Advanced Statistics and Data Mining for Data Science
Course

Advanced Statistics and Data Mining for Data Science

Packt Admin
Updated Feb 07, 2019

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. Style and Approach: 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. 


Target 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 


Business Outcomes  

  • Start by building your basic knowledge of statistics, then move on to some classical data mining algorithms such as K-means and Apriori
  • Apply statistical and data mining techniques to analyze and interpret results using CHAID, Linear Regression, and Neural Networks
  • Acquire a wider repertoire of analytical skills to help you make smart decisions for both customers and industries
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