Quantitative Methods for Data Analytics and Artificial Intelligence

This is a course about quantitative methods in data analytics and artificial intelligence fields.

Data and Data Set

Normally, we take a group of objects which shares the same type to analyse. When we analise these objects, there are different kinds of information we can get from them. For the individual object, we can learn the details of it. Furthermore, the details of the whole group objects getting together became a table. Every row shows the individual object (data sample), and every column shows a detail (feature) of the whole group of objects.

• Data Sample: Data point or data object.
• Feature: Attribute or variable.
  • Features can be a data field representing a characteristic of a data sample.
  • Features can be classified as Categorical (e.g. price, length, width, etc.) and Numerical (e.g. color, size, etc.).
  • Also, Categorical features can be classified as Nominal (定类) and Ordinal (定序).
    • The values of nominal features are symbols or "name of things", they don't have any meaningful order. Although the nominal data can be represented by numerical values, these numbers do not have mathematical meaning.
    • The values of ordinal features have oder. (e.g. size = {small, medium, large}; grades = {A, B, C})
• Data Set: Collections of data samples.

Data Analytics

The group of processes containing data collection, inspecting, cleansing, transforming and modeling, interpretation and reporting getting together is data analytics.

References

[1] Zhang Lu, Lan Liang. COMP 7180 Quantitative Methods for Data Analytics and Artificial Intelligence. Hong Kong Baptist University, 2020.
[2] Mostafa Eissa. Introduction to Machine Learning (opens new window)