2024/2025
Аналитика и представление данных
Статус:
Майнор
Где читается:
Высшая школа бизнеса
Охват аудитории:
для всех кампусов НИУ ВШЭ
Преподаватели:
Архипкина Светлана Владимировна,
Гладкова Маргарита Анатольевна,
Шабанова Диана Сундаровна
Язык:
английский
Кредиты:
5
Course Syllabus
Abstract
This course equips students with the essential skills to transform raw data into actionable insights and communicate them through compelling visualizations. The program combines analytical methods with visualization techniques widely applied in consulting and business problem-solving.
Students will explore the full analytical workflow — from data collection and cleaning, to identifying patterns, generating insights, and presenting recommendations. Emphasis is placed on how to design clear, persuasive visualizations and data stories that resonate with decision-makers.
Data visualization instrument that is used in the course - Yandex DataLens. However, part of the course will be dedicated to how GenAI may increase the efficiency of data analysis and storytelling.
Learning Objectives
- To equip students with the practical skills to transform raw data into actionable insights and compelling data stories using Yandex DataLens and GenAI, enabling them to inform strategy and solve critical business problems.
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