Content Documentation
- Sociology
- Agile Project Management
- Business Intelligence
- Homework Exercises
- Data engineering: Data architecture & pipeline design
- SQL: Data Definition Language (DDL) clauses 1 & Data Manipulation Language (DML) clauses
- DataLab 1: Research design, EDA, and codebook
- Introduction to Business Intelligence
- SQL: Data Query Language (DQL) clauses
- DataLab 2: Database & SQL assignment
- SQL: Data Definition Language (DDL) clauses 2
- Getting started with Power BI & Prepare data for analysis with Power BI
- DataLab 1: Data wrangling & UX design in Power BI
- Data modeling in Power BI
- Data visualization in Power BI
- DataLab 2: Basic visuals & DAX in Power BI
- Data analysis & Managing workspaces/datasets in Power BI
- DataLab 1: Advanced visuals & Analyzing data in Power BI
- Time Series
- Data Science
- Quantifying our World into Data
- Intro to R
- Datalab 00: SDG indicators
- Intro to Variables and Dataframes
- Descriptive Analyses & Visualisations
- Datalab 01: Exploratory Data Analysis
- Introduction to Probability 1
- Datalab 02: Findings & Data Visualisations
- Intro to Probability 2
- Analysing Relationships between Variables
- Datalab 03: Discussion
- Reporting and Visualising Data
- Datalab 04: Conclusion and Referencing
- Explanatory versus Predictive Modelling Approach
- Block A Recap
- Datalab 05: Poster Presentation Day
- Mathematics I: Linear Algebra and AI
- Introduction to Linear Algebra
- Elementary Operations on Matrices
- Matrix Multiplication and inversion
- Data Lab 1: Elementary Operations on Matrices
- Linear algebra applied to Linear Systems
- Linear Algebra and Linear models
- Data Lab 2: Creative Brief
- Data Lab 3: Creative Brief
- Linear Algebra and Image Processing I
- Linear Algebra and Image Processing II
- Introduction to Machine Learning
- Supervised and Unsupervised Learning
- Regression Algorithms
- Datalab 00: Regression Algorithms
- Classifications Algorithms
- Datalab 01: Classification Algorithms
- Decision Trees
- Supervised Learning: A Recap
- Datalab 02: Tree-Based Analyses
- Clustering: K-Means
- Datalab 03: Clustering Analyses
- Perceptron algorithm
- Datalab 04: Perceptron
- Final Assignment: Machine Learning Analysis
- Responsible and Explainable AI
- Fairness & Bias: Definitions
- DataLab 1: Implicit bias & A Designer's Critical Alphabet
- Fairness & Bias: Individual fairness, debiasing techniques & toolkits
- Fairness & Bias: Group fairness metrics
- DataLab 2: Fairness metrics, and debiasing techniques for image data
- Responsible & Explainable AI
- XAI: The need for explanations!
- Explaining neural networks
- DataLab 1: XAI
- Feature Attribution in Computer Vision
- Moving beyond feature attribution
- DataLab 2: XAI
- Artificial Intelligence
- Human-Centered Artificial intelligence
- Interaction & Information Processing Fundamentals
- Datalab 13: Risk-Assessment of Disruptive Technologies
- Interaction Design
- Interface Design
- Datalab 14: Conceptualizing a Wireframe Prototype
- User Testing
- Datalab 15: Introduction to A/B-Testing
- User-Centered Design for AI
- Designing for AI Algorithms Implementation
- Datalab 16: Client Testing of Wireframe Prototypes
- Final Assignment: Application Prototype
- Deep Learning
- Introduction to Deep Learning Project
- Data Structures for Deep Learning
- Data Lab 3: Tensors
- Perceptron algorithm
- Introduction to Neural Networks
- Data Lab 4: Neural Network in Python
- Implementing Neural networks using TensorFlow
- Data Lab 5: Neural Network in Tensorflow
- Neural Network for classification
- Neural Network in Google Playground
- Data Lab 6: Classification using TensorFlow (MNIST datasets)
- Image Classification (CNN using keras)
- 8.1 DLab-CreativeBrief.md
- Convolutional Neural Networks - Deep Dive
- Data Augmentation
- Data Lab - 7,8,9,10 : Working on Creative Brief
- Transfer Learning
- Practical Issues & BestPractices
- Geospatial Analytics
- Programming in Python
- Programming: A Primer
- Setting up your development environment
- Python for AI and Data Science
- Data Lab 1: Python Foundation exercises
- Python data types & data structures
- Python: Pandas
- Python: NumPy
- Data Lab 2: Python data structures exercises
- Python: Image processing
- Data Lab: Python Image Steganography
- Advanced NumPy: Broadcasting and Vectorizing
- Python: Matplotlib
- Data Lab: Python Image Steganography
- Python: Coding standards and reproducible data science
- Data Lab: Python Image Steganography
- Python - Recap of key concepts.
- Python (web) application development
- Data Lab: Python Image Steganography
- Digital Transformation
- AI for Business (1): Introduction into application of AI in businesses & Risks and benefits of AI
- Scientific writing (2): Citation & Mechanics of style of effective writing.
- Scientific writing (1): The structure of a paragraph and rules of writing & Bias-free language
- AI for Business (2): Application of AI by businesses
- Legal (2): The Proposed Artificial Intelligence Act
- AI in Science Fiction (3): Quizzes on Scientific writing. Presentations on the first draft of the report
- AI in Science Fiction (4): Quiz on Scientific writing. Q&A session on the report
- DataLab (1): Debates: Ethical Decision-Making
- DataLab (2): Debates: Ensuring Responsible AI is unrealistic.
- DataLab (1): Debates: Companies that implement the GDPR law have a competitive advantage.
- DataLab (2): Formative Feedback Session.
- Ethics & Law (1): Introduction to DEDA Framework. Preparation for debates: Ethical Decision-Making.
- Ethics & Law (2): Ethical Guidelines for Statistical Practice. Introduction to GDPR.
- Ethics & Law (3): Preparation for debates: Can AI Systems Be Ethical?
- Legal (1): Introduction to the European Union Law-Making Process.
- Legal (3): When GDPR meets AI
- Ethics & Law