Course Modules

See the Latest and Complete DSPA Modules (Notes, Code and Assignments) Here

See the Latest and Complete DSPA Modules (Notes, Code and Assignments) Here
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See the Latest and Complete DSPA Modules (Notes, Code and Assignments) Here 34651    
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    Summer 2017 DSPA Topics and Learning Modules Summer 2017 DSPA Topics and Learning Modules
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2. Managing data with R

2. Managing data with R
Prerequisites: See the Latest and Complete DSPA Modules (Notes, Code and Assignments) Here
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2. Managing data with R 32936    
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    02_ManagingData_Notes 02_ManagingData_Notes
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    02_ManagingData_Assignment 02_ManagingData_Assignment
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3. Data Visualization

3. Data Visualization
Prerequisites: 2. Managing data with R, See the Latest and Complete DSPA Modules (Notes, Code and Assignments) Here
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3. Data Visualization 32937    
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    03_DataVisualization_Notes 03_DataVisualization_Notes
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    03_DataVisualization_Assignment 03_DataVisualization_Assignment
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4. Linear Algebra & Matrix Computing

4. Linear Algebra & Matrix Computing
Prerequisites: 2. Managing data with R
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4. Linear Algebra & Matrix Computing 32938    
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    04_Notes_Linear Algebra and Matrix Computing 04_Notes_Linear Algebra and Matrix Computing
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    04_Linear Algebra & Matrix Computing Assignment 04_Linear Algebra & Matrix Computing Assignment
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5. Dimensionality Reduction

5. Dimensionality Reduction
Prerequisites: 4. Linear Algebra & Matrix Computing, See the Latest and Complete DSPA Modules (Notes, Code and Assignments) Here
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5. Dimensionality Reduction 32939    
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    Chapter 05 Dimensionality Reduction Chapter 05 Dimensionality Reduction
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    05_DimensionalityReduction_Assignment 05_DimensionalityReduction_Assignment
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6. Classification Using Nearest Neighbors

6. Classification Using Nearest Neighbors
Prerequisites: 4. Linear Algebra & Matrix Computing, See the Latest and Complete DSPA Modules (Notes, Code and Assignments) Here
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6. Classification Using Nearest Neighbors 32954    
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    Chapter 06 Lazy Learning kNN Chapter 06 Lazy Learning kNN
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    06_LazyLearning_kNN_Assignment 06_LazyLearning_kNN_Assignment
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7. Classification Using Naive Bayes

7. Classification Using Naive Bayes
Prerequisites: 4. Linear Algebra & Matrix Computing
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7. Classification Using Naive Bayes 32955    
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    Chapter 7: Naive Bayesian Classisication Chapter 7: Naive Bayesian Classisication
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    07_NaiveBayesianClass_Assignment 07_NaiveBayesianClass_Assignment
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8. Classification Using Decision Trees

8. Classification Using Decision Trees
Prerequisites: 4. Linear Algebra & Matrix Computing, 2. Managing data with R
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8. Classification Using Decision Trees 32956    
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    Chapter 8: Decision Tree Classification Chapter 8: Decision Tree Classification
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    08_DecisionTreeClass_Assignment 08_DecisionTreeClass_Assignment
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9. Regression Forecasting

9. Regression Forecasting
Prerequisites: 4. Linear Algebra & Matrix Computing, 2. Managing data with R
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9. Regression Forecasting 32957    
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    Chapter 9: Regression Forecasting Chapter 9: Regression Forecasting
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    09_RegressionForecasting_Assignment 09_RegressionForecasting_Assignment
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10. Neural Networks and Support Vector Machines

10. Neural Networks and Support Vector Machines
Prerequisites: 6. Classification Using Nearest Neighbors
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10. Neural Networks and Support Vector Machines 32958    
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    Chapter 10: ML NN SVM Classification Chapter 10: ML NN SVM Classification
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    10_ML_NN_SVM_Class_Assignment 10_ML_NN_SVM_Class_Assignment
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11. Association Rule Learning

11. Association Rule Learning
Prerequisites: 8. Classification Using Decision Trees
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11. Association Rule Learning 32959    
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    Chapter 11: Apriory Association Rule Learning Chapter 11: Apriory Association Rule Learning
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12. k-Means Clustering

12. k-Means Clustering
Prerequisites: 6. Classification Using Nearest Neighbors
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12. k-Means Clustering 32960    
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    Chapter 12: k-Means Clustering Chapter 12: k-Means Clustering
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13. Evaluating Model Performance

13. Evaluating Model Performance
Prerequisites: 9. Regression Forecasting
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13. Evaluating Model Performance 32961    
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    Chapter 13: Model Evaluation Chapter 13: Model Evaluation
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14. Improving Model Performance

14. Improving Model Performance
Prerequisites: 13. Evaluating Model Performance
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14. Improving Model Performance 32962    
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    Chapter 14: Improving Model Performance Chapter 14: Improving Model Performance
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15. Data Formats and Computation Optimization

15. Data Formats and Computation Optimization
Prerequisites: 2. Managing data with R, 8. Classification Using Decision Trees
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15. Data Formats and Computation Optimization 32963    
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    Chapter 15: Specialized ML Formats Optimization Chapter 15: Specialized ML Formats Optimization
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16. Variable and Feature Selection

16. Variable and Feature Selection
Prerequisites: 9. Regression Forecasting, 6. Classification Using Nearest Neighbors
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16. Variable and Feature Selection 32966    
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    Chapter 16: Feature Selection Chapter 16: Feature Selection
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17. Regularized Linear Modeling and Knockoff Filtering

17. Regularized Linear Modeling and Knockoff Filtering
Prerequisites: 4. Linear Algebra & Matrix Computing, 9. Regression Forecasting
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17. Regularized Linear Modeling and Knockoff Filtering 32967    
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    Chapter 17: Regularized Linear Model and COntrolled Variable Selection Chapter 17: Regularized Linear Model and COntrolled Variable Selection
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18. Big Longitudinal Data Analysis

18. Big Longitudinal Data Analysis
Prerequisites: 9. Regression Forecasting, 2. Managing data with R
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18. Big Longitudinal Data Analysis 32968    
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    Chapter 18: Big Longitudinal Data Analysis Chapter 18: Big Longitudinal Data Analysis
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19. Text Mining & Natural Language Processing

19. Text Mining & Natural Language Processing
Prerequisites: 2. Managing data with R, 9. Regression Forecasting
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19. Text Mining & Natural Language Processing 32969    
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    Chapter 19: NLP and Text Mining Chapter 19: NLP and Text Mining
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20. Prediction and Internal Statistical Cross Validation

20. Prediction and Internal Statistical Cross Validation
Prerequisites: 4. Linear Algebra & Matrix Computing, See the Latest and Complete DSPA Modules (Notes, Code and Assignments) Here, 9. Regression Forecasting
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20. Prediction and Internal Statistical Cross Validation 42769    
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    Chapter 20 Prediction Cross Validation Chapter 20 Prediction Cross Validation
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21. Fuction Optimization

21. Fuction Optimization
Prerequisites: 4. Linear Algebra & Matrix Computing
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21. Fuction Optimization 42770    
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    Function Optimization Notes Function Optimization Notes
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22. Deep Learning

22. Deep Learning
Prerequisites: 2. Managing data with R, 4. Linear Algebra & Matrix Computing, 10. Neural Networks and Support Vector Machines
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22. Deep Learning 45275    
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    Chapter 22. Deep Learning Chapter 22. Deep Learning
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Request a Certificate of Completion

Request a Certificate of Completion
Prerequisites: See the Latest and Complete DSPA Modules (Notes, Code and Assignments) Here, 2. Managing data with R, 3. Data Visualization, 4. Linear Algebra & Matrix Computing, 5. Dimensionality Reduction, 6. Classification Using Nearest Neighbors, 7. Classification Using Naive Bayes, 8. Classification Using Decision Trees, 9. Regression Forecasting, 10. Neural Networks and Support Vector Machines, 11. Association Rule Learning, 12. k-Means Clustering, 13. Evaluating Model Performance, 14. Improving Model Performance, 15. Data Formats and Computation Optimization, 16. Variable and Feature Selection, 17. Regularized Linear Modeling and Knockoff Filtering, 18. Big Longitudinal Data Analysis, 19. Text Mining & Natural Language Processing, 20. Prediction and Internal Statistical Cross Validation, 21. Fuction Optimization, 22. Deep Learning
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Request a Certificate of Completion 42772   true
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    Chapter 01: Foundations Chapter 01: Foundations
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    Chapter 02: Managing Data Chapter 02: Managing Data
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    Chapter 03: Data Visualization Chapter 03: Data Visualization
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    Chapter 04: Linear Algebra & Matrix Computing Chapter 04: Linear Algebra & Matrix Computing
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    Chapter 05: Dimensionality Reduction Chapter 05: Dimensionality Reduction
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    Chapter 06: Lazy Learning kNN Chapter 06: Lazy Learning kNN
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    Chapter 07: Naive Bayesian Classification Chapter 07: Naive Bayesian Classification
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    Chapter 08: Decision-Tree Classificaiton Chapter 08: Decision-Tree Classificaiton
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    Chapter 09: Regression Forecasting Chapter 09: Regression Forecasting
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    Chapter 10: Nearest neighbor and SVM Classification Chapter 10: Nearest neighbor and SVM Classification
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    Chapter 11: Apriori Association Rule Learning Chapter 11: Apriori Association Rule Learning
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    Chapter 12: k-Means Clustering Chapter 12: k-Means Clustering
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    Chapter 13: Model Evaluation Chapter 13: Model Evaluation
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    Chapter 14: Improving Model Performance Chapter 14: Improving Model Performance
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    Chapter 15: Specialized ML Formats Optimization Chapter 15: Specialized ML Formats Optimization
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    Chapter 16: Feature Selection Chapter 16: Feature Selection
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    Chapter 17: Regularized Linear Modeling and Controlled Variable Selection Chapter 17: Regularized Linear Modeling and Controlled Variable Selection
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    Chapter 18: Big Longitudinal Data Analysis Chapter 18: Big Longitudinal Data Analysis
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    Chapter 19: NLP and Text Mining Chapter 19: NLP and Text Mining
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    Chapter 20: Prediction Cross Validation Chapter 20: Prediction Cross Validation
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    Chapter 21: Function Optimization Chapter 21: Function Optimization
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    Chapter 22: Deep Learning Chapter 22: Deep Learning
    Score at least   Must score at least   to complete this module item Scored at least   Module item has been completed by scoring at least   View Must view in order to complete this module item Viewed Module item has been viewed and is complete Mark done Must mark this module item done in order to complete Marked done Module item marked as done and is complete Contribute Must contribute to this module item to complete it Contributed Contributed to this module item and is complete Submit Must submit this module item to complete it Submitted Module item submitted and is complete
 
minimum score must view must submit must contribute