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SNHU-DAT650.AE1 ISBN: 978-1-61691-081-5
DAT-650: Advanced Data Analytics
Expert Self-paced · 1 year access
19 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
01 / Lessons & labs
See exactly what you will learn and practice
Lessons
12 Interactive Lessons · 100 topics01 Case Study, Part 1: Business Understanding, Data Preparation, and EDA 5 topics +
- Cross-Industry Standard Practice for Data Mining
- Business Understanding Phase
- Data Understanding Phase, Part 1: Getting a Feel for the Data Set
- Data Preparation Phase
- Data Understanding Phase, Part 2: Exploratory Data Analysis
02 Milestone 1 LiveLab +
1 LiveLab in this lesson — see the labs panel →
03 Multivariate Statistics 9 topics +
- Two-Sample t-Test for Difference in Means
- Two-Sample Z-Test for Difference in Proportions
- Test for the Homogeneity of Proportions
- Chi-Square Test for Goodness of Fit of Multinomial Data
- Analysis of Variance
- Reference
- The R Zone
- R Reference
- Exercises
04 Case Study, Part 2: Clustering and Principal Components Analysis 9 topics +
- Partitioning the Data
- Developing the Principal Components
- Validating the Principal Components
- Profiling the Principal Components
- Choosing the Optimal Number of Clusters Using Birch Clustering
- Choosing the Optimal Number of Clusters Using k-Means Clustering
- Application of k-Means Clustering
- Validating the Clusters
- Profiling the Clusters
05 k-Nearest Neighbor Algorithm 12 topics · 2 LiveLab +
- Classification Task
- k-Nearest Neighbor Algorithm
- Distance Function
- Combination Function
- Quantifying Attribute Relevance: Stretching the Axes
- Database Considerations
- k-Nearest Neighbor Algorithm for Estimation and Prediction
- Choosing k
- Application of k-Nearest Neighbor Algorithm Using IBM/SPSS Modeler
- The R Zone
- R References
- Exercises
2 LiveLab in this lesson — see the labs panel →
06 Association Rules 13 topics · 1 LiveLab +
- Affinity Analysis and Market Basket Analysis
- Support, Confidence, Frequent Itemsets, and the A Priori Property
- How Does The A Priori Algorithm Work (Part 1)? Generating Frequent Itemsets
- How Does The A Priori Algorithm Work (Part 2)? Generating Association Rules
- Extension From Flag Data to General Categorical Data
- Information-Theoretic Approach: Generalized Rule Induction Method
- Association Rules are Easy to do Badly
- How Can We Measure the Usefulness of Association Rules?
- Do Association Rules Represent Supervised or Unsupervised Learning?
- Local Patterns Versus Global Models
- The R Zone
- R References
- Exercises
1 LiveLab in this lesson — see the labs panel →
07 Multiple Regression and Model Building 14 topics · 5 LiveLab +
- An Example of Multiple Regression
- The Population Multiple Regression Equation
- Inference in Multiple Regression
- Regression With Categorical Predictors, Using Indicator Variables
- Adjusting R2: Penalizing Models For Including Predictors That Are Not Useful
- Sequential Sums of Squares
- Multicollinearity
- Variable Selection Methods
- Gas Mileage Data Set
- An Application of Variable Selection Methods
- Using the Principal Components as Predictors in Multiple Regression
- The R Zone
- R References
- Exercises
5 LiveLab in this lesson — see the labs panel →
08 Variable Selection Methods 5 LiveLab +
5 LiveLab in this lesson — see the labs panel →
09 NaïVe Bayes and Bayesian Networks 15 topics · 4 LiveLab +
- Bayesian Approach
- Maximum A Posteriori (MAP) Classification
- Posterior Odds Ratio
- Balancing The Data
- Naïve Bayes Classification
- Interpreting The Log Posterior Odds Ratio
- Zero-Cell Problem
- Numeric Predictors for Naïve Bayes Classification
- WEKA: Hands-on Analysis Using Naïve Bayes
- Bayesian Belief Networks
- Clothing Purchase Example
- Using The Bayesian Network to Find Probabilities
- The R Zone
- R References
- Exercises
4 LiveLab in this lesson — see the labs panel →
10 Imputation of Missing Data 8 topics · 2 LiveLab +
- Need for Imputation of Missing Data
- Imputation of Missing Data: Continuous Variables
- Standard Error of the Imputation
- Imputation of Missing Data: Categorical Variables
- Handling Patterns in Missingness
- Reference
- The R Zone
- R References
2 LiveLab in this lesson — see the labs panel →
11 Case Study, Part 3: Modeling And Evaluation For Performance And Interpretability 9 topics · 1 LiveLab +
- Do You Prefer The Best Model Performance, Or A Combination Of Performance And Interpretability?
- Modeling And Evaluation Overview
- Cost-Benefit Analysis Using Data-Driven Costs
- Variables to be Input To The Models
- Establishing The Baseline Model Performance
- Models That Use Misclassification Costs
- Models That Need Rebalancing as a Surrogate for Misclassification Costs
- Combining Models Using Voting and Propensity Averaging
- Interpreting The Most Profitable Model
1 LiveLab in this lesson — see the labs panel →
12 Case Study, Part 4: Modeling and Evaluation for High Performance Only 6 topics +
- Variables to be Input to the Models
- Models that use Misclassification Costs
- Models that Need Rebalancing as a Surrogate for Misclassification Costs
- Combining Models using Voting and Propensity Averaging
- Lessons Learned
- Conclusions
Hands-On Labs Our edge
19 LiveLabs- Milestone I
- Running KNN
- Calculating the Euclidean Distance
- Milestone 2
- Approximating the Relationship between the Variables in a Scatterplot
- Identifying Confidence Intervals
- Creating a Dot Plot
- Determining the Sequential Sums of Squares
- Analyzing Multicollinearity
- Applying the Best Subsets Procedure in a Regression Model
- Applying Forward Selection Procedure
- Applying the Backward Elimination Procedure
- Applying the Stepwise Selection Procedure in a Regression Model
- Using the Principal Components as Predictors in Multiple Regression
- Calculating Posterior Odds Ratio
- Calculating the Log Posterior Odds Ratio
- Calculating the Numeric Predictors for Naive Bayes Classification
- Milestone 3
- Final Project
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02 / FAQs
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