À propos de ce cours

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Niveau intermédiaire
Approx. 17 heures pour terminer
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Compétences que vous acquerrez

OversamplingLogistic RegressionPredictive Modellingregression
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
Approx. 17 heures pour terminer
Anglais

Offert par

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SAS

Programme du cours : ce que vous apprendrez dans ce cours

Semaine
1

Semaine 1

1 heure pour terminer

Course Overview and Logistics

1 heure pour terminer
1 vidéo (Total 1 min), 6 lectures
1 vidéo
6 lectures
What You Learn in This Course5 min
Learner Prerequisites1 min
Using Forums and Getting Help10 min
Access SAS Software for this Course10 min
Set Up Data for This Course (REQUIRED) 30 min
About the Demos and Practices in this Course10 min
2 heures pour terminer

Understanding Predictive Modeling

2 heures pour terminer
15 vidéos (Total 34 min), 1 lecture, 6 quiz
15 vidéos
Introduction19s
Goals of Predictive Modeling1 min
Terms for Elements in Predictive Modeling49s
Basic Steps of Predictive Modeling2 min
Applications of Predictive Modeling1 min
Demonstration Scenario: Target Marketing for a Bank1 min
Demo: Examining the Code for Generating Descriptive Statistics and Frequency Tables2 min
Introduction21s
Data Challenges6 min
Analytical Challenges2 min
Separate Sampling1 min
Avoiding the Optimism Bias: Honest Assessment2 min
Splitting the Data for Model Training and Assessment3 min
Demo: Splitting the Data5 min
1 lecture
Summary10 min
6 exercices pour s'entraîner
Practice: Exploring the Bank Data for the Target Marketing Project20 min
Practice: Exploring the Veterans' Organization Data Used in the Practices20 min
Question 1.015 min
Question 1.025 min
Question 1.035 min
Practice: Splitting the Data20 min
Semaine
2

Semaine 2

2 heures pour terminer

Fitting the Model

2 heures pour terminer
18 vidéos (Total 54 min), 1 lecture, 4 quiz
18 vidéos
Introduction22s
Understanding the Logistic Regression Model2 min
Constraining the Posterior Probability Using the Logit Transformation1 min
Understanding the Fitted Surface1 min
Interpreting the Model by Calculating the Odds Ratio3 min
Understanding Logistic Discrimination1 min
Estimating Unknown Parameters Using Maximum Likelihood Estimation2 min
Interpreting Concordant, Discordant, and Tied Pairs1 min
Using PROC LOGISTIC to Fit Logistic Regression Models24s
Demo: Fitting a Basic Logistic Regression Model, Part 18 min
Demo: Fitting a Basic Logistic Regression Model, Part 212 min
Scoring New Cases26s
Demo: Scoring New Cases7 min
Introduction16s
Understanding the Effect of Oversampling53s
Understanding the Offset1 min
Demo: Correcting for Oversampling6 min
1 lecture
Summary10 min
4 exercices pour s'entraîner
Question 2.015 min
Question 2.025 min
Practice: Fitting a Logistic Regression Model20 min
Fitting the Model Review30 min
Semaine
3

Semaine 3

3 heures pour terminer

Preparing the Input Variables, Part 1

3 heures pour terminer
26 vidéos (Total 76 min)
26 vidéos
Introduction22s
Reasons for Missing Data2 min
Complete Case Analysis1 min
Methods for Imputing Missing Values2 min
Missing Value Imputation with Missing Value Indicator Variables3 min
Demo: Imputing Missing Values4 min
Cluster Imputation1 min
Introduction25s
Problems Caused by Categorical Inputs4 min
Solutions to Problems Caused by Categorical Inputs39s
Linking to Other Data Sets56s
Collapsing Categories by Thresholding53s
Collapsing Categories by Using Greenacre's Method3 min
Demo: Collapsing the Levels of a Nominal Input, Part 16 min
Demo: Collapsing the Levels of a Nominal Input, Part 210 min
Replacing Categorical Levels by Using Smoothed Weight-of-Evidence Coding2 min
Demo: Computing the Smoothed Weight of Evidence4 min
Introduction20s
Problem of Redundancy2 min
Variable Clustering Method1 min
Understanding Principal Components5 min
Divisive Clustering3 min
PROC VARCLUS Syntax1 min
Selecting a Representative Variable from Each Cluster1 min
Demo: Reducing Redundancy by Clustering Variables8 min
9 exercices pour s'entraîner
Question 3.015 min
Practice: Imputing Missing Values20 min
Question 3.025 min
Question 3.035 min
Question 3.045 min
Practice: Collapsing the Levels of a Nominal Input20 min
Practice: Computing the Smoothed Weight of Evidence20 min
Question 3.055 min
Practice: Reducing Redundancy by Clustering Variables20 min
Semaine
4

Semaine 4

4 heures pour terminer

Preparing the Input Variables, Part 2

4 heures pour terminer
23 vidéos (Total 92 min), 1 lecture, 12 quiz
23 vidéos
Detecting Nonlinear Relationships4 min
Demo: Performing Variable Screening, Part 15 min
Demo: Performing Variable Screening, Part 24 min
Univariate Binning and Smoothing2 min
Demo: Creating Empirical Logit Plots10 min
Remedies for Nonlinear Relationships2 min
Demo: Accommodating a Nonlinear Relationship, Part 16 min
Demo: Accommodating a Nonlinear Relationship, Part 27 min
Introduction26s
Specifying a Subset Selection Method in PROC LOGISTIC1 min
Best-Subsets Selection54s
Stepwise Selection2 min
Backward Elimination1 min
Scalability of the Subset Selection Methods in PROC LOGISTIC2 min
Detecting Interactions2 min
BIC-based Significance Level2 min
Demo: Detecting Interactions7 min
Demo: Using Backward Elimination to Subset the Variables4 min
Demo: Displaying Odds Ratios for Variables Involved in Interactions3 min
Demo: Creating an Interaction Plot3 min
Demo: Using the Best-Subsets Selection Method3 min
Demo: Using Fit Statistics to Select a Model9 min
1 lecture
Summary of Preparing the Input Variables, Parts 1 and 210 min
12 exercices pour s'entraîner
Question 3.065 min
Practice: Performing Variable Screening20 min
Practice: Creating Empirical Logit Plots20 min
Question 3.075 min
Question 3.085 min
Question 3.095 min
Practice: Using Forward Selection to Detect Interactions20 min
Question 3.105 min
Practice: Using Backward Elimination to Subset the Variables20 min
Question 3.115 min
Practice: Using Fit Statistics to Select a Model20 min
Preparing the Input Variables Review30 min

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Analyste statistique SAS d'entreprise

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