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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.
This Course Includes
udemy
4.1 (204 reviews )
6.5 total hours 
english
Online - Self Paced
course
Udemy
About Advanced Data Science Techniques in SPSS
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…
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:
Join right away and start building sophisticated, in-demand data analysis skills in SPSS!
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.