Study Console

Revision Notes

26 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

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)

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

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