Data-Orientation
Description: This lecture will start looking into the data dimension of the ML concept and will emphasise on the importance of data-orientation. We first define the concept of data, the associated challenges, and provide examples of data collection processes. We then define a data science methodology to iteratively build the datasets that will feed our machine learning models. This lecture explores the first step of this methodology, "data access".Department: Centro de Estudios y Asesorías en Estadística (CEASE)
Institution: Universidad de Nariño
Date: May 31, 2025
Hours: 4
From: 10:00 am
To: 12:00 am
Resources
Papers and Reports
- Zittrain J. (2022). Intellectual Debt: With Great Power Comes Great Ignorance
- Cabrera C. et al. (2023) Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective
- Lawrence N. D. (2017) Data Readiness Levels
- Taherdoost H. (2021) Data Collection Methods and Tools for Research; A Step-by-Step Guide to Choose Data Collection Technique for Academic and Business Research Projects
- Boda Bodas and Road Traffic Injuries in Uganda: An Overview of Traffic Safety Trends from 2009 to 2017
Web
- The Data Dichotomy: Rethinking the way we Treat Data and Services
- The Data Science Landscape - Advanced Data Science Lecture at Cambridge
- OpenML Datasets
- Tensorflow Datasets
- Iris Dataset
- UK Price Paid Dataset
- Open Postcode Geo Dataset
- Open Street Maps API
- Kaggle Datasets
- Registry of Research Data Repositories