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  3. Introduction to Data Science (LTAT.02.002)
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Introduction to Data Science 2018/19 fall

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  • Lectures
  • Homeworks
  • Projects
  • Call for Data
  • Tutorials

Lectures

Lecture 01 - Sept 5 - Introduction

Slides part 1, slides part 2, video part 1 (covering slides 1-24, affected by technical problems), video part 2 (covering slides 26-end)

Lecture 02 - Sept 12 - First look at the data

Slides part 1, slides part 2, video

Lecture 03 - Sept 19 - Data exploration

Slides, video

Lecture 04 - Sept 26 - Frequent pattern mining

Slides, video

Lecture 05 - Oct 3 - Clustering and dimensionality reduction

Slides, video

Lecture 06 - Oct 10 - Machine learning 1: Introduction

Slides part 1, slides part 2, video

Lecture 07 - Oct 17 - Machine learning 2: Classification

Slides, video

Lecture 08 - Oct 24 - Machine learning 3: ROC analysis and regression

Slides, video

Lecture 09 - Oct 31 - Machine learning 4: Deep learning

Slides, video part 1, video part 2

Lecture 10 - Nov 7 - Computational statistics

Slides, video

Lecture 11 - Nov 14 - Databases and big data

Slides, video

Project kickoff slides, video

Lecture 12 - Nov 21 - Applications

Slides, video

Nov 28 - Project pitches (same room and time as the lecture)

Video

Lecture 13 - Dec 5 - The world of data science (software tools, privacy, ethics, ...)

Dec 12 - Consultation for projects and exam (same room and time as the lecture)

Dec 19 - Poster session in the atrium of Paabel (Ülikooli 17) at 16:00-19:00

  • Institute of Computer Science
  • Faculty of Science and Technology
  • University of Tartu
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