Introduction to Data Science (500189-B-6)
Tilburg University (UVT)
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Lecture notes minor Applied Advanced Research Methods (UvT)
- Voordeelbundel • 4 items • 2021
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This bundle contains:
- All the lecture notes on Introduction to Data Science
- All the lecture notes on Topics in Causal Analysis
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Introduction to Data Science lectures 1-9 (UvT)
- Voordeelbundel • 2 items • 2021
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Study: Bachelor Psychology Tilburg University
Major/minor: Psychological Methods and Data Science/Applied Advanced Research Methods
Course: Introduction to Data Science 2020/2021 (jaar 3)

This course is divided in two parts:
1. Supervised learning
2. Unsupervised learning
This summary covers both topics.
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Lecture notes Introduction to Data Science Unsupervised Learning (Lecture 6-9) (UvT)
- College aantekeningen • 32 pagina's • 2021
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Study: Bachelor Psychology Tilburg University 
Major/minor: Psychological Methods and Data Science/Applied Advanced Research Methods 
Course: Introduction to Data Science 2020/2021 (jaar 3) 
Professor: Kim de Roover 
Based on: Lectures and slides 
 
This course is divided in two parts: 
1. Supervised learning 
2. Unsupervised learning 
This summary covers the second topic.
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Lecture notes Introduction to Data Science Supervised learning (lecture 1-5) (UvT)
- College aantekeningen • 51 pagina's • 2021
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Study: Bachelor Psychology Tilburg University 
Major/minor: Psychological Methods and Data Science/Applied Advanced Research Methods 
Course: Introduction to Data Science 2020/2021 (jaar 3) 
Professor: Kyle Lang 
Based on: Lectures and slides 
 
This course is divided in two parts: 
1. Supervised learning 
2. Unsupervised learning 
This summary covers the second topic.
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Summary of Introduction to Data Science course
- Samenvatting • 22 pagina's • 2019
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- €8,49
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This document presents an in depth summary of the Introduction to Data Science course within the Cognitive Science and Artificial Intelligence bachelor program at Tilburg University.

This summary covers the basic topics of Machine Learning and Data Science, such as Supervised and Supervised learning at an intermediate level. It also provides clear explanations of the most used algorithms in ML, both for Classification and Regression analysis in Supervised Learning and clustering and dimensional...
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