by Jesus Salcedo | Released February 2018 | ISBN: 9781788830348

Your one stop solution to conquering the woes in Statistics, Data Mining, Data Analysis and Data ScienceAbout This VideoStart by building your basic knowledge of statistics, then move on to some classical data mining algorithms such as K-means and AprioriApply statistical and data mining techniques to analyze and interpret results using CHAID, Linear Regression, and Neural NetworksAcquire a wider repertoire of analytical skills to help you make smart decisions for both customers and industriesIn DetailData 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. Show and hide more

1. Chapter 1 : Data Mining and Statistics
   • The Course Overview 00:03:01
   • Comparing and Contrasting Statistics and Data Mining 00:11:20
   • Comparing and Contrasting IBM SPSS Statistics and IBM SPSS Modeler 00:11:38
   • Types of Projects 00:03:58
2. Chapter 2 : Predictive Modeling
   • Predictive Modeling: Purpose, Examples, and Types 00:04:38
   • Characteristics and Examples of Statistical Predictive Models 00:02:12
   • Linear Regression: Purpose, Formulas, and Demonstration 00:10:00
   • Linear Regression: Assumptions 00:05:54
   • Characteristics and Examples of Decision Trees Models 00:02:27
   • CHAID: Purpose and Theory 00:02:50
   • CHAID Demonstration 00:05:45
   • CHAID Interpretation 00:09:39
   • Characteristics and Examples of Machine Learning Models 00:02:23
   • Neural Network: Purpose and Theory 00:04:29
   • Neural Network Demonstration 00:07:30
   • Comparing Models 00:05:45
3. Chapter 3 : Cluster Analysis
   • Cluster Analysis: Purpose Goals, and Applications 00:05:49
   • Cluster Analysis: Basics 00:12:10
   • Cluster Analysis: Models 00:04:12
   • K-Means Demonstration 00:08:35
   • K-Means Interpretation 00:07:52
   • Using Additional Fields to Create a Cluster Profile 00:06:18
4. Chapter 4 : Association Modeling
   • Association Modeling Theory: Examples and Objectives 00:07:25
   • Association Modeling Theory: Basics and Applications 00:07:59
   • Demonstration: Apriori Setup and Options 00:07:59
   • Demonstration: Apriori Rule Interpretation 00:04:51
   • Demonstration: Apriori with Tabular Data 00:06:50
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