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1. Course Detail
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2. Course Curriculum
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3. Certificate
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- Course Details
- Course Curriculum
- Sample Certificate
- Review
- Education
- online course
- Certificate
- Predictive Modeling with SAS Enterprise Miner
Course Curriculums
Are you interested in learning and hands on Predictive Modelling with SAS Enterprise Miner? Log on to our platform, register with us and learn and get expertise on predictive modelling. Educba brings to you an intensive training course online with 10 + hours of HD videos, with 56 + lectures and already enrolled students. The course curriculum and areas covered are hereby described. The data sets used and criteria with variables used are clearly explained. It is important to validate the entire model.
SAS Enterprise Miner Course Cirriculum
The curriculum includes the following topics under study .Your knowledge base will get enriched by us.
PM SAS EM INTRO
which covers the following course details:
- Introduction of SAS enterprise miner
- Selecting a SAS table
- Creating input data node
- Metadata advisor options
- Add more data sources
- Sample statistics
- Trial report
- Properties of cluster node
- Variable selection.
PM SAS EM VARIABLE SELECTION:
- Input variable
- Variables of r square
- Binary target variable
- Variable and effect summary
- Variable selection-variable ID’s
- Variable frequency table
- Variable uploading model comparison
- Run data partition node
- Variable selection
- Understanding transformation of variables
- Updating transformation of variables
3 SAS PM EM COMBINATION:
- Combination-result of regression
- Combination of iteration plot
- Subseries plot
- Creating ensemble diagram
- SAS code
- Decision tree model
- Run and update decision tree model
- Creating score node
- DT-Result of model comparison
- Leaf statistics and tree map
- Interactively trees window
- Result node data partition
- Building decision trees
SAS PM EM NEURAL NETWORK
- Neural network model output
- Model weight history
- Neural network
- ROC chart
- Neural network-SAS code
- Results of Auto neural node
- Run model comparison
- DEX-Variable ID’s
- Score rating overlay
- Runtime Dmine Regression node
- Average square error
SAS PM EM REGRESSION:
- Regression -table effect
- Result of regression model
- Update regression node
- Creating flow diagram
SOME BASIC QUESTIONS:
- How long is the course?
The course is a 56 + lectures with 10 + long hours of course.
- What are the advantages of this course?
The course helps you with a hand on analytics and to become an expertise in data handling and spss platform. You can learn the regression tool usage with details on regression table, result of the regression model and creating flow diagrams etc. This is great for studying and learning .It has helped many to learn relative valuation .For banks p/bv ratio study is a good one .It has helped to build the base for otherstatistical analysis. That’s the reason why people are thankful to Educba for this learning course.
- What are SAS statistics lessons?
A complete overview of ANOVA, to help train and develop the model ,and for the rest look at our course curriculum and visit us at educba.com.
- What is the SAS PM EM NEURAL NETWORK content?
The course content is as Neural network model output
- Model weight history
- Neural network
- ROC chart
- Neural network-SAS code
- Results of Auto neural node
- Run model comparison
- DEX-Variable ID’s
- Score rating overlay
- Runtime Dmine Regression node
- Average square error
With our course being an intensive course of predictive analytics we assure you complete guidance throughout the course. Other related courses are Advanced SAS, Comprehensive data analytics bundle, advanced SDAS with sql, advanced sas programmer course etc. A complete knowledge of r square, t values, p values helps you to enhance your statistical depth of knowledge. Accessing the code and real time understanding how it works helps you to build on your expertise in the tool. User can analyse the means, standard deviations and get the entire look and feel of running the code.
- Why Educba?
Educba is a large platform for learning be it VMware training, excel, database, or analytics if SAS platform. The platform offers intensive online video training on each of the above mentioned courses and enhances the knowledge base of the user. So I would refer and recommend Educba.com.
With the pace at which analytics is advancing companies are looking out for more adhesive platforms to work and analyse data and analyse trends. This brings forth the importance of this course to understand the spss platform t tests, mean and standard deviation and other concepts much more in depth. This brings forth a larger zone of learning for new bussing professionals and students.
Educba’s course on predictive analytics is designed to help new beginners with no prior experience in defect management to learn and unlearn new management skills.It opens up the course to everybody.Be it product development or testing a novice student willing to learn is our prospect.
- How is this course related to defect management?
Even without prior knowledge of defect management, is it possible to take up the course?
The course is designed keeping in view novice learners and that the learner is willing to learn and unlearn things. It aims to make you aware of the benefits of defect management.
- Can you give insights on hypothesis testing course?
eduCBA’s course on Hypothesis testing is a small course for all those interested and having an inclination towards dealing in statistical data. This course shall help one draw conclusions about the population from a sample and making decision in regard to the entire population.
For any query call/miss call-
Mob-+918587999769
Section 1 PM SAS EM INTRO
1 Introduction of SAS Enterprise Miner
2 Select a SAS Table
3 Creating Input Data Node
4 Metadata Advisor Options
5 Add More Data Sources
6 Sample Statistics
7 Trial report
8 Properties of Cluster Node
9 Variable Selction
Section 2 PM SAS EM VARIABLE SELECTION
10 Input Variable
11 Values of R-Square
12 Binary Target Variable
13 Variable and Effect Summary
14 Variable Selection – Variable ID’s
15 Variable Frequency Table
16 Variable S – Updating Model Comparison
17 Run Data Partition Node
18 Variable Selection – Fit Statistics
19 Understanding Transformation of Variables
20 Score Ranking Overlay Res.
21 Update Transformation of Variables
Section 3 SAS PM EM COMBINATION
22 Combination of Regression Model
23 Combination – Result of Regression Node
24 Combination Iteration Plot
25 Subseries Plot
26 Creating Densemble Diagram
27 SAS Code
28 Decision Tree Model
29 Run and Upadate Decision Tree Model
30 Creating Dscore Node
31 DT – Resulf of Model Comparison
32 Leaf Statistics and Tree Map
33 Interactively Decision Trees
34 Result Node Data Partition
35 Interactively Trees Window
36 Building a Decision Trees
Section 4 SAS PM EM NEURAL NETWORK
37 Neural Network Model
38 Neural Network Model Output
39 Model Weight History
40 Neural Network – Final Weight
41 ROC Chart
42 Neural Network -Iteration Plot
43 Neural Network – SAS Code
44 Neural Network – Cumulative Lift
45 Decision Processing
46 Results of Auto Neural Node
47 Run Model Comparison
48 DEX – Variable ID’s
49 Average Square Error
50 Score Rating overlay – Event
51 Run Dmine Regression Node
Section 5 SAS PM EM REGRESSION
52 Regression with Binary Target
53 Regression – Table Effect Plots
54 Result of Regression Model
55 Update Regression Node
56 Creating Flow Diagram
Here is a sample for the course completion certificate which you will receive after complete the course. This certificate is widely accepted across industries and will boost your chances to grab the job opportunities.
Mail us at: [email protected] with below details to receive your certificate:
- Name
- Course Name
- Date of Completion
- Address
Course Certificate:-
Here is a sample for the course completion certificate which you will receive after complete the course. This certificate is widely accepted across industries and will boost your chances to grab the job opportunities.
Mail us at: [email protected] with below details to receive your certificate:
- Name
- Course Name
- Date of Completion
- Address
Course Certificate:-
Section 1 PM SAS EM INTRO
1 Introduction of SAS Enterprise Miner
2 Select a SAS Table
3 Creating Input Data Node
4 Metadata Advisor Options
5 Add More Data Sources
6 Sample Statistics
7 Trial report
8 Properties of Cluster Node
9 Variable Selction
Section 2 PM SAS EM VARIABLE SELECTION
10 Input Variable
11 Values of R-Square
12 Binary Target Variable
13 Variable and Effect Summary
14 Variable Selection – Variable ID’s
15 Variable Frequency Table
16 Variable S – Updating Model Comparison
17 Run Data Partition Node
18 Variable Selection – Fit Statistics
19 Understanding Transformation of Variables
20 Score Ranking Overlay Res.
21 Update Transformation of Variables
Section 3 SAS PM EM COMBINATION
22 Combination of Regression Model
23 Combination – Result of Regression Node
24 Combination Iteration Plot
25 Subseries Plot
26 Creating Densemble Diagram
27 SAS Code
28 Decision Tree Model
29 Run and Upadate Decision Tree Model
30 Creating Dscore Node
31 DT – Resulf of Model Comparison
32 Leaf Statistics and Tree Map
33 Interactively Decision Trees
34 Result Node Data Partition
35 Interactively Trees Window
36 Building a Decision Trees
Section 4 SAS PM EM NEURAL NETWORK
37 Neural Network Model
38 Neural Network Model Output
39 Model Weight History
40 Neural Network – Final Weight
41 ROC Chart
42 Neural Network -Iteration Plot
43 Neural Network – SAS Code
44 Neural Network – Cumulative Lift
45 Decision Processing
46 Results of Auto Neural Node
47 Run Model Comparison
48 DEX – Variable ID’s
49 Average Square Error
50 Score Rating overlay – Event
51 Run Dmine Regression Node
Section 5 SAS PM EM REGRESSION
52 Regression with Binary Target
53 Regression – Table Effect Plots
54 Result of Regression Model
55 Update Regression Node
56 Creating Flow Diagram
