - Histogram and Outlier Removal/ |
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46.4 MB |
/.../Python - Logistic Regression/ |
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16.0 MB |
/.../Python - Manual Accuracy Assessment/ |
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23.8 MB |
/Python - Predictions/ |
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16.3 MB |
/.../Python - Prepare X and Y/ |
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9.2 MB |
/.../Python - Preparing Script and Loading Data/ |
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26.4 MB |
/.../Python - Summary Statistics/ |
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26.8 MB |
/.../Python - Training and Test Set/ |
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24.8 MB |
/.../Python - Transforming Dependent Variable/ |
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19.0 MB |
/8 PART B_ ECONOMETRICS & CAUSAL INFERENCE/ |
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What are Econometrics & Causal Inference and why are they important.pdf |
195.3 KB |
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0.1 KB |
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0.0 KB |
/Assumptions/ |
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6.0 MB |
/.../CASE STUDY - Bitcoin Pricing (Briefing)/ |
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6.5 MB |
/CHALLENGE - Introduction/ |
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24.9 MB |
/CHALLENGE - Solutions/ |
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69.8 MB |
/.../Causal Impact Step-by-Step/ |
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3.6 MB |
/.../Correlation Recap and Stationarity/ |
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8.5 MB |
/.../Difference-in-Differences Framework/ |
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4.7 MB |
/.../Google Causal Impact - Game Plan/ |
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3.2 MB |
/.../Interpretation of Results/ |
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12.3 MB |
/.../Python - Bitcoin Price loading/ |
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20.1 MB |
/Python - Correlation/ |
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18.7 MB |
/.../Python - Defining Dates/ |
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9.0 MB |
/.../Python - Google Causal Impact Setup/ |
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9.4 MB |
/.../Python - Google Causal Impact/ |
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40.2 MB |
/.../Python - Impact Results/ |
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35.9 MB |
/.../Python - Installing and Importing Libraries/ |
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12.5 MB |
/.../Python - Load Control Groups/ |
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21.6 MB |
/.../Python - Preparing DataFrame/ |
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48.2 MB |
/.../Python - Preparing for Correlation Matrix/ |
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16.4 MB |
/Python - Stationarity/ |
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48.7 MB |
/.../Time Series Data/ |
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3.1 MB |
/.../Why Econometrics and Causal Inference/ |
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11.7 MB |
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Total files 257 |
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