Kursus
Bayesian Modeling with RJAGS
- LanjutanTingkat Keterampilan
- 4.9+
- 60
In this course, youll learn how to implement more advanced Bayesian models using RJAGS.
Probabilitas & Statistik
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In this course, youll learn how to implement more advanced Bayesian models using RJAGS.
Probabilitas & Statistik
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Unlock the power of parallel computing in R. Enhance your data analysis skills, speed up computations, and process large datasets effortlessly.
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Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.
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Learn strategies for answering probability questions in R by solving a variety of probability puzzles.
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Learn how to visualize big data in R using ggplot2 and trelliscopejs.
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Learn to create animated graphics and linked views entirely in R with plotly.
Visualisasi Data
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Learn how to identify important drivers of demand, look at seasonal effects, and predict demand for a hierarchy of products from a real world example.
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Predict employee turnover and design retention strategies.
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Use C++ to dramatically boost the performance of your R code.
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Learn how to tune your models hyperparameters to get the best predictive results.
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Master the essential skills of data manipulation in Julia. Learn how to inspect, transform, group, and visualize DataFrames using real-world datasets.
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Master data visualization in Julia. Learn how to make stunning plots while understanding when and how to use them.
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Learn how to predict click-through rates on ads and implement basic machine learning models in Python so that you can see how to better optimize your ads.
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Continue learning with purrr to create robust, clean, and easy to maintain iterative code.
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Learn how to write scalable code for working with big data in R using the bigmemory and iotools packages.
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Build PowerPoint presentations with Microsoft Copilot. Turn documents into slides, generate visuals, and speaker notes.
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Master Microsoft Copilot in Word to write faster, understand documents instantly, and collaborate more effectively.
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Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.
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Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the network
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