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Development Data Science

Advanced Data Science Techniques in SPSS

Hone your SPSS skills to perfection - grasp the most high level data analysis methods available in the SPSS program.
Rating: 4.2 out of 54.2 (117 ratings)
18,708 students
Created by Bogdan Anastasiei
Last updated 12/2020
English
English [Auto]
30-Day Money-Back Guarantee

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

Course content

14 sections • 87 lectures • 6h 41m total length

  • Preview05:16

  • Preview05:34
  • Our Practical Example
    02:26
  • Executing the Stepwise Regression Method
    03:04
  • Interpreting the Results of the Stepwise Method
    09:12
  • Executing the Forward Selection Regression
    01:14
  • Interpreting the Results of the Forward Selection Method
    04:51
  • Executing the Backward Selection Regression
    00:53
  • Interpreting the Results of the Backward Selection Method
    04:37
  • Comparing Nested Models Using the Remove Method
    04:46
  • Executing the Regression Analysis with the Remove Method
    03:35
  • Interpreting the Results of the Remove Method
    03:36

  • Preview03:27
  • Preview04:41
  • Performing a Quadratic Regression in SPSS (1)
    06:51
  • Performing a Quadratic Regression in SPSS (2)
    06:55
  • Performing a Cubic Regression in SPSS (1)
    07:02
  • Performing a Cubic Regression in SPSS (2)
    04:11
  • Performing an Inverse Regression in SPSS (1)
    04:22
  • Performing an Inverse Regression in SPSS (2)
    02:54
  • Performing a Nonlinear Regression With an Exponential Relationship
    04:45
  • Performing a Nonlinear Regression With a Logistic Relationship
    05:45

  • Preview03:37
  • Selecting the Optimal Number of Neighbors
    04:13
  • Our Practical Example
    01:48
  • Performing the KNN technique
    05:08
  • Interpreting the results of the KNN analysis
    17:33
  • Finding the Optimal Number of Neighbors with Cross-Validation
    01:55
  • Interpreting the Cross-Validation Results
    01:45
  • Using the KNN Model for Future Predictions
    07:32

  • Preview06:21
  • Binary Trees (CART)
    05:48
  • Non-Binary Trees (CHAID)
    05:19
  • Advantages and Disadvantages of Decision Trees
    01:23

  • Growing a Binary Regression Tree (CART)
    06:11
  • Intepreting a Binary Regression Tree (1)
    09:10
  • Intepreting a Binary Regression Tree (2)
    04:25
  • Computing the R Squared
    02:57
  • Growing a CART Regression Tree with Cross-Validation
    02:07
  • Interpreting the Cross-Validation Results for a Regression Tree
    02:47
  • Growing a CART Classification Tree in SPSS
    05:18
  • Interpreting the CART Classification Tree
    13:01
  • Growing a CART Classification Tree with Cross-Validation
    02:13
  • Interpreting the Cross-Validation Results for a Classification Tree
    03:59
  • Using Binary Trees for Future Predictions
    11:51

  • Building a CHAID Regression Tree
    03:21
  • Interpreting a CHAID Regression Tree
    07:49
  • Growing a CHAID Regression Tree with Cross-Validation
    03:45
  • Building a CHAID Classification Tree
    04:33
  • Interpreting a CHAID Classification Tree
    08:02
  • Growing a CHAID Classification Tree with Cross-Validation
    04:33
  • Using Non-Binary Trees for Future Predictions
    07:06

  • Preview03:48
  • Preview03:25
  • Activation Functions
    03:40
  • Neural Network Learning Process
    03:39

  • Building a Multilayer Perceptron
    06:42
  • Interpreting the Multilayer Perceptron
    09:28
  • Interpreting the ROC Curve
    04:18
  • Using the Multilayer Perceptron for Future Predictions
    04:23

  • Building an RBF Neural Network
    05:08
  • Interpreting the RBF Network
    05:38
  • Using the RBF Network for Future Predictions
    03:08

Requirements

  • SPSS program installed (version 21+)
  • Basic SPSS knowledge
  • Basic or intermediate statistics knowledge

Description

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!

 

 

Who this course is for:

  • students
  • PhD candidates
  • academic researchers
  • business researchers
  • University teachers
  • anyone who is passionate about data analysis and data science

Instructor

Bogdan Anastasiei
University Teacher and Consultant
Bogdan Anastasiei
  • 4.4 Instructor Rating
  • 5,527 Reviews
  • 195,915 Students
  • 12 Courses

      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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