Predictive Analytics & Modeling with SAS

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EDUCBA Bridging the Gap

9:20:42

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1 - Introduction of SAS Enterprise Miner.mp4

11:35

2 - Select a SAS Table.mp4

09:54

3 - Creating Input Data Node.mp4

12:58

4 - Metadata Advisor Options.mp4

08:53

5 - Add More Data Sources.mp4

11:01

6 - Sample Statistics.mp4

10:22

7 - Trial report.mp4

09:32

8 - Properties of Cluster Node.mp4

08:06

9 - Variable Selection.mp4

09:13

10 - Input Variable.mp4

09:38

11 - Input Variable Continues.mp4

09:43

12 - Values of R Square.mp4

09:22

13 - More on Variable Selection.mp4

08:55

14 - Binary Target Variable.mp4

08:41

15 - Variable and Effect Summary.mp4

09:16

16 - Variable Selection Variable IDs.mp4

08:39

17 - Variable Frequency Table.mp4

09:12

18 - Variable S Updating Model Comparison.mp4

08:47

19 - Run Data Partition Node.mp4

08:14

20 - Variable Selection Fit Statistics.mp4

09:22

21 - Understanding Transformation of Variables.mp4

09:37

22 - Score Ranking Overlay Res.mp4

09:08

23 - Update Transformation of Variables.mp4

09:46

24 - Combination of Different Models.mp4

08:44

25 - Properties of Neural Network.mp4

08:33

26 - Analyzing the Output Variable.mp4

12:16

27 - Combination of Regression Model.mp4

07:42

28 - Combination Result of Regression Node.mp4

10:29

29 - Combination Iteration Plot.mp4

10:00

30 - Subseries Plot.mp4

11:55

31 - Creating Densemble Diagram.mp4

09:40

32 - SAS Code.mp4

11:55

33 - Decision Tree Model.mp4

10:06

34 - Run and Upadate Decision Tree Model.mp4

10:10

35 - Creating Dscore Node.mp4

08:37

36 - DT Resulf of Model Comparison.mp4

10:19

37 - Leaf Statistics and Tree Map.mp4

10:16

38 - Interactively Decision Trees.mp4

09:22

39 - Result Node Data Partition.mp4

09:14

40 - Interactively Trees Window.mp4

09:06

41 - Building a Decision Trees.mp4

08:50

42 - Neural Network Model.mp4

10:10

43 - Neural Network Model Output.mp4

09:40

44 - Model Weight History.mp4

12:28

45 - Neural Network Final Weight.mp4

06:08

46 - ROC Chart.mp4

07:41

47 - Neural Network Iteration Plot.mp4

08:45

48 - Neural Network SAS Code.mp4

10:09

49 - Neural Network Cumulative Lift.mp4

06:23

50 - Decision Processing.mp4

06:22

51 - Results of Auto Neural Node.mp4

07:01

52 - Run Model Comparison.mp4

08:00

53 - DEX Variable IDs.mp4

10:48

54 - Average Square Error.mp4

06:14

55 - Score Rating overlay Event.mp4

05:41

56 - Run Dmine Regression Node.mp4

05:53

57 - Regression with Binary Target.mp4

07:59

58 - Regression Table Effect Plots.mp4

07:43

59 - Result of Regression Model.mp4

08:53

60 - Update Regression Node.mp4

08:56

61 - Creating Flow Diagram.mp4

08:40

Description

Learn and get expertise on Predictive Analytics & Modeling with SAS

What You'll Learn?

The course helps you with a hand on analytics and to become an expertise in data handling and sas platform
You can learn the regression tool usage with details on regression table, result of the regression model and creating flow diagrams etc
It has helped to build the base for other statistical analysis
Learning hands on Predictive Modelling with SAS Enterprise Miner

Who is this for?

Students from technical or computer science fields are highly welcome, similarly, those from mathematics or statistics background is highly suitable. Most commonly students have a degree in B. Tech / BCA/ B. Sc./ MCA/ M. Sc/ M. Tech or MBA degree. Entry-level working professionals from the software field, banking, insurance, share market, information technologies who want to migrate to data analysis are also very suitable and they comprise a major chunk of our class size. The predictive modeling course is also suitable for managers and seasoned industry professionals who want to be a consultant or data scientist.

What You Need to Know?

Prior knowledge of Quantitative Methods, MS Office and Data will be useful

More details

Description
Predictive Analytics & Modeling can be understood as the process of creation, test, and validation of a model. It uses concepts from statistics in predicting the outcomes. Predictive Analytics & Modeling contains a different set of methods like machine learning, statistics, artificial intelligence and so on. These models are made up of several predictors, also called attributes that are likely to impact future results. Predictive modeling is currently the most widely used in computer science, information technology, and information services domain.
This Predictive Analytics & Modeling course targets to provide predictive modeling skills as mentioned above to business sectors/domains. Quantitative methods and predictive modeling concepts from this predictive modeling course could be extensively used in many fields to understand the current customer behavior, customer satisfaction, financial market trends, studying effects of medicine in pharma sectors after drugs are developed and administered.
Minitab or SAS and SPSS are among the leading developers in the world towards building statistical analysis software. Across the world, these softwareâ€™s are used by thousands of companies. These are also used by over 10000 universities and colleges for research and teaching. Some major clients of Minitab, for example, consist of Pfizer, Royal Bank of Scotland, Nestle, Boeing, Toshiba, and DuPont.
Many independent studies conducted by companies like Mckinsey, Gartner, and others have predicted that data science, machine learning, and predictive modeling is going to be the biggest jobs of the 21st century and these professionals are going to be rewarded the best for it.
This course covers many tangible skills that students can count on for jobs and career switch. These skills are explained here to help students understand the value of this Predictive Analytics & Modeling course.
Skill to analyze data and see a complex pattern: data understanding and pattern extraction is a key skill for predictive modeling and a successful person in this domain should be able to make sense of data in no time. In this course, you will learn how to do that. You will be taught various types of data distribution, data patterns, and data understanding techniques. These skills will help you lifelong in making better and more intuitive decisions in all fields of work.
Hands-on coding skill: â€“ The Predictive Analytics & Modeling course teaches three tools- Minitab, SAS, and SPSS. For that, this predictive modeling course is quite good. For predictive modeling and machine learning course one needs to be comfortable with coding, and hence having a sharp understanding of practical implementation is very important. This course teaches all these skills so that the student is industry ready and can comfortably work in real-life use cases.
Strong understanding of concepts: â€“ Machine learning concepts such as regression, classification, support vector machines, neural network, ROC curve, and many more concepts are taught which are frequently asked in interviews and which judges a candidateâ€™s understanding of predictive modeling.
Who this course is for:
Students from technical or computer science fields are highly welcome, similarly, those from mathematics or statistics background is highly suitable. Most commonly students have a degree in B. Tech / BCA/ B. Sc./ MCA/ M. Sc/ M. Tech or MBA degree. Entry-level working professionals from the software field, banking, insurance, share market, information technologies who want to migrate to data analysis are also very suitable and they comprise a major chunk of our class size. The predictive modeling course is also suitable for managers and seasoned industry professionals who want to be a consultant or data scientist.

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