English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 53m | 839 MB
Your one stop solution to conquering the woes in Statistics, Data Mining, Data Analysis and Data Science
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 application-oriented course takes a practical approach and discusses situations in which you would use each statistical and data mining technique, the assumptions made by the method, how to set up the analysis, and how to interpret the results. No proofs will be derived, but rather the focus will be on the practical aspects of data analysis in answering research questions.
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
Table of Contents
Data Mining and Statistics
1 The Course Overview
2 Comparing and Contrasting Statistics and Data Mining
3 Comparing and Contrasting IBM SPSS Statistics and IBM SPSS Modeler
4 Types of Projects
Predictive Modeling
5 Predictive Modeling – Purpose, Examples, and Types
6 Characteristics and Examples of Statistical Predictive Models
7 Linear Regression – Purpose, Formulas, and Demonstration
8 Linear Regression – Assumptions
9 Characteristics and Examples of Decision Trees Models
10 CHAID – Purpose and Theory
11 CHAID Demonstration
12 CHAID Interpretation
13 Characteristics and Examples of Machine Learning Models
14 Neural Network – Purpose and Theory
15 Neural Network Demonstration
16 Comparing Models
Cluster Analysis
17 Cluster Analysis – Purpose Goals, and Applications
18 Cluster Analysis – Basics
19 Cluster Analysis – Models
20 K-Means Demonstration
21 K-Means Interpretation
22 Using Additional Fields to Create a Cluster Profile
Association Modeling
23 Association Modeling Theory – Examples and Objectives
24 Association Modeling Theory – Basics and Applications
25 Demonstration – Apriori Setup and Options
26 Demonstration – Apriori Rule Interpretation
27 Demonstration – Apriori with Tabular Data
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