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Main
UQU Programs
Data Science

Data Modelling

DS3402
4 hours English

Data Modelling

Data Modelling
MTH1141 Calculus 1
4 hours No requisites
DS2401 Machine Learning for Data Science
4 hours Two requisites
1 Basic math and data modelling concepts: / Linear Algebra / Probability / Review the concept differential calculus, integral calculus and chain rules
2 Perceptron Neural Network Model
3 Feedforward Neural Network Architecture
4 Training Neural Network using Gradent Descent and Stochastic Gradient Descent
5 Implementing MLPs with Keras: / Installing TensorFlow / Building an Image Classifier Using the Sequential API / Building a Regression MLP Using the Sequential API / Saving and Restoring a Model / Using Callbacks / Visualization Using TensorBoard / Fine-Tuning Neural Network Hyperparameters / Number of Hidden Layers / Number of Neurons per Hidden Layer / Learning Rate, Batch Size and Other Hyperparameters
6 Training Neural Network: the implementation, which include the following topics: / Vanishing/Exploding Gradients problems / Avoiding overfitting through Regularization / Analysing Loss Curves
7 Neural Networks for Spatial Data: Convolutional Neural Network. / Convolutional Operation / Convolutional Neural Network / Stride / Pooling Layer / Common CNN Architectures and application
8 Sequence Model - Recurrent and Recursive Nets (RNN): / Training RNN / Problem: learning challenges / Solution: LSTM and Gated RNNs
9 Sequence to Sequence Model with Attention: / Hard and Soft Attention / How to Measure Attention / Attention Challenges / Visualizing Attention
10 Transformers Models: / Transformer overview / Self-attention / Multi-head attention / Pioneering transformers: machine translation / Common transformers: GPT and BERT
11 Generative Models: / Discriminative vs. Generative models. / Introduction to Generative model / Taxonomy of Generative Models. / Autoencoders / Variational Autoencoders (VAEs). / Generative Aversarial Networks
12 Reinforcement Learning
13 Model Evaluation
14 Model Deployment, Serving and Monitoring
1.1 Mapped to: K1

Identify appropriate tools and techniques for data modeling.

Teaching Strategy Lectures Lab Excerises Case Study
Assessment Methods Midterm and Final exams Quiz Lab exam
1.2 Mapped to: K2

Demonstrate understating of the role of math and technology in building data models for a range of real-world problems.

Teaching Strategy Lectures Lab Excerises Case Study
Assessment Methods Midterm and Final exams Quiz Lab exam
1.3 Mapped to: K3

Compare and explain differences between different data modeling techniques.

Teaching Strategy Lectures Lab Excerises
Assessment Methods Midterm and Final exams Quiz Lab assesment Project
2.1 Mapped to: S1

Apply appropriate mathematical, statistical and machine learning techniques for building data models.

Teaching Strategy Lectures Lab Excerises
Assessment Methods Midterm and Final exams Quiz Lab assesment Project
2.2 Mapped to: S2

Develop research methods involving the use of data modeling techniques to solve real-world data science problems.

Teaching Strategy Lectures Lab Excerises Case Study
Assessment Methods Midterm and Final exams Lab assesment Project
2.3 Mapped to: S4

Interpret and explain the output of data models.

Teaching Strategy Lectures Lab Excerises Case Study
Assessment Methods Project
3.1 Mapped to: V2

Demonstrate ethical considerations of building and using data models.

Teaching Strategy Lectures Lab Excerises
Assessment Methods Project
Assessment
Weight
Quizzes
20%
Midterm Exam
15%
Lab Assessment
10%
DM Project
15%
Final Exam
40%
Total
100%