Video description
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.
What You Will Learn
- Get familiar with advanced statistics and data mining techniques
- Differentiate between the various types of predictive models
- Master linear regression
- Explore the results of a decision tree
- Work with neural networks
- Understand when to perform cluster analysis and when to use association modeling
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
About The Author
Jesus Salcedo: Jesus Salcedo has a PhD in psychometrics from Fordham University. He is an independent statistical consultant and has been using SPSS products for over 20 years. He is a former SPSS Curriculum Team Lead and Senior Education Specialist who has written numerous SPSS training courses and trained thousands of users.
Table of contents
- Chapter 1 : Data Mining and Statistics
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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
- Chapter 4 : Association Modeling
Product information
- Title: Advanced Statistics and Data Mining for Data Science
- Author(s):
- Release date: February 2018
- Publisher(s): Packt Publishing
- ISBN: 9781788830348
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