CO3722 Data Science

Lecture Documents¶

CO3722 Lecture 1.pdf

Written Notes¶

An Introduction To Data Science¶

Module Overview¶

• Understanding Data
• Programming (with an appropriate language)
• Data Science for Business

Learning Objectives¶

• Perform & Evaluate Basic Descriptive, Exploratory, and Confirmatory Data Analysis
• Write Programs (with an appropriate language) for analysing data
• Visualise the Data in Numerous Ways

Lecture Learning Objectives¶

• Define Data Science
• Discuss examples of Data Science and its relation to Artificial Intelligence

What is AI¶

flowchart TD
    C((Artficial Intelligence))
    B((Machine Learning))
    A((Deep Learning))

    A --> B --> C

The Turing Test Approach¶

The Turing test suggests that computers would require the following:
- Natural Language Processing - To enable it to communicate
- Knowledge Representation - To store what data it is aware of
- Automated Reasoning - To use the stored data to answer questions or draw new conclusions
- Machine Learning - To adapt to new circumstances, and detect patterns

Neuroscience¶

Artificial intelligence in terms of neural networks are inspired by biological, based on neurons and the connections between them.

What is Data Science¶

Data Science - The application of Computational and Statistical techniques to address or gain insight into a problem in the real world.

Examples of Data Science¶

• Data Collection
• Statistics
• Data Processing
• Scientific Inquiry
• AI (Machine Learning)
• Visualisation
• Business Analytics
• Communication and Presentation

Data Science Model¶

flowchart LR
    A[Data Collection]
    B[Data Processing]
    C[Exploration / Visualisation]
    D[Analysis / Machine Learning]
    E[Insight / Policy Decisions]

    A --> B --> A
    B --> C --> B
    C --> D --> C
    E --> D --> E

    C --> A
    D --> A & B
    E --> A & B & C

Summary¶

• Define Data Science
• Discuss examples of Data Science and its relation to Artificial Intelligence.

CO3722 Lecture 2 - Ethics