Master Curriculum
Master Curriculum
Structure Overview
| Course Categories | Number of Courses | Credits | |||
|---|---|---|---|---|---|
| Common Core Course (CCC) | 1 | 3 | |||
| Academic Core Courses (ACC) | 4 | 12 | |||
| In-Depth Prodi Course (IDC) | 3 | 9 | |||
| Enrichment Courses (EC) | 1 | 0-3 | |||
| Final Year Projects (FYP)*) | 6 | 14 | |||
| Total Credits | 38 |
Semester 1
| No | Course Code | Course Name | Credits | Course Category | Prerequisite |
|---|---|---|---|---|---|
| 1 | STA1511 | Statistical Analysis | 3(2-1) | ACC | |
| 2 | STA1501 | Statistical Theory | 3(2-1) | ACC | |
| 3 | STA1561 | Statistical Programming | 3(2-1) | ACC | |
| 4 | STA1581 | Data Science | 3(2-1) | ACC | |
| Total Credits | 12 |
Semester 2
| No | Course Code | Course Name | Credits | Course Category | Prerequisite |
|---|---|---|---|---|---|
| 1 | STA1500 | Quantitative Research Methods | 3(2-1) | CCC | |
| 2 | STA169A | Thesis Proposal | 2(0-2) | FYP | |
| Specialization Courses: Statistical Modeling | |||||
| 3 | Choose at least one | ||||
| STA1521 | Design and Analysis of Experiments | 3(2-1) | IDC | ||
| STA1522 | Sampling Methods | 3(2-1) | IDC | ||
| 4 | STA1541 | Analisis Peubah Ganda | 3(2-1) | IDC | |
| Specialization Courses: Predictive Analytics | |||||
| 3 | Choose at least one | ||||
| STA1521 | Design and Analysis of Experiments | 3(2-1) | IDC | ||
| STA1522 | Sampling Methods | 3(2-1) | IDC | ||
| 4 | Choose at least one | ||||
| STA1542 | Time Series Analysis | 3(2-1) | IDC | ||
| STA1543 | Categorical Data Analysis | 3(2-1) | IDC | ||
| Specialization Courses: Big Data Analytics | |||||
| 3 | STA1562 | Statistical Data Management | 3(2-1) | IDC | |
| 4 | STA1582 | Statistical Machine Learning | 3(2-1) | IDC |
Semester 3
| No | Course Code | Course Name | Credits | Course Category | Prerequisite |
|---|---|---|---|---|---|
| 1 | STA1697 | Colloqium | FYP | CCC | |
| Specialization Courses: Statistical Modeling | |||||
| 2 | STA1631 | Generalized Linear Model | 3(2-1) | IDC | |
| Specialization Courses: Predictive Analytics | |||||
| 2 | STA1551 | Classification Modeling | 3(2-1) | IDC | |
| Specialization Courses: Big Data Analytics | |||||
| 2 | STA1563 | Data Exploration and Visualization | 3(2-1) | IDC |
Semester 4
| No | Course Code | Course Name | Credits | Course Category | Prerequisite |
|---|---|---|---|---|---|
| 1 | Choose at least one | ||||
| PPS1692 | National Publication | 2(0-2) | FYP | ||
| PPS1695 | International Publication | 3(0-3) | FYP | ||
| PPS1698 | Publication in International Seminar Proceedings | 2(0-2) | FYP | ||
| 2 | PPS1691 | Thesis Seminar | 1(0-1) | FYP | |
| 3 | STA169B | Thesis | 6(0-6) | FYP | |
| 4 | STA169C | Thesis Examination | 2(0-2) | FYP | |
| Total Credits | 16 |
Capaian Pembelajaran Program Studi
| Category | Code | Learning Outcome |
|---|---|---|
| Sikap | AT | Memiliki kemandirian intelektual dalam berpikir kritis sebagai pembelajar sepanjang hayat. |
| Pengetahuan | K1 | Memiliki pemahaman mendalam mengenai konsep dan teori statistika dan sains data yang mendasari aplikasi analisis data dan pengembangan keilmuan tingkat lanjut. |
| K2 | Memiliki pengetahuan algoritmik mendalam tentang pengelolaan data dan pemrograman yang mendukung analisis statistika/sains data secara lebih customized dan efisien. | |
| K3 | Memiliki pemahaman mendalam tentang teknik pengumpulan data melalui percontohan atau percobaan atau akuisisi data. | |
| K4 | Memiliki pengetahuan yang luas mengenai pemodelan statistika/sains data baik yang bersifat supervised maupun unsupervised learning, serta penyajian yang baik. | |
| Keterampilan | A1 | Memiliki kemampuan merancang proses pengumpulan data dan mengelola implementasinya dalam bentuk survei kompleks atau percobaan tingkat lanjut atau akuisisi data dari berbagai sumber database yang mendukung penyelesaian masalah nyata. |
| A2 | Memiliki kemampuan menyusun strategi analisis data dan mengaplikasikannya menggunakan teknik statistika atau statistical machine learning dengan bantuan komputer. | |
| A3 | Memiliki kemampuan kontekstualisasi hasil analisis dan pemodelan statistika dan sains data sebagai pendukung pengambilan keputusan. | |
| Kompetensi | C1 | Memiliki kemampuan memformulasikan permasalahan nyata ke dalam permasalahan statistika dan sains data sehingga diperoleh solusi yang mampu dipahami oleh pemangku kepentingan sesuai bidang kajian. |
| C2 | Memiliki kemampuan mengevaluasi efektifitas proses pengumpulan data yang relevan dengan pencarian solusi bagi penyelesaian masalah terapan, terutama dalam bidang pertanian tropika dan kemaritiman, baik melalui survei atau percobaan atau pemanfaatan database atau pengumpulan data digital. | |
| C3 | Memiliki kemampuan mengelola tim analisis data yang menggunakan teknik statistika atau machine learning lanjut. | |
| C4 | Memiliki kemampuan mengartikulasikan hasil analisis data dalam bentuk komunikasi yang efektif dengan pemangku kepentingan, baik secara lisan maupun tertulis. |
Program Learning Outcomes
| Category | Code | Learning Outcome |
|---|---|---|
| Attitude | AT | Demonstrate critical thinking and intellectual independence as a lifelong learner. |
| Knowledge | K1 | Master core concepts and theories in statistics and data science that support advanced data analysis and scientific development. |
| K2 | Possess strong algorithmic and programming knowledge for efficient, customized statistical/data analysis. | |
| K3 | Understand key methods of data collection, including sampling, experimentation, and data acquisition. | |
| K4 | Demonstrate broad knowledge of modeling techniques, including supervised and unsupervised learning, and effective data presentation. | |
| Skills | A1 | Design and implement data collection processes for complex surveys, experiments, or multi-source data acquisition. |
| A2 | Develop and execute data analysis strategies using statistical or machine learning techniques and computational tools. | |
| A3 | Interpret and contextualize analysis results to support informed decision-making. | |
| Competence | C1 | Formulate real-world problems into statistical/data science frameworks to deliver stakeholder-relevant solutions. |
| C2 | Evaluate data collection processes to support applied solutions, particularly in tropical agriculture and maritime domains. | |
| C3 | Lead data analysis teams utilizing advanced statistical or machine learning methods. | |
| C4 | Communicate analytical findings effectively to stakeholders, both orally and in writing. |
