Advanced Statistics and Data Mining for Data Science [Video]
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-
Free ChapterData Mining and Statistics
-
Predictive Modeling
- Predictive Modeling: Purpose, Examples, and Types
- Characteristics and Examples of Statistical Predictive Models
- Linear Regression: Purpose, Formulas, and Demonstration
- Linear Regression: Assumptions
- Characteristics and Examples of Decision Trees Models
- CHAID: Purpose and Theory
- CHAID Demonstration
- CHAID Interpretation
- Characteristics and Examples of Machine Learning Models
- Neural Network: Purpose and Theory
- Neural Network Demonstration
- Comparing Models
-
Cluster Analysis
-
Association Modeling
About this
video
Data Science is an ever-evolving field. Data Science includes techniques and theories extracted from statistics, computer science, and machine learning. This video course will be your companion and ensure that you master various data mining and statistical techniques.
The course starts by comparing and contrasting statistics and data mining and then provides an overview of the various types of projects data scientists usually encounter. You will then learn predictive/classification modeling, which is the most common type of data analysis project. As you move forward on this journey, you will be introduced to the three methods (statistical, decision tree, and machine learning) with which you can perform predictive modeling. Finally, you will explore segmentation modeling to learn the art of cluster analysis. Towards the end of the course, you will work with association modeling, which will allow you to perform market basket analysis.
This course uses SPSS v25, while not the latest version available, it provides relevant and informative content for legacy users of SPSS.
All the resource files are added to the GitHub repository at: https://github.com/PacktPublishing/Advanced-Statistics-and-Data-Mining-for-Data-Science
- Publication date:
- February 2018
- Publisher
- Packt
- Duration
- 2 hours 53 minutes
- ISBN
- 9781788830348
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