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Advanced Statistics and Data Mining for Data Science
Rating: 4.2 out of 5(60 ratings)
364 students

Advanced Statistics and Data Mining for Data Science

Your one stop solution to conquering the woes in Statistics, Data Mining, Data Analysis and Data Science
Created byPackt Publishing
Last updated 3/2018
English

What you'll 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

Course content

4 sections • 27 lectures • 2h 53m total length

  • The Course Overview3:01
    This video will give you an overview about the course.
  • Comparing and Contrasting Statistics and Data Mining11:20
    The aim of this video is to study the similarities and differences between data mining and statistics.
  • Comparing and Contrasting IBM SPSS Statistics and IBM SPSS Modeler11:38

    This aim of this video is to discuss the similarities and differences between the two software packages.

  • Types of Projects3:58

    This aim of this video is to discuss the three different types of projects that data scientists likely encounter.

Requirements

  • 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.

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.

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.

Who this course is 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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