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  • Kode Mata Kuliah: ADM
  • Pendahuluan:
    ADM membahas berbagai teknik untuk menemukan informasi penting (insight) dari data kategorik maupun numerik dengan menggunakan advanced analystics, yaitu berbagai model Statistik tingkat lanjut seperti model regresi, klasifikasi, dan pengelompokan.
  • Objective:
    tau-learners mampu untuk:
    – Melakukan exploratory data analysis (EDA) sederhana untuk menyiapkan data (preprocessing), menyusun hipotesis (dugaan awal), cek asumsi dasar model, dan analisa dasar.
    – Membuat model rekomendasi sederhana
    – Melakukan prediksi numerik (regresi) dan kategorik  (klasifikasi) dengan berbagai model dasar.
    – Melakukan analisa unsupervised baik clustering maupun reduksi dimensi
    – Memberikan evaluasi dan interpretasi yang tepat pada berbagai model dua poin diatas
  • Prasyarat
    – Calculus (turunan), Linear algebra (matrix operation, eigen, metric, etc), Basic Geometry, Discrete Mathematics.
    – Statistika Dasar, Model Linear,
    – Highly recommended: Multivariate Data Analysis*, Time Series Analysis*, *Spatial Data Analysis
  • Forum Diskusi:
    https://tau-data.id/forums/forum/data-mining/
  • Evaluasi
    – Online quiz pada setiap materi kuliah
    – Offline Exams (optional for university partners)
    – Capstone Data Mining Project
  • Tools/Software:
    – Python: Jupyter Notebook (Anaconda/WinPython) atau Google Colaboratory
    – Python Modules: Scikit-learn, imbalance learning, networkX, etc.
    – Voyant Tools
    – Gephy
  • Silabus
    1. Pendahuluan dan Proses Data Mining
    2. Data Mining Overview: Tipe Data dan Teknik Data Mining
    3. Pengenalan Exploratory Data Analysis: Importing Data, PreProcessing, Basic Statistics & Visualisasi
    4. Pengenalan model rekomendasi: Association Rule/Market Basket Analysis
    5. Supervised Learning 1: Korelasi, Regresi, & Regresi Logistik
    6. Supervised Learning 2: Decision Tree, k-NN, Naïve Bayesian, SVM, ANN
    7. Evaluasi Revisited: Ensemble Learning, Imbalance Learning
    8. Categorical Data Analysis (optional)
    9. Basic Data Mining on Time and Space
    10. Unsupervised Learning/Interdependence Method: PCA, Random Indexing, t-SNE, UMAP.
    11. Unsupervised Learning/Segmentation Methods: k-Means (and its variations), Hierarchical Clustering, DBScan, Evaluation.
    12. Introduction to Text Mining: Topic Modelling, Sentiment Analysis
    13. Introduction to Social Network Analysis: Centrality, Partition, Community
    14. Capstone Project
  • Referensi
    – Data Mining: Concepts and Techniques by J Han, M Kamber & J Pei, 2012, 3rd edition, Morgan Kaufmann
    – P.Cabena, P. Hadjinian, R. Stadler, J. Verhees, and A. Zanasi. Discovering Data Mining: From Concept to Implementation. IBM, 1997
    – U. Fayyad, G. Piatetsky-Shapiro, and P. Smith. From data mining to knowledge discovery. AI Magzine,Volume 17,  pages 37-54, 1996.
    – Barry, A. J. Michael & Linoff, S. Gordon. 2004. Data Mining Techniques. Wiley Publishing, Inc. Indianapolis : xxiii + 615 hlm.
    – Hand, David etc. 2001. Principles of Data Mining. MIT Press Cambridge, Massachusetts : xxvii + 467 hlm.
    – Hornick, Mark F., Marcade, Erik & Vankayala, Sunil. 2007. Java Data Mining: Strategy,Standard, and Practice. Morgan Kaufman. San Francisco : xxi + 519 hlm.
    – Tang, ZhaoHui & Jamie, MacLennan. 2005. Data Mining with SQL Server 2005. Wiley Publishing, Inc. Indianapolis : xvii + 435 hal

 

Data Science, IoT, & Big Data

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