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100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
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Stufe „Mittel“
Ca. 10 Stunden zum Abschließen
Englisch
Untertitel: Englisch

Kompetenzen, die Sie erwerben

StatisticsBayesian StatisticsBayesian InferenceR Programming

Karriereergebnisse der Lernenden

21%

nahm einen neuen Beruf nach Abschluss dieser Kurse auf

15%

ziehen Sie für Ihren Beruf greifbaren Nutzen aus diesem Kurs
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. 10 Stunden zum Abschließen
Englisch
Untertitel: Englisch

Dozent

von

University of California, Santa Cruz-Logo

University of California, Santa Cruz

Lehrplan - Was Sie in diesem Kurs lernen werden

InhaltsbewertungThumbs Up91%(8,413 Bewertungen)Info
Woche
1

Woche 1

3 Stunden zum Abschließen

Probability and Bayes' Theorem

3 Stunden zum Abschließen
8 Videos (Gesamt 38 min), 4 Lektüren, 5 Quiz
8 Videos
Lesson 1.1 Classical and frequentist probability6m
Lesson 1.2 Bayesian probability and coherence3m
Lesson 2.1 Conditional probability4m
Lesson 2.2 Bayes' theorem6m
Lesson 3.1 Bernoulli and binomial distributions5m
Lesson 3.2 Uniform distribution5m
Lesson 3.3 Exponential and normal distributions2m
4 Lektüren
Module 1 objectives, assignments, and supplementary materials3m
Background for Lesson 110m
Supplementary material for Lesson 23m
Supplementary material for Lesson 320m
5 praktische Übungen
Lesson 116m
Lesson 212m
Lesson 3.120m
Lesson 3.2-3.310m
Module 1 Honors15m
Woche
2

Woche 2

3 Stunden zum Abschließen

Statistical Inference

3 Stunden zum Abschließen
11 Videos (Gesamt 59 min), 5 Lektüren, 4 Quiz
11 Videos
Lesson 4.2 Likelihood function and maximum likelihood7m
Lesson 4.3 Computing the MLE3m
Lesson 4.4 Computing the MLE: examples4m
Introduction to R6m
Plotting the likelihood in R4m
Plotting the likelihood in Excel4m
Lesson 5.1 Inference example: frequentist4m
Lesson 5.2 Inference example: Bayesian6m
Lesson 5.3 Continuous version of Bayes' theorem4m
Lesson 5.4 Posterior intervals7m
5 Lektüren
Module 2 objectives, assignments, and supplementary materials3m
Background for Lesson 410m
Supplementary material for Lesson 45m
Background for Lesson 510m
Supplementary material for Lesson 510m
4 praktische Übungen
Lesson 48m
Lesson 5.1-5.218m
Lesson 5.3-5.416m
Module 2 Honors6m
Woche
3

Woche 3

2 Stunden zum Abschließen

Priors and Models for Discrete Data

2 Stunden zum Abschließen
9 Videos (Gesamt 66 min), 2 Lektüren, 4 Quiz
9 Videos
Lesson 6.2 Prior predictive: binomial example5m
Lesson 6.3 Posterior predictive distribution4m
Lesson 7.1 Bernoulli/binomial likelihood with uniform prior3m
Lesson 7.2 Conjugate priors4m
Lesson 7.3 Posterior mean and effective sample size7m
Data analysis example in R12m
Data analysis example in Excel16m
Lesson 8.1 Poisson data8m
2 Lektüren
Module 3 objectives, assignments, and supplementary materials3m
R and Excel code from example analysis10m
4 praktische Übungen
Lesson 612m
Lesson 715m
Lesson 815m
Module 3 Honors8m
Woche
4

Woche 4

3 Stunden zum Abschließen

Models for Continuous Data

3 Stunden zum Abschließen
9 Videos (Gesamt 69 min), 5 Lektüren, 5 Quiz
9 Videos
Lesson 10.1 Normal likelihood with variance known3m
Lesson 10.2 Normal likelihood with variance unknown3m
Lesson 11.1 Non-informative priors8m
Lesson 11.2 Jeffreys prior3m
Linear regression in R17m
Linear regression in Excel (Analysis ToolPak)13m
Linear regression in Excel (StatPlus by AnalystSoft)14m
Conclusion1m
5 Lektüren
Module 4 objectives, assignments, and supplementary materials3m
Supplementary material for Lesson 1010m
Supplementary material for Lesson 115m
Background for Lesson 1210m
R and Excel code for regression5m
5 praktische Übungen
Lesson 912m
Lesson 1020m
Lesson 1110m
Regression15m
Module 4 Honors6m

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  • You should have exposure to the concepts from a basic statistics class (for example, probability, the Central Limit Theorem, confidence intervals, linear regression) and calculus (integration and differentiation), but it is not expected that you remember how to do all of these items. The course will provide some overview of the statistical concepts, which should be enough to remind you of the necessary details if you've at least seen the concepts previously. On the calculus side, the lectures will include some use of calculus, so it is important that you understand the concept of an integral as finding the area under a curve, or differentiating to find a maximum, but you will not be required to do any integration or differentiation yourself.

  • Data analysis is done using computer software. This course provides the option of Excel or R. Equivalent content is provided for both options. A very brief introduction to R is provided for people who have never used it before, but this is not meant to be a course on R. Learners using Excel are expected to already have basic familiarity of Excel.

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