A) Introduction to Data Science (W35-36)

Note: Group Portfolio Assignment - Exploratory Data Analysis (EDA) Deadline: Friday, 13 September 2024, 12:00 PM

This topic includes 5 sessions as follows:

  • Welcome to Data Science! (Monday, September 2nd, 08:15-14:15): This session will introduce students to the fundamentals of data science, with a focus on Python. Students will learn about the Python data science stack, essential tools and platforms, and software setup. They will also get a preview of the upcoming weeks and a refresher on Python basics.
  • Data Handling and Manipulation I (Lecture) (Wednesday, September 4th, 10:15-14:15): This session will cover the foundational aspects of data handling in Python. Students will learn about the different types of data that are important in data science, and they will explore essential operations like arrange, group-by, filter, select, and join. By the end of this session, students should have a solid understanding of primary data manipulation techniques.
  • Exploratory Data Analysis & Essential Statistics (Thursday, September 5th, 08:15-12:00): This session will introduce students to exploratory data analysis (EDA) and essential statistics. Students will learn how to use EDA to uncover patterns, anomalies, and frame questions in data. They will also learn about foundational measures and techniques for data interpretation.
  • Data Visualization in Data Science (Friday, September 6th, 08:15-12:00): This session will teach students the importance of effective data visualization in data science. Students will explore Seaborn, a Python library for intuitive statistical graphics, and Altair, a declarative visualization library for Python. They will also have the opportunity to create impactful visualizations with real datasets through hands-on exercises.
  • Building an Employee Attrition Dashboard (Monday, September 9th, 10:15-14:15): This session will guide students through creating an interactive Employee Attrition Dashboard using Streamlit, a powerful Python framework for building data-driven web applications. Participants will learn how to integrate data processing, visualization, and interactivity into a cohesive dashboard.