كلية الهندسة - جامعة عين شمس, الرئيسية

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Data Mining and Business Intelligence

What Will Learn?

  • Course Aims
    By the end of the course the students will be able to: • Understand data mining principles and techniques • Develop and apply critical thinking, problem-solving, and decision-making skills. • Categorize and carefully differentiate between situations for applying different data mining techniques: mining frequent pattern, association, correlation, classification, prediction, and cluster analysis. • Design and implement systems for data mining. • Evaluate the performance of different data mining algorithms. • Propose data mining solutions for different applications. • Understand text mining with an application to the Life Sciences.
  • Course Goals
  • Decent Work and Economic Growth
  • Industry, Innovation and Infrastructure
  • Sustainable Cities and Communities

Requirements

PHM113 AND CSE244

Description

  • English Description

    Definitions, Data mining process, Knowledge discovery in databases. Data pre-processing: Data cleaning, data integration, Data reduction, Data transformation, Data discretization. Data warehousing. Mining frequent patterns, Association rules, Correlation. Classification: k-nearest neighbors, multiple linear regression, logistic regression, Decision tree, Bayes classification, Rule-based classification, Model evaluation and selection, Support Vector Machines (SVMs), Anomaly detection. Cluster analysis: Partition methods, Hierarchical methods, Density methods. Outlier detection: Statistical methods, Proximity-based methods. Web mining: Text and Web-page pre-processing, Inverted index, Latent semantic indexing Web search, Web meta-search, Social network analysis, Web crawling. Business intelligence. Data mining tools. Applications of data mining to various application domains. Data mining case studies.
  • Arabic Description

    Definitions, Data mining process, Knowledge discovery in databases. Data pre-processing: Data cleaning, data integration, Data reduction, Data transformation, Data discretization. Data warehousing. Mining frequent patterns, Association rules, Correlation. Classification: k-nearest neighbors, multiple linear regression, logistic regression, Decision tree, Bayes classification, Rule-based classification, Model evaluation and selection, Support Vector Machines (SVMs), Anomaly detection. Cluster analysis: Partition methods, Hierarchical methods, Density methods. Outlier detection: Statistical methods, Proximity-based methods. Web mining: Text and Web-page pre-processing, Inverted index, Latent semantic indexing Web search, Web meta-search, Social network analysis, Web crawling. Business intelligence. Data mining tools. Applications of data mining to various application domains. Data mining case studies.
  • قسم
    هندسة الحاسبات والنظم
  • الساعات المعتمدة
    3
  • الدرجات
    الإجمالي ( 100 ) = نصف العام (25) + tr.Major Assessment (30 = tr.Industry 0% , tr.Project 15% , tr.Self_learning 5% , tr.Seminar 15% ) + tr.Minor Assessment (5) + درجة الامتحان (40)
  • الساعات
    ساعات المحاضرة: 2, ساعات التعليم: 2, ساعات المعمل: 0
  • Required SWL
    125
  • Equivalent ECTS
    5
  • Main Textbooks books
  • • Data Mining: Concepts and Techniques, Han et al., Elsevier, 3rd edition, 2012, ISBN 978-0-12-381479-1.
  • • Introduction to Data Mining, Tan et al., Addison-Wesley, 2015, ISBN 978-0133128901.
  • Recommended books
  • • Data Mining: Practical Machine Learning Tools and Techniques , Ian H. Witten, Eibe Frank, and Mark A. Hall. (3rd ed.). Morgan Kaufmann, 2011. ISBN 978-0-12-374856-0
  • • Data mining for business intelligence: concepts, techniques, and applications in Microsoft Office Excel with XLMiner / Galit Shmueli, Nitin R. Patel, Peter C.Bruce. – 2nd ed, 2015
  • • Python Data Science Handbook, https://jakevdp.github.io/PythonDataScienceHandbook/