Advanced Statistics and Data Mining for Data Science

Advanced Statistics and Data Mining for Data Science, available at $34.99, has an average rating of 4.3, with 27 lectures, based on 54 reviews, and has 340 subscribers.

You will learn about 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 This course is ideal for individuals who are This course is suitable for developers who want to analyze data, and learn data mining, and statistical techniques in depth. It is particularly useful for This course is suitable for developers who want to analyze data, and learn data mining, and statistical techniques in depth.

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Summary

Title: Advanced Statistics and Data Mining for Data Science

Price: $34.99

Average Rating: 4.3

Number of Lectures: 27

Number of Published Lectures: 27

Number of Curriculum Items: 27

Number of Published Curriculum Objects: 27

Original Price: $109.99

Quality Status: approved

Status: Live

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

Who Should Attend

This course is suitable for developers who want to analyze data, and learn data mining, and statistical techniques in depth.

Target Audiences

This course is suitable for developers who want to analyze data, and learn data mining, and statistical techniques in depth.

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.

About the Author:

Jesus Salcedo has a Ph.D. in Psychometrics from Fordham University. He is an independent statistical and data mining 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.

Course Curriculum

Chapter 1: Data Mining and Statistics

Lecture 1: The Course Overview

Lecture 2: Comparing and Contrasting Statistics and Data Mining

Lecture 3: Comparing and Contrasting IBM SPSS Statistics and IBM SPSS Modeler

Lecture 4: Types of Projects

Chapter 2: Predictive Modeling

Lecture 1: Predictive Modeling: Purpose, Examples, and Types

Lecture 2: Characteristics and Examples of Statistical Predictive Models

Lecture 3: Linear Regression: Purpose, Formulas, and Demonstration

Lecture 4: Linear Regression: Assumptions

Lecture 5: Characteristics and Examples of Decision Trees Models

Lecture 6: CHAID: Purpose and Theory

Lecture 7: CHAID Demonstration

Lecture 8: CHAID Interpretation

Lecture 9: Characteristics and Examples of Machine Learning Models

Lecture 10: Neural Network: Purpose and Theory

Lecture 11: Neural Network Demonstration

Lecture 12: Comparing Models

Chapter 3: Cluster Analysis

Lecture 1: Cluster Analysis: Purpose Goals, and Applications

Lecture 2: Cluster Analysis: Basics

Lecture 3: Cluster Analysis: Models

Lecture 4: K-Means Demonstration

Lecture 5: K-Means Interpretation

Lecture 6: Using Additional Fields to Create a Cluster Profile

Chapter 4: Association Modeling

Lecture 1: Association Modeling Theory: Examples and Objectives

Lecture 2: Association Modeling Theory: Basics and Applications

Lecture 3: Demonstration: Apriori Setup and Options

Lecture 4: Demonstration: Apriori Rule Interpretation

Lecture 5: Demonstration: Apriori with Tabular Data

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 1 votes

2 stars: 3 votes

3 stars: 9 votes

4 stars: 25 votes

5 stars: 16 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!