Advanced Data Science Techniques in SPSS

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Bogdan Anastasiei

6:41:18

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1. Getting Started

1. Introduction.mp4

05:16

2. Advanced Linear Regression Techniques

1. Introduction to Stepwise Regression.mp4

05:34

2. Our Practical Example.mp4

02:26

3. Executing the Stepwise Regression Method.mp4

03:04

4. Interpreting the Results of the Stepwise Method.mp4

09:12

5. Executing the Forward Selection Regression.mp4

01:14

6. Interpreting the Results of the Forward Selection Method.mp4

04:51

7. Executing the Backward Selection Regression.mp4

00:53

8. Interpreting the Results of the Backward Selection Method.mp4

04:37

9. Comparing Nested Models Using the Remove Method.mp4

04:46

10. Executing the Regression Analysis with the Remove Method.mp4

03:35

11. Interpreting the Results of the Remove Method.mp4

03:36

3. Nonlinear Regression Analysis

1. Types of Nonlinear Functions.mp4

03:27

2. An Important Classification of the Nonlinear Relationships.mp4

04:41

3. Performing a Quadratic Regression in SPSS (1).mp4

06:52

4. Performing a Quadratic Regression in SPSS (2).mp4

06:55

5. Performing a Cubic Regression in SPSS (1).mp4

07:02

6. Performing a Cubic Regression in SPSS (2).mp4

04:11

7. Performing an Inverse Regression in SPSS (1).mp4

04:22

8. Performing an Inverse Regression in SPSS (2).mp4

02:54

9. Performing a Nonlinear Regression With an Exponential Relationship.mp4

04:45

10. Performing a Nonlinear Regression With a Logistic Relationship.mp4

05:45

4. K Nearest Neighbor in SPSS

1. Introduction to K Nearest Neighbor (KNN).mp4

03:37

2. Selecting the Optimal Number of Neighbors.mp4

04:13

3. Our Practical Example.mp4

01:48

4. Performing the KNN technique.mp4

05:08

5. Interpreting the results of the KNN analysis.mp4

17:33

6. Finding the Optimal Number of Neighbors with Cross-Validation.mp4

01:56

7. Interpreting the Cross-Validation Results.mp4

01:45

8. Using the KNN Model for Future Predictions.mp4

07:32

5. Introduction to Decision Trees

1. What Are Decision Trees.mp4

06:21

2. Binary Trees (CART).mp4

05:48

3. Non-Binary Trees (CHAID).mp4

05:19

4. Advantages and Disadvantages of Decision Trees.mp4

01:23

6. Growing Binary Trees (CART) in SPSS

1. Growing a Binary Regression Tree (CART).mp4

06:11

2. Intepreting a Binary Regression Tree (1).mp4

09:10

3. Intepreting a Binary Regression Tree (2).mp4

04:25

4. Computing the R Squared.mp4

02:57

5. Growing a CART Regression Tree with Cross-Validation.mp4

02:07

6. Interpreting the Cross-Validation Results for a Regression Tree.mp4

02:47

7. Growing a CART Classification Tree in SPSS.mp4

05:18

8. Interpreting the CART Classification Tree.mp4

13:01

9. Growing a CART Classification Tree with Cross-Validation.mp4

02:14

10. Interpreting the Cross-Validation Results for a Classification Tree.mp4

03:59

11. Using Binary Trees for Future Predictions.mp4

11:51

7. Growing Non-Binary Trees (CHAID) in SPSS

1. Building a CHAID Regression Tree.mp4

03:21

2. Interpreting a CHAID Regression Tree.mp4

07:49

3. Growing a CHAID Regression Tree with Cross-Validation.mp4

03:45

4. Building a CHAID Classification Tree.mp4

04:33

5. Interpreting a CHAID Classification Tree.mp4

08:02

6. Growing a CHAID Classification Tree with Cross-Validation.mp4

04:33

7. Using Non-Binary Trees for Future Predictions.mp4

07:06

8. Introduction to Neural Networks

1. The Architecture of an Artificial Neural Network.mp4

03:48

2. What Happens Inside of a Neuron.mp4

03:25

3. Activation Functions.mp4

03:40

4. Neural Network Learning Process.mp4

03:39

9. Training a Multilayer Perceptron (MLP) in SPSS

1. Building a Multilayer Perceptron.mp4

06:42

2. Interpreting the Multilayer Perceptron.mp4

09:28

3. Interpreting the ROC Curve.mp4

04:18

4. Using the Multilayer Perceptron for Future Predictions.mp4

04:23

10. Training a Radial Basis Function (RBF) Neural Network in SPSS

1. Building an RBF Neural Network.mp4

05:08

2. Interpreting the RBF Network.mp4

05:38

3. Using the RBF Network for Future Predictions.mp4

03:08

11. Two-Step Cluster Analysis

1. What is Two-Step Clustering.mp4

04:23

2. Executing the Two-Step Cluster Analysis.mp4

02:30

3. Interpreting the Output of the Two-Step Cluster Analysis (1).mp4

11:16

4. Interpreting the Output of the Two-Step Cluster Analysis (2).mp4

16:46

5. Examining the Evaluation Variables.mp4

05:15

6. Using Your Clustering Model for Future Predictions.mp4

04:02

12. Survival Analysis

1. What Is the Survival Analysis.mp4

05:43

2. Introduction to the Kaplan-Meier Method.mp4

02:09

3. Introduction to the Cox Regression.mp4

04:27

4. Our Practical Example.mp4

03:38

5. Executing the Kaplan-Meier Procedure.mp4

03:25

6. Interpreting the Results of the Kaplan-Meier Method (1).mp4

04:44

7. Interpreting the Results of the Kaplan-Meier Method (2).mp4

02:44

8. Executing the Cox Regression.mp4

05:57

9. Interpreting the Cox Regression.mp4

05:32

13. Practical Exercises

1.1 Practice 01 linear regression.pdf

1. Practical Exercises for the Linear Regression.html

2.1 Practice 02 nonlinear regression.pdf

2. Practical Exercises for the Nonlinear Regression.html

3.1 Practice 03 KNN.pdf

3. Practical Exercises for the KNN Method.html

4.1 Practice 04 regression trees.pdf

4. Practical Exercises for the Regression Trees.html

5.1 Practice 05 classification trees.pdf

5. Practical Exercises for the Classification Trees.html

6.1 Practice 06 neural networks.pdf

6. Practical Exercises for the Neural Networks.html

7.1 Practice 07 cluster.pdf

7. Practical Exercises for the Cluster Analysis.html

8.1 Practice 08 survival.pdf

8. Practical Exercises for the Survival Analysis.html

14. Download Your Resources Here

1. Download Links.html

Description

Hone your SPSS skills to perfection - grasp the most high level data analysis methods available in the SPSS program.

What You'll Learn?

Perform advanced linear regression using predictor selection techniques
Perform any type of nonlinear regression analysis
Make predictions using the k nearest neighbor (KNN) technique
Use binary (CART) trees for prediction (both regression and classification trees)
Use non-binary (CHAID) trees for prediction (both regression and classification trees)
Build and train a multilayer perceptron (MLP)
Build and train a radial basis funcion (RBF) neural network
Perform a two-way cluster analysis
Run a survival analysis using the Kaplan-Meier method
Run a survival analysis using the Cox regression
Validate the predictive techniques (KNN, trees, neural networks) using the validation set approach and the cross-validation
Save a predictive analysis model and use it for predictions on future new data

Who is this for?

students

PhD candidates

academic researchers

business researchers

University teachers

anyone who is passionate about data analysis and data science

What You Need to Know?

SPSS program installed (version 21+)

Basic SPSS knowledge

Basic or intermediate statistics knowledge

More details

Description
Become a Top Performing Data Analyst â€“ Take This Advanced Data Science Course in SPSS!

Within a few days only you can master some of the most complex data analysis techniques available in the SPSS program. Even if you are not a professional mathematician or statistician, you will understood these techniques perfectly and will be able to apply them in practical, real life situations.

These methods are used every day by data scientists and data miners to make accurate predictions using their raw data. If you want to be a high skilled analyst, you must know them!

Without further ado, letâ€™s see what you are going to learnâ€¦

Stepwise regression analysis, a technique that helps you select the best subset of predictors for a regression analysis, when you have a big number of predictors. This way you can create regression models that are both parsimonious and effective.
Nonlinear regression analysis. After finishing this course, you will be able to fit any nonlinear regression model using SPSS.
K nearest neighbor, a very popular predictive technique used mostly for classification purposes. So you will learn how to predict the values of a categorical variable with this method.
Decision trees. We will approach both binary (CART) and non-binary (CHAID) trees. For each of these two types we will consider two cases: the case of response dependent variables (regression trees) and the case of categorical response variables (classification trees).
Neural networks. Artificial neural networks are hot now, since they are a suitable predictive tool in many situations. In SPSS we can train two types of neural network: the multilayer perceptron (MLP) and the radial basis function (RBF) network. We are going to study both of them in detail.
Two-step cluster analysis, an effective grouping procedure that allows us to identify homogeneous groups in our population. It is useful in very many fields like marketing research, medicine (gene research, for example), biology, computer science, social science etc.
Survival analysis. If you have to estimate one of the following: the probable time until a certain event happens, what percentage of your population will suffer the event or which particular circumstances influence the probability that the event happens, than you need to apply on of the survival analysis method studied here: Kaplan-Meier or Cox regression.

For each analysis technique, a short theoretical introduction is provided, in order to familiarize the reader with the fundamental notions and concepts related to that technique. Afterwards, the analysis is executed on a real-life data set and the output is thoroughly explained.

Moreover, for some techniques (KNN, decision trees, neural networks) you will also learn:

How to validate your model on an independent data set, using the validation set approach or the cross-validation
How to save the model and use it for make predictions on new data that may be available in the future.

Join right away and start building sophisticated, in-demand data analysis skills in SPSS!

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Who this course is for:
students
PhD candidates
academic researchers
business researchers
University teachers
anyone who is passionate about data analysis and data science

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My name is Bogdan Anastasiei and I am an assistant professor at the University of Iasi, Romania, Faculty of Economics and Business Administration. I teach Internet marketing and quantitative methods for business. I am also a business consultant. I have run quantitative risk analyses and feasibility studies for various local businesses and been implied in academic projects on risk analysis and marketing analysis. I have also written courses and articles on Internet marketing and online communication techniques. I have 24 years experience in teaching and about 15 years experience in business consulting.

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