Module specification 

Credits: 10 ECTS

Useful Links:

http://www.oracle-dba-online.com/sql/oracle_sql_tutorial.htm 

https://www.tutorialspoint.com/sql/ 

https://livesql.oracle.com/apex/livesql/file/index.html  

Module specification

Credits: 5 ECTS

Material Provided:

Setup and started with R so 

• • Work through the introductory Chapters 1 to 3 of Introduction to Data Science available at: https://rafalab.dfci.harvard.edu/dsbook/getting-started.html

OR

• • Work through the basic projects in Hands On Programming in R available at : https://rstudio-education.github.io/hopr/basics.html

Module specification

Credits: 5 ECTS

Module specification

Credits: 10 ECTS

Books:

1. Fundamentals of Machine Learning for Predictive Data Analytics Algorithms, Worked Examples, and Case Studies. By John D. Kelleher, Brian Mac Namee and Aoife D'Arcy

Link: https://mitpress.mit.edu/9780262029445/fundamentals-of-machine-learning-for-predictive-data-analytics/

2. Intro Statistical Learning: https://www.statlearning.com 

3. Introduction to Machine Learning with Python: A Guide for Data Scientists 1st Edition, by Andreas C. Müller, Sarah Guido

Blogs and Websites:

1. A Friendly Introduction to Machine Learning: https://www.youtube.com/watch?v=IpGxLWOIZy4 

2. A Visual Introduction to Machine Learning: http://www.r2d3.us/visual-intro-to-machine-learning-part-1/  

3. An Introduction to Machine Learning: https://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer  

4. TensorFlow playground: http://playground.tensorflow.org 

5. Simple intro to Machine Learning: https://martechtoday.com/how-machine-learning-works-150366  

6. Machine Learning Mastery Blog: http://machinelearningmastery.com/blog/ 

7. Edwin ChenBlog: http://blog.echen.me/ 

Apps and Tutorials:

Python: 

Getting Started with Scikit-learn in 5 Steps - https://www.kdnuggets.com/5-steps-getting-started-scikit-learn

Python scikit-learn tutorial - https://www.digitalocean.com/community/tutorials/python-scikit-learn-tutorial

Scikit-learn Machine Learning code examples - https://scikit-learn.org/stable/auto_examples/index.html

Module specification

Credits: 5 ECTS

Module specification

Credits: 5 ECTS

Useful information:

This module does not have any specific prerequisites, however some background knowledge of data protection legislation and an interest in some of the key ethical debates in data science and machine learning would be useful. 

Suggested Reading:

What is Data Management?

https://www.dataversity.net/what-is-data-management/#

Overview of the General Data Protection Regulation (GDPR)

https://www.citizensinformation.ie/en/government_in_ireland/data_protection/overview_of_general_data_protection_regulation.html

World Economic Forum - How to Prevent Discriminatory Outcomes in Machine Learning

3 

http://www3.weforum.org/docs/WEF_40065_White_Paper_How_to_Prevent_Discriminatory_Outcomes_in_Machine_Learning.pdf

Spark Podcast - AI's problem with disability and diversity

https://www.cbc.ca/radio/spark/362-machine-learning-outliers-smart-device-ownership-and-more-1.4279433/ai-s-problem-with-disability-and-diversity-1.4279444

Core texts:

O’Keefe, K. and O’Brien, D. (2018) Ethical Data and Information Management: Concepts, Tools and Methods (1st. ed.). Kogan Page Ltd., GBR.

DAMA International (2017) DAMA DMBOK, DAMA DMBOK – Data Management Body of Knowledge, Technics Publications, New Jersey