2024/2025
Аналитика и представление данных
Статус:
Майнор
Где читается:
Высшая школа бизнеса
Когда читается:
1, 2, 4 модуль
Охват аудитории:
для всех кампусов НИУ ВШЭ
Язык:
английский
Кредиты:
5
Контактные часы:
48
Course Syllabus
Abstract
The course "Data Analysis and Visualization" is designed to provide students with essential skills in analyzing and visualizing data using various tools and techniques. Students will learn how to manipulate and interpret data, create meaningful visualizations, and draw insights to make informed decisions. Through hands-on projects and real-world case studies, participants will gain practical experience in applying data analysis and visualization methods to solve complex problems. This course is suitable for beginners as well as intermediate learners looking to enhance their data analysis skills.
Learning Objectives
- Explain and demonstrate the basic principles of data storytelling. Provide an understanding of working with interactive data storytelling tools. Master the basic skills of creating statistical data visualizations. Practical skills in creating visualizations based on machine-readable data and building stories based on data.
Expected Learning Outcomes
- Explains the basics of visual thinking, knows how to choose a type of visualization and determine the method of visual encoding.
- Can select legible and attractive color schemes to build a story in visualization.
- Shows how to create a graph using online visualization tools.
- Chooses a topic suitable for visualization.
- Names where to find open data, knows how to search for key facts in existing data and creates a story based on them.
- Can accurately build visualizations based on statistical data.
- Explains what data art is and its objectives.
- Explains what data storytelling is and how it differs.
- Distinguishes between types of maps, knows how to build a point map and a polygon map.
- Develops a project with graphics and text. Defends the final project.
Course Contents
- What is data storytelling, examples of work.
- Basics of visualization.
- Visualizing data using graphs.
- Anatomy of a diagram.
- Cartography.
- Longreads, scrolling and project assembly.
- Features of creating a graphic flow chart.
- Data storytelling in social networks.
Interim Assessment
- 2024/2025 2nd module0.2 * Attendance + 0.3 * Final project + 0.1 * Homework 1 + 0.1 * Homework 2 + 0.1 * Homework 3 + 0.1 * Homework 4 + 0.1 * Homework 5
Bibliography
Recommended Core Bibliography
- Brent Dykes. (2020). Effective Data Storytelling : How to Drive Change with Data, Narrative and Visuals. Wiley.
- Data analysis for social science : a friendly and practical introduction, Llaudet, E., 2023
- Graph Data Science with Neo4j : learn how to use Neo4j 5 with Graph Data Science library 2.0 and its Python driver for your project, Scifo, E., 2023
- The data storytelling workbook, Feigenbaum, A., 2020
Recommended Additional Bibliography
- Gerasimov, I., Glebov, S., Kaplunovski, A., Mogilner, M., & Semyonov, A. (2015). “Big Data” and “Small Stories” for the Future. Ab Imperio, 4, 9–25. https://doi.org/10.1353/imp.2015.0093