Course Overview

• Course Name: Data Science (2022/2023)

• Program: The third year of Bachelor Informatiekunde (i.e., Information Science)

• Institution: Informatics Institute, University of Amsterdam

• Instructor: Yen-Chia Hsu <y.c.hsu@uva.nl>

• Refer to the course syllabus for details.

• Refer to datanose for the time table and classroom location.

• Refer to Canvas for announcements, the link to live lectures, and other members’ emails in the teaching team.

All the course content on this website is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Schedule Outline

Warning

This schedule may be changed during the development of this course.

Below is the outline of weekly activities. The term “notebook” refers to the Jupyter Notebook script. The term “tutorial” refers to step-by-step guidences of a notebook script. We strongly recommand you to bring your laptop during the lectures with tutorials.

Preparation for Lectures

In the link for each lecture, there is a preparation section. We strongly recommend you to prepare them before going to the lectures or tutorials. If you come to the class without doing the preparation part, you may not be able to understand the contents of lectures or tutorials.

• Week 1

  • Lecture 1 (Feb 7): Introduce the course and explain why data science is important using examples

  • Assignment 1 (work session and self-study): Python programming warm-up (e.g., pandas, numpy, scipy)

  • Lecture 2 (Feb 9): Recap fundamentals of data science techniques (e.g., basic statistics, basic machine learning concepts, metrics to evaluate model performance)

• Week 2

  • Lecture 3 (Feb 14): Tutorial for the structured data processing module (using Jupyter Notebook)

  • Assignment 2 (work session and self-study): Structured data processing module

  • Lecture 4 (Feb 16): Introduce the idea of using Decision Tree and Random Forest for structured data processing

• Week 3

  • Lecture 5 (Feb 21): Overview of deep learning techniques and applications

  • Assignment 3 (work session and self-study): Practice mock-up exam

  • Lecture 6 (Feb 23): Overview of crowdscourcing

• Week 4

  • Mid-term Exam (Feb 28)

  • A lecture to discuss the mid-term exam

• Week 5

  • Lecture 7 (Mar 7): Tutorial for the text data processing module (using Jupyter Notebook)

  • Assignment 4: Text data processing module

  • Lecture 8 (Mar 9): Explain details in the pipeline of processing text data

• Week 6

  • Lecture 9 (Mar 14): Self-study the PyTorch deep learning framework (using Jupyter Notebook)

  • Assignment 5: Use PyTorch to implement a pipeline for structured data processing

  • Lecture 10 (Mar 16): Tutorial for the image data processing module (using Jupyter Notebook)

• Week 7

  • Lecture 11 (Mar 21): Explain details in the pipeline of processing image data

  • Assignment 6: Image data processing module

  • Lecture 12 (Mar 23): Guest lecture by Dr. Jie Yang (an assistant professor from TU Delft EWI) to give examples and talk about state-of-the-art research in Human-Centered AI. This lecture will be give remotely.

• Week 8

  • Final Exam (Mar 28)