| Kode Mata Kuliah | AS3109 / 3 SKS |
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| Penyelenggara | 103 - Astronomi / FMIPA |
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| Kategori | Kuliah |
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| Bahasa Indonesia | English |
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| Nama Mata Kuliah | Statistika dan Data Mining | Statistics and Data Mining |
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| Bahan Kajian | - Komputasi cepat dan dataset masif dalam astronomi
- Eksplorasi analisis data: metode eksplorasi dan kuantifikasi struktur pada distribusi multivarian titik data; Metode analisis komponen dan metode reduksi dimensi data; Regresi serta pencocokan dan seleksi model; Metode klasifikasi berpenuntun (supervised classification); Analisis deret waktu
| - Fast computing and massive datasets in astronomy
- Exploratory data analysis: methods for exploring and quantifying structure in multivariate distributions of data points; Component analysis methods and data dimension reduction methods; Regression and model matching and selection; Guided classification method (supervised classification); Time series analysis
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| Capaian Pembelajaran Mata Kuliah (CPMK) | - Mahasiswa mampu memahami tipe dan sumber data masif astronomi serta mampu mengerjakan strategi komputasi cepat yang diperlukannya
- Mahasiswa mampu menjelaskan secara komprehensif konsep probabilitas, distribusi statistik, dan statistika inferensial (klasik dan Bayesian)
- Mahasiswa mampu melakukan eksplorasi dan kuantifikasi struktur pada distribusi multivarian titik data yang meliputi estimasi kerapatan parametrik dan algoritma menemukan penggugusan (clustering) pada dataset, serta mampu menerapkan komputasi untuk kebutuhan tersebut
- Mahasiswa mampu menerapkan analisis komponen dan reduksi dimensi data, serta mampu menerapkan komputasi untuk kebutuhan tersebut
- Mahasiswa mampu menjelaskan konsep dan metode regresi serta pencocokan dan seleksi model, serta mampu menerapkan komputasi untuk kebutuhan tersebut
- Mahasiswa mampu menjelaskan konsep dan metode klasifikasi berpenuntun (supervised classification), serta mampu menerapkan komputasi untuk kebutuhan tersebut
- Mahasiswa mampu menjelaskan konsep dan metode analisis deret waktu, serta mampu menerapkan komputasi untuk kebutuhan tersebut
| - Students are able to understand the types and sources of massive astronomical data and are able to work on the fast computing strategies they need
- Students are able to comprehensively explain the concepts of probability, statistical distribution, and inferential statistics (classical and Bayesian)
- Students are able to explore and quantify the structure of the multivariate distribution of data points which includes parametric density estimation and algorithms for finding clustering in datasets, and are able to apply computing for these needs
- Students are able to apply component analysis and data dimension reduction, and are able to apply computing for these needs
- Students are able to explain the concepts and methods of regression as well as model matching and selection, and are able to apply computing for these needs
- Students are able to explain the concepts and methods of supervised classification, and are able to apply computing for these needs
- Students are able to explain the concepts and methods of time series analysis, and are able to apply computing for these needs
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| Metode Pembelajaran | Ceramah, praktikum | Lectures, practicum |
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| Modalitas Pembelajaran | Sinkron dan/atau asinkron | Synchronized and/or asynchronous |
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| Jenis Nilai | ABCDE |
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| Metode Penilaian | Kuis, Tugas Individual, Tugas Kelompok, UTS, UAS | Quizzes, indepence tasks, group tasks, Mid-Semester Exam, Final-Semester Exam |
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| Catatan Tambahan | | |
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