Durham_Master_Data_Science

Index	Module Title	Lecturer	Credits
COMP42215_2022	Introduction to Computer Science	Dr Robert Powell	15
PHIL42415_2022	Ethics and Bias in Data Science	Dr Aadil Kurji	15
BUSI4S115_2022	Strategic Leadership	Dr Ziya Ete	15
MATH42715_2022	Introduction to Statistics for Data Science	Dr Tahani Coolen-Maturi Dr Sarah Heaps	15
COMP42315_2022	Programming for Data Science	Dr Pak Ho Shum Dr Gagangeet Aujla	15
MATH42815_2022	Machine Learning	Dr Samuel Jackson Dr Hailiang Du	15
MATH42515_2022	Data Exploration, Visualization and Unsupervised Learning	Dr Jonathan Cumming Dr Hyeyoung Maeng	15
COMP42415_2022	Text Mining and Language Analytics	Dr Stamos Katsigiannis	15
ANTH40A15_2022	Critical Perspectives on Data and Quantification	unknown	15

Comments:

EBDS和Strategic Leadership这种文科型作业就是essay，就不上传交的作业了，课程内容就放在这个页面
IMDS这门课没选，但是既然可以看到课程内容，一起放在这个页面供参考
MATH的课程都是用的R，其中Dr Hailiang Du的ML部分是不强制使用什么语言的，而且作业不用交代码
COMP的课程都是用Python，而且分数较高，能选COMP的课尽量COMP
其他我选择的课程内容依据课程放到相应文件夹中
Programme and Module Handbook_g5k823 是g5k823 22Fall选课说明，其他分支都有相应的Handbook，可以自己去官网下载

上传的作业可以作为参考，主要参考老师给的feedback，大概率这些feedback的点也是以后Assignment中的得分点，看了老师的feedback，做Assignment的时候避免这些点失分，也许可以多得几分。
一些作业尚未出分，暂时不上传，出分后再传

Tips：

• 很多人的信息检索能力太差了，要努力提高呀，学习一下检索技巧（其实就是多加关键词和一些特殊作用的符号，再加上点耐心）
• 出来了就不要再用百度了，还是Google吧，如果想用bing，也要用New Bing（虽然我一直没用，但是据说接入ChatGPT的接口后还是很强大的）【内事问百度，外事问谷歌】
• 善用ChatGPT：用ChatGPT学习，而不是直接让ChatGPT给你写essay
• 文科类的essay不要一条一条ctrl+v粘贴参考文献了，学学Zotero吧，简单入门即可，装上插件（Zotero PDF Translate）就可以实现划词翻译了。简单学学就足够写essay、毕业论文了。
• 认识一下Overleaf，LaTeX语言写各种report，对数学、计算机的Report非常友好，比word写出来的论文漂亮多了，我上传的pdf report就是用的Overleaf写的。一年也就几个report，常用到的代码也就几个，特殊代码ChatGPT查一下就好了。Overleaf使用Durham账号登录就可以使用overleaf premium。

ICS -- COMP42215 Introduction to Computer Science

Dr Rob Powell – Course Lecturer

Content

• Topic 1 - Python Data Types
• Topic 2 - Python Data Structures
• Topic 3 - Control Flow
• Topic 4 - Functions and Modules
• Topic 5 - NumPy
• Topic 6 - Towards Data Science
• Topic 7 - Towards Economics and Finance with Pandas
• Topic 8 - Error Checking
• IO In Python

ISDS -- MATH42715: Introduction To Statistics For Data Science

Lecturer: Dr Tahani Coolen-Maturi
Lecturer: Dr Sarah Heaps

Workshop2022: Introduction to Statistics for Data Science

Content

• Week 1
  • 1 Basic Concepts
  • 2 Data Types in Statistics
  • 3 Descriptive Statistics
  • 4 Continuous Distributions
  • 5 Sampling
  • 6 Estimation
  • 7 Hypothesis Testing One Sample
  • 8 Hypothesis Testing Two Samples
  • 9 Nonparametric Tests
• Week 2
  • 1 Correlation
  • 2 Simple regression: Introduction
  • 3 Simple Regression: Coefficient of Determination
  • 4 Simple Linear Regression: Assumptions
  • 5 Simple Linear Regression: Inference
  • 6 Simple Linear Regression: Confidence and Prediction intervals
  • 7 Multiple Linear Regression: Introduction
• Week 3
  • 1 Introduction to Variable Selection
  • 2 Assessing Predictive Error
  • 3 Cross-Validation
  • 4 The Bootstrap
• Week 4
  • 1 Introduction
  • 2 Model-Based Classification
  • 3 Logistic Regression
  • 4 Discriminant Analysis
  • 5 Assessing Predictive Error and Cross-Validation

MATH42815_2022 -- Machine Learning

Dr Samuel Jackson
Dr Hailiang Du

Workshop2022: Machine Learning Part I
Workshop2022: Machine Learning Part II

Assignment 1 Online 选择或填空，不再上传具体题目
Assignment 2 Ungraded

Content

• I Variable Subset Selection

  • 1 Variable Subset Selection
  • 2 Model Search Methods

• II Shrinkage

  • 3 Ridge Regression
  • 4 The Lasso
  • 5 Choosing 𝜆
  • 6 Principal Component Regression

• III Transformation

  • 7 Polynomial Regression
  • 8 Step Functions
  • 9 Splines
  • 10 Generalised Additive Models

• 1 Recap and Basic Concepts

  • 1.1 Supervised and Unsupervised Learning
  • 1.2 Classificationand Regression
  • 1.3 Parametric vs non-parametric models
  • 1.4 Uncertainty
  • 1.5 Model Assessment
  • 1.6 What do we need?
  • 1.7 Models learned so far

• 2 Tree-based Models

  • 2.1 Classification and Regression Trees
  • 2.2 Bagging
  • 2.3 Random Forests
  • 2.4 Boosting

• 3 Neural Networks and Deep Learning

  • 3.1 Motivation
  • 3.2 Warmup
  • 3.3 Feedforward Neural Networks
  • 3.4 Gradient Descent Optimization
  • 3.5 Overfitting
  • 3.6 Convolutional neural networks (CNNs)
  • 3.7 Autoencoder

DEVUL -- MATH42515_2022 Data Exploration, Visualization and Unsupervised Learning

Lecturer: Dr Jonathan Cumming
Lecturer: Dr Hyeyoung Maeng

Workshop2022: Data Exploration and Visualization
Workshop2022: Unsupervised Learning

Assignment 1 Ungraded Assignment 2 Ungraded

Content

• Lecture 1 - Exploratory Data Analysis

• Lecture 2 - Exploring Continuous Variables

• Lecture 3 - Exploring Categorical Variables

• Lecture 4 - Dependency, Relationships, and Associations

• Lecture 5 - Exploring Multivariate Continuous Data

• Lecture 6 - Exploring Many Categorical Variables

• Lecture 7 - Comparisons and Data Quality

• Lecture 8 - Smoothing and Kernel Denisty Estimation

• Lecture 9 - Exploring Time Series

• Lecture 10 - Making Effective Data Visualisations

• Introduction: Unsupervised Learning

• Dimension reduction: Principal component analysis

• Dimension reduction: Correspondence Analysis

• Latent variable models: Factor Analysis

• Cluster Analysis: K-means Clustering

• Cluster Analysis: Hierarchical Clustering

• Association: Market Basket Analysis

TMLA -- COMP42415_2022 Text Mining and Language Analytics

Lecturer: Dr Stamos Katsigiannis

Assignment: Ungraded

Content

• Lecture 0 - About the course
• Lecture 1 - Text pre-processing
• Lecture 2 - One-hot encoding & Term frequency-based representation
• Lecture 3 - N-grams
• Lecture 4 - Word Embeddings I
• Lecture 5 - Feed-forward Neural Networks & Convolutional Neural Networks
• Lecture 6 - Word Embeddings II
• Lecture 7 - Naive Bayes and Sentiment Classification
• Lecture 8 - Recurrent Neural Networks & Long Short-term Memory

PHIL42415_2022 -- Ethics and Bias in Data Science

这课虚得很，很不推荐选，选这个不需要选数学，事实上根据同学们的反馈，总体上数学分数比这课高得多

Content

• Lecture One - Data Bias & Discrimination
• Lecture Two - Data, the Environment & Sustainability
• Lecture Three - Algorithmic Decision-Making & Black Box Reasoning
• Lecture Four - Privacy, Consent & Personal Data

BUSI4S115_2022 -- Strategic Leadership

另一门水课，总体上比EBDS好点

Content

• Week 1: Introduction to the Module
• Week 2: Organizational Leadership & Leadership Traits and Skills
• Week 3: Leadership Behaviours & Situational Leadership
• Week 4: Relational Leadership
• Week 5: Transformational Leadership
• Week 6: Organizational Culture & Adaptive Leadership
• Week 7: Leading Change through Strategic Leadership
• Week 8: Ethics and Leadership
• Week 9: Dark Side & Leadership within Teams & Creative Leadership
• Week 10: Leadership and Gender & Leading Across Cultures

MATH42615_2022 -- Introduction to Mathematics for Data Science

没选这门课，但是资料放开，所以也一并展示出来，供可能选课的同学参考

• Week 1: Basics of vectors
  We will start by looking at how vectors can be used to help us describe various things in the world. This leads to looking at the basic geometry of vectors. Covers lecture videos 1.1 to 3.2
• Week 2: Matrices and linear transformations
  Covers lecture videos 3.3 to 4.3
• Week 3: Probability theory and Calculus
  In this week we will look at the mathematical description of randomness and probability, which is essential in understanding statistics, data, and measurement. Covers lecture videos 5.1 to 7.5
• Week 4: More calculus and applications