053630 SE Research Seminar (2023W)

This seminar aims to provide an overview of the current state-of-the-art data science research, highlighting key techniques and methods used in the field. The seminar will be divided into two segments. In the first part, attendees will learn about the data science research process by reading and critically evaluating scientific papers. The second part is dedicated to a practical approach where participants get hands-on experience with the tools
and methods used in applied data science research and get a sense of the process involved in applying these tools to real-world problems.

Goals of part 1
By presenting a published research work, participants learn about the latest developments and challenges in fields such as machine learning, natural language processing, computer vision, mathematical aspects of deep learning, applied data science, and other related areas. In particular, students learn how to present and identify a research question, communicate results, and critically evaluate the quality and relevance of a paper. The seminar aims to give attendees a thorough comprehension of the research process by having multiple students actively engage in the presentation of a scholarly paper. Each individual will be assigned one of three distinct roles, each emphasizing a specific facet of the presented work, enriching the discussion and understanding. These roles include:
1. Conducting a literature review of related works.
2. Summarising the presented material for a non-specialist audience (layperson).
3. Identifying potential avenues for future research or practical applications.
Through this approach, attendees will be exposed to various aspects of the research process, including reviewing existing literature, effectively communicating technical concepts, and recognizing opportunities for future study.

Goals of part 2
The research seminar’s coding portion is divided into four programming exercises posted and graded throughout the semester. The exercises are structured as follows:
A1: Introduction to data science research: This exercise aims to show how to set up a programming environment and complete basic programming tasks.
A2: Data preprocessing and visualization: Attendees implement data preprocessing and visualization techniques using a programming language such as Python or R.
A3: Machine learning algorithms: The goal is to implement and compare the performance of different machine learning algorithms on a dataset.
A4: Applied data science project: Using the skills learned in the previous sessions, participants will be expected to collect and clean data, run experiments, and use effective visualizations to explore and communicate their results in an oral presentation.