Informações sobre o curso

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Nível intermediário
Aprox. 17 horas para completar
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Habilidades que você terá

OversamplingLogistic RegressionPredictive Modellingregression
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 17 horas para completar
Inglês

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SAS

Programa - O que você aprenderá com este curso

Semana
1

Semana 1

1 hora para concluir

Course Overview and Logistics

1 hora para concluir
1 vídeo (Total 1 mín.), 6 leituras
1 vídeos
6 leituras
What You Learn in This Course5min
Learner Prerequisites1min
Using Forums and Getting Help10min
Access SAS Software for this Course10min
Set Up Data for This Course (REQUIRED) 30min
About the Demos and Practices in this Course10min
2 horas para concluir

Understanding Predictive Modeling

2 horas para concluir
15 vídeos (Total 34 mín.), 1 leitura, 6 testes
15 videos
Introduction19s
Goals of Predictive Modeling1min
Terms for Elements in Predictive Modeling49s
Basic Steps of Predictive Modeling2min
Applications of Predictive Modeling1min
Demonstration Scenario: Target Marketing for a Bank1min
Demo: Examining the Code for Generating Descriptive Statistics and Frequency Tables2min
Introduction21s
Data Challenges6min
Analytical Challenges2min
Separate Sampling1min
Avoiding the Optimism Bias: Honest Assessment2min
Splitting the Data for Model Training and Assessment3min
Demo: Splitting the Data5min
1 leituras
Summary10min
6 exercícios práticos
Practice: Exploring the Bank Data for the Target Marketing Project20min
Practice: Exploring the Veterans' Organization Data Used in the Practices20min
Question 1.015min
Question 1.025min
Question 1.035min
Practice: Splitting the Data20min
Semana
2

Semana 2

2 horas para concluir

Fitting the Model

2 horas para concluir
18 vídeos (Total 54 mín.), 1 leitura, 4 testes
18 videos
Introduction22s
Understanding the Logistic Regression Model2min
Constraining the Posterior Probability Using the Logit Transformation1min
Understanding the Fitted Surface1min
Interpreting the Model by Calculating the Odds Ratio3min
Understanding Logistic Discrimination1min
Estimating Unknown Parameters Using Maximum Likelihood Estimation2min
Interpreting Concordant, Discordant, and Tied Pairs1min
Using PROC LOGISTIC to Fit Logistic Regression Models24s
Demo: Fitting a Basic Logistic Regression Model, Part 18min
Demo: Fitting a Basic Logistic Regression Model, Part 212min
Scoring New Cases26s
Demo: Scoring New Cases7min
Introduction16s
Understanding the Effect of Oversampling53s
Understanding the Offset1min
Demo: Correcting for Oversampling6min
1 leituras
Summary10min
4 exercícios práticos
Question 2.015min
Question 2.025min
Practice: Fitting a Logistic Regression Model20min
Fitting the Model Review30min
Semana
3

Semana 3

3 horas para concluir

Preparing the Input Variables, Part 1

3 horas para concluir
26 vídeos (Total 76 mín.)
26 videos
Introduction22s
Reasons for Missing Data2min
Complete Case Analysis1min
Methods for Imputing Missing Values2min
Missing Value Imputation with Missing Value Indicator Variables3min
Demo: Imputing Missing Values4min
Cluster Imputation1min
Introduction25s
Problems Caused by Categorical Inputs4min
Solutions to Problems Caused by Categorical Inputs39s
Linking to Other Data Sets56s
Collapsing Categories by Thresholding53s
Collapsing Categories by Using Greenacre's Method3min
Demo: Collapsing the Levels of a Nominal Input, Part 16min
Demo: Collapsing the Levels of a Nominal Input, Part 210min
Replacing Categorical Levels by Using Smoothed Weight-of-Evidence Coding2min
Demo: Computing the Smoothed Weight of Evidence4min
Introduction20s
Problem of Redundancy2min
Variable Clustering Method1min
Understanding Principal Components5min
Divisive Clustering3min
PROC VARCLUS Syntax1min
Selecting a Representative Variable from Each Cluster1min
Demo: Reducing Redundancy by Clustering Variables8min
9 exercícios práticos
Question 3.015min
Practice: Imputing Missing Values20min
Question 3.025min
Question 3.035min
Question 3.045min
Practice: Collapsing the Levels of a Nominal Input20min
Practice: Computing the Smoothed Weight of Evidence20min
Question 3.055min
Practice: Reducing Redundancy by Clustering Variables20min
Semana
4

Semana 4

4 horas para concluir

Preparing the Input Variables, Part 2

4 horas para concluir
23 vídeos (Total 92 mín.), 1 leitura, 12 testes
23 videos
Detecting Nonlinear Relationships4min
Demo: Performing Variable Screening, Part 15min
Demo: Performing Variable Screening, Part 24min
Univariate Binning and Smoothing2min
Demo: Creating Empirical Logit Plots10min
Remedies for Nonlinear Relationships2min
Demo: Accommodating a Nonlinear Relationship, Part 16min
Demo: Accommodating a Nonlinear Relationship, Part 27min
Introduction26s
Specifying a Subset Selection Method in PROC LOGISTIC1min
Best-Subsets Selection54s
Stepwise Selection2min
Backward Elimination1min
Scalability of the Subset Selection Methods in PROC LOGISTIC2min
Detecting Interactions2min
BIC-based Significance Level2min
Demo: Detecting Interactions7min
Demo: Using Backward Elimination to Subset the Variables4min
Demo: Displaying Odds Ratios for Variables Involved in Interactions3min
Demo: Creating an Interaction Plot3min
Demo: Using the Best-Subsets Selection Method3min
Demo: Using Fit Statistics to Select a Model9min
1 leituras
Summary of Preparing the Input Variables, Parts 1 and 210min
12 exercícios práticos
Question 3.065min
Practice: Performing Variable Screening20min
Practice: Creating Empirical Logit Plots20min
Question 3.075min
Question 3.085min
Question 3.095min
Practice: Using Forward Selection to Detect Interactions20min
Question 3.105min
Practice: Using Backward Elimination to Subset the Variables20min
Question 3.115min
Practice: Using Fit Statistics to Select a Model20min
Preparing the Input Variables Review30min

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