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Advanced Data Science Techniques In SPSS Download Now

Advanced Data Science Techniques In SPSS

Advanced Data Science Techniques In SPSS

Hone your SPSS expertise to perfection – grasp probably the most excessive stage knowledge evaluation strategies obtainable within the SPSS program.

What you’ll study

  • Carry out superior linear regression utilizing predictor choice methods
  • Carry out any kind of nonlinear regression evaluation
  • Make predictions utilizing the ok nearest neighbor (KNN) method
  • Use binary (CART) bushes for prediction (each regression and classification bushes)
  • Use non-binary (CHAID) bushes for prediction (each regression and classification bushes)
  • Construct and practice a multilayer perceptron (MLP)
  • Construct and practice a radial foundation funcion (RBF) neural community
  • Carry out a two-way cluster evaluation
  • Run a survival evaluation utilizing the Kaplan-Meier methodology
  • Run a survival evaluation utilizing the Cox regression
  • Validate the predictive methods (KNN, bushes, neural networks) utilizing the validation set method and the cross-validation
  • Save a predictive evaluation mannequin and use it for predictions on future new knowledge
  • SPSS program put in (model 21+)
  • Fundamental SPSS information
  • Fundamental or intermediate statistics information


Grow to be a High Performing Data Analyst – Take This Advanced Data Science Course in SPSS!

Inside just a few days solely you may grasp a number of the most advanced knowledge evaluation methods obtainable within the SPSS program. Even in case you are not knowledgeable mathematician or statistician, you’ll understood these methods completely and can be capable to apply them in sensible, actual life conditions.

These strategies are used day-after-day by knowledge scientists and knowledge miners to make correct predictions utilizing their uncooked knowledge. If you wish to be a excessive expert analyst, you need to know them!

With out additional ado, let’s see what you will study…

  • Stepwise regression evaluation, a method that helps you choose one of the best subset of predictors for a regression evaluation, when you will have an enormous variety of predictors. This manner you may create regression fashions which might be each parsimonious and efficient.
  • Nonlinear regression evaluation. After ending this course, it is possible for you to to suit any nonlinear regression mannequin utilizing SPSS.
  • Ok nearest neighbor, a extremely popular predictive method used largely for classification functions. So you’ll learn to predict the values of a categorical variable with this methodology.
  • Resolution bushes. We’ll method each binary (CART) and non-binary (CHAID) bushes. For every of those two varieties we are going to take into account two instances: the case of response dependent variables (regression bushes) and the case of categorical response variables (classification bushes).
  • Neural networks. Synthetic neural networks are sizzling now, since they’re an appropriate predictive software in lots of conditions. In SPSS we will practice two sorts of neural community: the multilayer perceptron (MLP) and the radial foundation operate (RBF) community. We’re going to research each of them intimately.
  • Two-step cluster evaluation, an efficient grouping process that permits us to determine homogeneous teams in our inhabitants. It’s helpful in very many fields like advertising and marketing analysis, medication (gene analysis, for instance), biology, laptop science, social science and so forth.
  • Survival evaluation. If you need to estimate one of many following: the possible time till a sure occasion occurs, what share of your inhabitants will undergo the occasion or which specific circumstances affect the likelihood that the occasion occurs, than it’s good to apply on of the survival evaluation methodology studied right here: Kaplan-Meier or Cox regression.

For every evaluation method, a brief theoretical introduction is offered, to be able to familiarize the reader with the basic notions and ideas associated to that method. Afterwards, the evaluation is executed on a real-life knowledge set and the output is completely defined.

Furthermore, for some methods (KNN, resolution bushes, neural networks) additionally, you will study:

  • Find out how to validate your mannequin on an impartial knowledge set, utilizing the validation set method or the cross-validation
  • Find out how to save the mannequin and use it for make predictions on new knowledge which may be obtainable sooner or later.

Be a part of straight away and begin constructing refined, in-demand knowledge evaluation expertise in SPSS!

Who this course is for:
  • college students
  • PhD candidates
  • educational researchers
  • enterprise researchers
  • College lecturers
  • anybody who’s captivated with knowledge evaluation and knowledge science

Created by Bogdan Anastasiei
Final up to date 2/2018
English [Auto-generated]

Dimension: 909.55 MB

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