What will you learn in this course?

Identify key data mining techniques for data science applications, including classification, clustering, and association rule mining
Apply data preprocessing and feature selection methods to improve data quality and analysis accuracy in data mining projects
Utilize popular data mining tools and software to extract meaningful insights from large datasets in real-world scenarios

Design and implement data mining workflows to uncover patterns, trends, and relationships for business decision-making
Evaluate the effectiveness of data mining models using metrics like accuracy, precision, and recall in data science projects
Integrate data mining results into data-driven strategies to enhance predictive analytics and business intelligence

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Lessons | 7

00:03:38

Basic Statistic Data Mining for Data Science The Course Overview | packtpub com

00:05:13

Basic Statistic Data Mining for Data Science Reason for Summariz Individual Variable|packtpub com

00:08:21

Basic Statistic Data Mining for Data Science Hypothesis Testing and Probability | packtpub com

00:14:05

Basic Statistic Data Mining for Data Sci Chi square Test Theory and Assumption|packtpub com

00:09:52

Basic Statistic Data Mining for Data Sci Independnt Sample T Test Theory Assumptn|packtpub com

00:09:01

Basic Statistic Data Mining for Data Science One way ANOVA Theory Assumption|packtpub com

00:10:25

Basic Statistic Data Mining for Data Sci Pearson Corelatn Coeficient Theory Asumptn|packtpub com

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