Advanced Statistics and Data Mining for Data Science
Video description
Your one stop solution to conquering the woes in Statistics, Data Mining, Data Analysis and Data Science
About This Video
Start by building your basic knowledge of statistics, then move on to some classical data mining algorithms such as K-means and Apriori
Apply statistical and data mining techniques to analyze and interpret results using CHAID, Linear Regression, and Neural Networks
Acquire a wider repertoire of analytical …
Advanced Statistics and Data Mining for Data Science
Video description
Your one stop solution to conquering the woes in Statistics, Data Mining, Data Analysis and Data Science
About This Video
Start by building your basic knowledge of statistics, then move on to some classical data mining algorithms such as K-means and Apriori
Apply statistical and data mining techniques to analyze and interpret results using CHAID, Linear Regression, and Neural Networks
Acquire a wider repertoire of analytical skills to help you make smart decisions for both customers and industries
In Detail
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.
Audience
This course is suitable for developers who want to analyze data, and learn data mining, and statistical techniques in depth. This is an ideal course for those in Data Analytics, Data Management, Business Analytics, Business Intelligence, Information Security, Information Center, Finance, Marketing, and Data Mining; and specifically data developers, data warehousers, data consultants, and statisticians—across all industries and sectors
Comparing and Contrasting Statistics and Data Mining
Comparing and Contrasting IBM SPSS Statistics and IBM SPSS Modeler
Types of Projects
Chapter 2 : 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
Chapter 3 : Cluster Analysis
Cluster Analysis: Purpose Goals, and Applications
Cluster Analysis: Basics
Cluster Analysis: Models
K-Means Demonstration
K-Means Interpretation
Using Additional Fields to Create a Cluster Profile
Chapter 4 : Association Modeling
Association Modeling Theory: Examples and Objectives
Association Modeling Theory: Basics and Applications
Demonstration: Apriori Setup and Options
Demonstration: Apriori Rule Interpretation
Demonstration: Apriori with Tabular Data
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