Über diesen Kurs

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Stufe „Mittel“
Ca. 24 Stunden zum Abschließen
Englisch
Untertitel: Englisch

Kompetenzen, die Sie erwerben

Predictive AnalyticsDecision-Making SoftwareGeodemographic SegmentationValidated Learning
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Stufe „Mittel“
Ca. 24 Stunden zum Abschließen
Englisch
Untertitel: Englisch

von

University of Illinois at Urbana-Champaign-Logo

University of Illinois at Urbana-Champaign

Beginnen Sie damit, auf Ihren Master-Abschluss hinzuarbeiten.

Dieses Kurs ist Teil des reinen Onlineabschlusses Master of Science in Accountancy (iMSA) von University of Illinois at Urbana-Champaign. Wenn Sie in das komplette Programm aufgenommen werden, werden Ihre Kurse auf Ihren Abschluss angerechnet.

Lehrplan - Was Sie in diesem Kurs lernen werden

Woche
1

Woche 1

9 Stunden zum Abschließen

Module 0: Get Ready & Module 1: Drowning in Data, Starving for Knowledge

9 Stunden zum Abschließen
13 Videos (Gesamt 104 min), 11 Lektüren, 4 Quiz
13 Videos
Meet Professor Sridhar Seshadri1m
Rattle Installation Guidelines for Windows11m
R and Rattle Installation Instructions for Mac OS14m
Overview of Rattle7m
Lecture 1-1: Introduction to Clustering11m
Lecture 1-2: Applications of Clustering7m
Lecture 1-3: How to Cluster10m
Lecture 1-4: Introduction to K Means8m
Lecture 1-5: Hierarchical (Agglomerative) Clustering8m
Lecture 1-6: Measuring Similarity Between Clusters10m
Lecture 1-7: Real World Clustering Example6m
Lecture 1-8: Clustering Practice and Summary3m
11 Lektüren
Syllabus30m
About the Discussion Forums10m
Glossary10m
Brand Descriptions10m
Update Your Profile10m
Module 0 Agenda5m
Rattle Tutorials (Interface, Windows, Mac)30m
Frequent Asked Questions10m
Module 1 Overview20m
Module 1 Readings, Data Sets, and Slides1h 30m
Module 1 Peer Review Assignment Answer Key10m
3 praktische Übungen
Orientation Quiz30m
Module 1 Practice Problems10m
Module 1 Graded Quiz30m
Woche
2

Woche 2

5 Stunden zum Abschließen

Module 2: Decision Trees

5 Stunden zum Abschließen
7 Videos (Gesamt 65 min), 3 Lektüren, 3 Quiz
7 Videos
Lecture 2-2: Model Complexity7m
Lecture 2-3: Rule Based Classifiers9m
Lecture 2-4: Entropy and Decision Trees14m
Lecture 2-5: Classification Tree Example7m
Lecture 2-6: Regression Tree Example8m
Lecture 2-7: Introduction to Forests and Spam Filter Exercise9m
3 Lektüren
Module 2 Overview20m
Module 2 Readings, Data Sets, and Slides30m
Module 2 Peer Review Assignment Answer Key10m
2 praktische Übungen
Module 2 Practice Problems30m
Module 2 Graded Quiz30m
Woche
3

Woche 3

5 Stunden zum Abschließen

Module 3: Rules, Rules, and More Rules

5 Stunden zum Abschließen
8 Videos (Gesamt 65 min), 3 Lektüren, 3 Quiz
8 Videos
Lecture 3-2: K-Nearest Neighbor9m
Lecture 3-3: K-Nearest Neighbor Classifier3m
Lecture 3-4: Selecting the Best K in Rstudio12m
Lecture 3-5: Bayes' Rule7m
Lecture 3-6: The Naïve Bayes Trick13m
Lecture 3-7: Employee Attrition Example5m
Lecture 3-8: Employee Attrition Example in Rstudio, Exercise, and Summary9m
3 Lektüren
Module 3 Overview20m
Module 3 Readings, Data Sets, and Slides30m
Module 3 Peer Review Assignment Answer Key10m
2 praktische Übungen
Module 3 Practice Problems10m
Module 3 Graded Quiz30m
Woche
4

Woche 4

5 Stunden zum Abschließen

Module 4: Model Performance and Recommendation Systems

5 Stunden zum Abschließen
8 Videos (Gesamt 68 min), 3 Lektüren, 3 Quiz
8 Videos
Lecture 4-2: Classification Tree Example11m
Lecture 4-3: True and False Negatives8m
Lecture 4-4: Clock Example Exercise2m
Lecture 4-5: Making Recommendations13m
Lecture 4-6: Association Rule Mining6m
Lecture 4-7: Collaborative Filtering7m
Lecture 4-8: Recommendation Example in Rstudio and Summary12m
3 Lektüren
Module 4 Overview20m
Module 4 Readings, Data Sets, and Slides1h
Module 4 Peer Review Assignment Answer Key10m
2 praktische Übungen
Module 4 Practice Problems10m
Module 4 Graded Quiz30m

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