Study Console

Revision Notes

39 lectures, one page each: the concepts explained in Traditional Chinese with the English terms kept, worked examples, a self-test and a one-page cheat sheet.

Exam markers are inferred from what the slides stress, repeat and work through — they are not the lecturer’s announced scope.

COM6001Computing with Big Data

  1. Week 1Introduction to Big Data
  2. Week 2Python Review 1: Environment, Data Types & Data Structures
  3. Week 3Python Basics II: Control Statements, Functions, Lambda & Packages
  4. Week 4pandas Data Manipulation: Series, DataFrame, I/O, Combining & Renaming
  5. Week 5Pandas Data Manipulation (II)
  6. Week 6Data Cleaning (I): Unnecessary Data & Missing Values
  7. Week 7Data Cleaning (II): Outliers, Datetime, Encoding & Binning
  8. Week 8Text Cleaning & Feature Scaling
  9. Week 9Feature Transformation (3): map / apply & EDA (1): Aggregating Statistics, Sampling, Correlation
  10. Week 10EDA (2): Group By, Pivot Tables & Intro to Data Visualization

COM6002Big Data Management

  1. Week 1Introduction to Big Data Management
  2. Week 2Data Collection (Data Ingestion)
  3. Week 3(Centralized) Relational Database Management Systems (RDBMS): Theories and Concepts
  4. Week 4SQL
  5. Week 5Data Visualization and Applications: Power BI (Practical)
  6. Week 6Distributed Database Systems
  7. Week 7NoSQL Databases: Graph, Column, Wide-column, In-memory, Search & Vector

COM6004Data Mining

  1. Chapter 1Introduction to Data Mining
  2. Chapter 2Data: Attributes, Quality, Similarity & Preprocessing
  3. Chapter 3Data Exploration & Visualization
  4. Chapter 4AClassification (I): Decision Trees, KNN, Naïve Bayes & SVM
  5. Chapter 4BClassification (II): Overfitting, Model Evaluation & Imbalanced Classes
  6. Chapter 5Cluster Analysis: K-means, Hierarchical & DBSCAN
  7. Chapter 6Association Analysis: Association Rule Mining
  8. Chapter 7 (Supplement)Matrix Algebra Review（非考試範圍）
  9. Chapter 7Dimensionality Reduction: PCA, SVD & Factor Analysis（非考試範圍）
  10. Chapter 8Ensemble Methods: Bagging, Boosting (AdaBoost) & Random Forest（非考試範圍）

COM6005Statistical Modeling

  1. Module 1Descriptive Statistics & Inferential Statistics
  2. Module 2aSimulation, Variable Types & Probability Functions
  3. Module 2bBinomial, Geometric & Poisson Distributions
  4. Module 2cNormal Distribution, QQ-plot & Approximation
  5. Module 3Confidence Interval & Hypothesis Testing: Concepts
  6. Module 3aOne-sample & Two-sample t-tests
  7. Module 3bTabular Data & Chi-squared Tests
  8. Module 4Linear Regression
  9. Module 4bModel Building Techniques
  10. Module 5Logistic Regression
  11. Module 6Model Comparison / Feature Selection
  12. Module 7Principal Component Analysis (PCA)