Welcome to Cluster Analysis, Association Mining, and Model Evaluation. In this course we will begin with an exploration of cluster analysis and segmentation, and discuss how techniques such as collaborative filtering and association rules mining can be applied. We will also explain how a model can be evaluated for performance, and review the differences in analysis types and when to apply them.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Data Science Fundamentals
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Spezialisierung Data Science FundamentalsUniversity of California, IrvineÃœber diesen Kurs
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Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Kurs 3 von 4 in
Stufe „Mittel“
Ca. 4 Stunden zum Abschließen
Englisch
Untertitel: Englisch
Was Sie lernen werden
Cluster analysis and segmentation
Collaborative filtering and market basket analysis
Applications of classification- and regression-type prediction models
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100Â % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Kurs 3 von 4 in
Stufe „Mittel“
Ca. 4 Stunden zum Abschließen
Englisch
Untertitel: Englisch
von
University of California, Irvine
Since 1965, the University of California, Irvine has combined the strengths of a major research university with the bounty of an incomparable Southern California location. UCI’s unyielding commitment to rigorous academics, cutting-edge research, and leadership and character development makes the campus a driving force for innovation and discovery that serves our local, national and global communities in many ways.
Lehrplan - Was Sie in diesem Kurs lernen werden
1 Stunde zum Abschließen
Cluster Analysis and Segmentation
Welcome to Module 1, Cluster Analysis and Segmentation. In this module we will explore cluster analysis, a popular unsupervised learning algorithm. We will also review the two major styles of cluster analysis, and discuss potential applications to different industries.
1 Stunde zum Abschließen
2 Lektüren
1 Stunde zum Abschließen
Collaborative Filtering, Association Rules Mining (Market Basked Analysis)
Welcome to Module 2, Collaborative Filtering, Association Rules Mining, & Market Basket Analysis. In this module we will begin with an explanation of collaborative filtering and association rules mining, and how these techniques are used to make automatic predictions. We will also take a closer look at the various common applications of market basket analysis.
1 Stunde zum Abschließen
1 Video (Gesamt 3 min), 1 Lektüre, 1 Quiz
1 Stunde zum Abschließen
Classification-Type Prediction Models
Welcome to Module 3, Classification-Type Prediction Models. In this module we will begin with an explanation of how classification-type prediction models are evaluated for performance, and how a confusion matrix can help visualize that performance. We will also discuss the applicability of cluster analysis, and how it can be used to detect rare events such as fraudulent transactions.
1 Stunde zum Abschließen
1 Video (Gesamt 5 min), 2 Lektüren
1 Stunde zum Abschließen
Regression-Type Prediction Models
Welcome to Module 4, Regression-Type Prediction Models. In this module we will review how regression analytics are used for both hypothesis testing and prediction, and how a scatter plot can be leveraged to better understand the relationship between two variables. We will also discuss the differences between correlation analysis and a regression analysis, and a look at simple vs multiple regression.
1 Stunde zum Abschließen
1 Lektüre
Ãœber den Spezialisierung Data Science Fundamentals
This specialization demystifies data science and familiarizes learners with key data science skills, techniques, and concepts. The course begins with foundational concepts such as analytics taxonomy, the Cross-Industry Standard Process for Data Mining, and data diagnostics, and then moves on to compare data science with classical statistical techniques. The course also provides an overview of the most common techniques used in data science, including data analysis, statistical modeling, data engineering, manipulation of data at scale (big data), algorithms for data mining, data quality, remediation and consistency operations.
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