Advanced Data Science Techniques in SPSS

Advanced Data Science Techniques in SPSS, available at $59.99, has an average rating of 4.1, with 87 lectures, based on 205 reviews, and has 25370 subscribers.

You will learn about 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 This course is ideal for individuals who are students or PhD candidates or academic researchers or business researchers or University teachers or anyone who is passionate about data analysis and data science It is particularly useful for students or PhD candidates or academic researchers or business researchers or University teachers or anyone who is passionate about data analysis and data science.

Enroll now: Advanced Data Science Techniques in SPSS

Summary

Title: Advanced Data Science Techniques in SPSS

Price: $59.99

Average Rating: 4.1

Number of Lectures: 87

Number of Published Lectures: 87

Number of Curriculum Items: 87

Number of Published Curriculum Objects: 87

Original Price: $69.99

Quality Status: approved

Status: Live

What You Will 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 Should Attend

students

PhD candidates

academic researchers

business researchers

University teachers

anyone who is passionate about data analysis and data science

Target Audiences

students

PhD candidates

academic researchers

business researchers

University teachers

anyone who is passionate about data analysis and data science

Become a Top Performing Data Analyst – 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, let’s see what you are going to learn…

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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Course Curriculum

Chapter 1: Getting Started

Lecture 1: Introduction

Chapter 2: Advanced Linear Regression Techniques

Lecture 1: Introduction to Stepwise Regression

Lecture 2: Our Practical Example

Lecture 3: Executing the Stepwise Regression Method

Lecture 4: Interpreting the Results of the Stepwise Method

Lecture 5: Executing the Forward Selection Regression

Lecture 6: Interpreting the Results of the Forward Selection Method

Lecture 7: Executing the Backward Selection Regression

Lecture 8: Interpreting the Results of the Backward Selection Method

Lecture 9: Comparing Nested Models Using the Remove Method

Lecture 10: Executing the Regression Analysis with the Remove Method

Lecture 11: Interpreting the Results of the Remove Method

Chapter 3: Nonlinear Regression Analysis

Lecture 1: Types of Nonlinear Functions

Lecture 2: An Important Classification of the Nonlinear Relationships

Lecture 3: Performing a Quadratic Regression in SPSS (1)

Lecture 4: Performing a Quadratic Regression in SPSS (2)

Lecture 5: Performing a Cubic Regression in SPSS (1)

Lecture 6: Performing a Cubic Regression in SPSS (2)

Lecture 7: Performing an Inverse Regression in SPSS (1)

Lecture 8: Performing an Inverse Regression in SPSS (2)

Lecture 9: Performing a Nonlinear Regression With an Exponential Relationship

Lecture 10: Performing a Nonlinear Regression With a Logistic Relationship

Chapter 4: K Nearest Neighbor in SPSS

Lecture 1: Introduction to K Nearest Neighbor (KNN)

Lecture 2: Selecting the Optimal Number of Neighbors

Lecture 3: Our Practical Example

Lecture 4: Performing the KNN technique

Lecture 5: Interpreting the results of the KNN analysis

Lecture 6: Finding the Optimal Number of Neighbors with Cross-Validation

Lecture 7: Interpreting the Cross-Validation Results

Lecture 8: Using the KNN Model for Future Predictions

Chapter 5: Introduction to Decision Trees

Lecture 1: What Are Decision Trees?

Lecture 2: Binary Trees (CART)

Lecture 3: Non-Binary Trees (CHAID)

Lecture 4: Advantages and Disadvantages of Decision Trees

Chapter 6: Growing Binary Trees (CART) in SPSS

Lecture 1: Growing a Binary Regression Tree (CART)

Lecture 2: Intepreting a Binary Regression Tree (1)

Lecture 3: Intepreting a Binary Regression Tree (2)

Lecture 4: Computing the R Squared

Lecture 5: Growing a CART Regression Tree with Cross-Validation

Lecture 6: Interpreting the Cross-Validation Results for a Regression Tree

Lecture 7: Growing a CART Classification Tree in SPSS

Lecture 8: Interpreting the CART Classification Tree

Lecture 9: Growing a CART Classification Tree with Cross-Validation

Lecture 10: Interpreting the Cross-Validation Results for a Classification Tree

Lecture 11: Using Binary Trees for Future Predictions

Chapter 7: Growing Non-Binary Trees (CHAID) in SPSS

Lecture 1: Building a CHAID Regression Tree

Lecture 2: Interpreting a CHAID Regression Tree

Lecture 3: Growing a CHAID Regression Tree with Cross-Validation

Lecture 4: Building a CHAID Classification Tree

Lecture 5: Interpreting a CHAID Classification Tree

Lecture 6: Growing a CHAID Classification Tree with Cross-Validation

Lecture 7: Using Non-Binary Trees for Future Predictions

Chapter 8: Introduction to Neural Networks

Lecture 1: The Architecture of an Artificial Neural Network

Lecture 2: What Happens Inside of a Neuron?

Lecture 3: Activation Functions

Lecture 4: Neural Network Learning Process

Chapter 9: Training a Multilayer Perceptron (MLP) in SPSS

Lecture 1: Building a Multilayer Perceptron

Lecture 2: Interpreting the Multilayer Perceptron

Lecture 3: Interpreting the ROC Curve

Lecture 4: Using the Multilayer Perceptron for Future Predictions

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

Lecture 1: Building an RBF Neural Network

Lecture 2: Interpreting the RBF Network

Lecture 3: Using the RBF Network for Future Predictions

Chapter 11: Two-Step Cluster Analysis

Lecture 1: What is Two-Step Clustering?

Lecture 2: Executing the Two-Step Cluster Analysis

Lecture 3: Interpreting the Output of the Two-Step Cluster Analysis (1)

Lecture 4: Interpreting the Output of the Two-Step Cluster Analysis (2)

Lecture 5: Examining the Evaluation Variables

Lecture 6: Using Your Clustering Model for Future Predictions

Chapter 12: Survival Analysis

Lecture 1: What Is the Survival Analysis?

Lecture 2: Introduction to the Kaplan-Meier Method

Lecture 3: Introduction to the Cox Regression

Lecture 4: Our Practical Example

Lecture 5: Executing the Kaplan-Meier Procedure

Lecture 6: Interpreting the Results of the Kaplan-Meier Method (1)

Lecture 7: Interpreting the Results of the Kaplan-Meier Method (2)

Lecture 8: Executing the Cox Regression

Lecture 9: Interpreting the Cox Regression

Chapter 13: Practical Exercises

Lecture 1: Practical Exercises for the Linear Regression

Lecture 2: Practical Exercises for the Nonlinear Regression

Lecture 3: Practical Exercises for the KNN Method

Lecture 4: Practical Exercises for the Regression Trees

Lecture 5: Practical Exercises for the Classification Trees

Lecture 6: Practical Exercises for the Neural Networks

Lecture 7: Practical Exercises for the Cluster Analysis

Lecture 8: Practical Exercises for the Survival Analysis

Chapter 14: Download Your Resources Here

Instructors

Bogdan Anastasiei
University Teacher and Consultant

Rating Distribution

1 stars: 4 votes

2 stars: 4 votes

3 stars: 27 votes

4 stars: 76 votes

5 stars: 94 votes

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