Course Modules

HS650 DSPA Welcome

HS650 DSPA Welcome
Module Completed Module In Progress Module Locked
HS650 DSPA Welcome 171250  
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    Welcome Video Welcome Video
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    Welcome Video Welcome Video
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    MIDAS R&D Spacekime Video (Ivo Dinov/SOCR) MIDAS R&D Spacekime Video (Ivo Dinov/SOCR)
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    AMIA_Video_Data_Tools_Services_Nov_2020 AMIA_Video_Data_Tools_Services_Nov_2020
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Introduction, Organization & Motivation

Introduction, Organization & Motivation
Prerequisites: HS650 DSPA Welcome
Module Completed Module In Progress Module Locked
Introduction, Organization & Motivation 171258  
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    Introduction, Organization & Motivation Introduction, Organization & Motivation
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Chapter 1: R Foundations

Chapter 1: R Foundations
Prerequisites: Introduction, Organization & Motivation
Module Completed Module In Progress Module Locked
Chapter 1: R Foundations 171259  
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    R Foundations R Foundations
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Chapter 2: Data Management

Chapter 2: Data Management
Prerequisites: Chapter 1: R Foundations
Module Completed Module In Progress Module Locked
Chapter 2: Data Management 171260  
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    Data Management Data Management
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Chapter 3: Data Visualization

Chapter 3: Data Visualization
Prerequisites: Chapter 2: Data Management
Module Completed Module In Progress Module Locked
Chapter 3: Data Visualization 171264  
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    Data Visualization Data Visualization
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Chapter 4: Linear Algebra & Matrix Computing

Chapter 4: Linear Algebra & Matrix Computing
Prerequisites: Chapter 3: Data Visualization
Module Completed Module In Progress Module Locked
Chapter 4: Linear Algebra & Matrix Computing 188224  
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    Linear Algebra & Matrix Computing Linear Algebra & Matrix Computing
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Chapter 5: Dimensionality Reduction

Chapter 5: Dimensionality Reduction
Prerequisites: Chapter 4: Linear Algebra & Matrix Computing
Module Completed Module In Progress Module Locked
Chapter 5: Dimensionality Reduction 188225  
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    Dimensionality Reduction Dimensionality Reduction
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Chapter 6: Lazy Learning – Classification Using Nearest Neighbors

Chapter 6: Lazy Learning – Classification Using Nearest Neighbors
Prerequisites: Chapter 5: Dimensionality Reduction
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Chapter 6: Lazy Learning – Classification Using Nearest Neighbors 188226  
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    Lazy Learning – Classification Using Nearest Neighbors Lazy Learning – Classification Using Nearest Neighbors
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Chapter 7: Probabilistic Learning – Classification Using Naive Bayes

Chapter 7: Probabilistic Learning – Classification Using Naive Bayes
Prerequisites: Chapter 6: Lazy Learning – Classification Using Nearest Neighbors
Module Completed Module In Progress Module Locked
Chapter 7: Probabilistic Learning – Classification Using Naive Bayes 188227  
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    Probabilistic Learning – Classification Using Naive Bayes Probabilistic Learning – Classification Using Naive Bayes
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Chapter 8: Divide and Conquer – Classification

Chapter 8: Divide and Conquer – Classification
Prerequisites: Chapter 7: Probabilistic Learning – Classification Using Naive Bayes
Module Completed Module In Progress Module Locked
Chapter 8: Divide and Conquer – Classification 188228  
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    Divide and Conquer – Classification Divide and Conquer – Classification
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Chapter 9: Forecasting Numeric Data – Regression Methods

Chapter 9: Forecasting Numeric Data – Regression Methods
Prerequisites: Chapter 8: Divide and Conquer – Classification
Module Completed Module In Progress Module Locked
Chapter 9: Forecasting Numeric Data – Regression Methods 188229  
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    Forecasting Numeric Data – Regression Methods Forecasting Numeric Data – Regression Methods
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Chapter 10: Black Box Methods – Neural Networks and Support Vector Machines

Chapter 10: Black Box Methods – Neural Networks and Support Vector Machines
Prerequisites: Chapter 9: Forecasting Numeric Data – Regression Methods
Module Completed Module In Progress Module Locked
Chapter 10: Black Box Methods – Neural Networks and Support Vector Machines 188230  
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    Black Box Methods – Neural Networks and Support Vector Machines Black Box Methods – Neural Networks and Support Vector Machines
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Chapter 11: Apriori Algorithm for Association Rule Learning

Chapter 11: Apriori Algorithm for Association Rule Learning
Prerequisites: Chapter 10: Black Box Methods – Neural Networks and Support Vector Machines
Module Completed Module In Progress Module Locked
Chapter 11: Apriori Algorithm for Association Rule Learning 188231  
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    Apriori Algorithm for Association Rule Learning Apriori Algorithm for Association Rule Learning
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Chapter 12: k-Means Clustering

Chapter 12: k-Means Clustering
Prerequisites: Chapter 11: Apriori Algorithm for Association Rule Learning
Module Completed Module In Progress Module Locked
Chapter 12: k-Means Clustering 188232  
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    k-Means Clustering k-Means Clustering
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Chapter 13: Evaluating Model Performance

Chapter 13: Evaluating Model Performance
Prerequisites: Chapter 12: k-Means Clustering
Module Completed Module In Progress Module Locked
Chapter 13: Evaluating Model Performance 188233  
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    Evaluating Model Performance Evaluating Model Performance
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Chapter 14: Improving Model Performance

Chapter 14: Improving Model Performance
Prerequisites: Chapter 13: Evaluating Model Performance
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Chapter 14: Improving Model Performance 188234  
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    Improving Model Performance Improving Model Performance
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Chapter 15: Specialized Machine Learning Topics: Data Formats and Optimization of Computation

Chapter 15: Specialized Machine Learning Topics: Data Formats and Optimization of Computation
Prerequisites: Chapter 14: Improving Model Performance
Module Completed Module In Progress Module Locked
Chapter 15: Specialized Machine Learning Topics: Data Formats and Optimization of Computation 188235  
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    Specialized Machine Learning Topics: Data Formats and Optimization of Computation Specialized Machine Learning Topics: Data Formats and Optimization of Computation
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Chapter 16: Variable/Feature Selection

Chapter 16: Variable/Feature Selection
Prerequisites: Chapter 15: Specialized Machine Learning Topics: Data Formats and Optimization of Computation
Module Completed Module In Progress Module Locked
Chapter 16: Variable/Feature Selection 188236  
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    Variable/Feature Selection Variable/Feature Selection
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Chapter 17: Regularized Linear Modeling and Knockoff Filtering

Chapter 17: Regularized Linear Modeling and Knockoff Filtering
Prerequisites: Chapter 16: Variable/Feature Selection
Module Completed Module In Progress Module Locked
Chapter 17: Regularized Linear Modeling and Knockoff Filtering 188237  
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    Regularized Linear Modeling and Knockoff Filtering Regularized Linear Modeling and Knockoff Filtering
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Chapter 18: Big Longitudinal Data Analysis

Chapter 18: Big Longitudinal Data Analysis
Prerequisites: Chapter 17: Regularized Linear Modeling and Knockoff Filtering
Module Completed Module In Progress Module Locked
Chapter 18: Big Longitudinal Data Analysis 188238  
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    Big Longitudinal Data Analysis Big Longitudinal Data Analysis
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Chapter 19: Text Mining & Natural Language Processing

Chapter 19: Text Mining & Natural Language Processing
Prerequisites: Chapter 18: Big Longitudinal Data Analysis
Module Completed Module In Progress Module Locked
Chapter 19: Text Mining & Natural Language Processing 188239  
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    Text Mining & Natural Language Processing Text Mining & Natural Language Processing
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Chapter 20: Prediction & Internal Statistical Cross-validation

Chapter 20: Prediction & Internal Statistical Cross-validation
Prerequisites: Chapter 19: Text Mining & Natural Language Processing
Module Completed Module In Progress Module Locked
Chapter 20: Prediction & Internal Statistical Cross-validation 188240  
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    Prediction & Internal Statistical Cross-validation Prediction & Internal Statistical Cross-validation
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Chapter 21: Function Optimization

Chapter 21: Function Optimization
Prerequisites: Chapter 20: Prediction & Internal Statistical Cross-validation
Module Completed Module In Progress Module Locked
Chapter 21: Function Optimization 188243  
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    Function Optimization Function Optimization
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Chapter 22: Deep Learning

Chapter 22: Deep Learning
Prerequisites: Chapter 21: Function Optimization
Module Completed Module In Progress Module Locked
Chapter 22: Deep Learning 188244  
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    Deep Learning Deep Learning
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minimum score must view must submit must contribute