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Обычная версия сайта
12
Ноябрь

Data Visualization

2021/2022
Учебный год
ENG
Обучение ведется на английском языке
4
Кредиты

Преподаватель

Course Syllabus

Abstract

The growing availability of informative datasets and software tools has led to increased reliance on data visualizations across many areas. Data visualization provides a powerful way to communicate data-driven findings, motivate analyses, and detect flaws. This course will give you the skills you need to leverage data to reveal valuable insights.
Learning Objectives

Learning Objectives

  • The purpose of "Data Visualization" discipline is to master modern applied visualization tools (Excel, Power BI and Tableau), familiarization with different types of graphs, their construction and organization in reports and dashboards. The objectives of mastering the discipline "Data Visualization" are: 1) to be able to choose the right method of data visualization, 2) to gain experience using visualization tools (Excel, Power BI and Tableau) and 3) to practice organizing reports and dashboards with visualization results.
Expected Learning Outcomes

Expected Learning Outcomes

  • to know basic approaches and methods of data visualization
  • to know the basic rules of effective design and presentation of data
  • to be able to create dashboards using Power BI and Tableau software
  • to be able to create interesting, informative and creative stories using data
Course Contents

Course Contents

  • Introduction to Data Visualization
    Introduction to data visualization. What is data visualization. Visual and statistical thinking. History of Visualization. Review of visualization software/internet tools. Understanding image file formats. Visual explanations. Telling a story with images. Getting access to Tableau Public/Desktop and Power BI Desktop.
  • Methodology of Data Visualization
    Several methodological approaches and developments that allow you to build processes in the field of visual analytics more systematically, to identify risks and accelerate the work
  • Basic Types of Visualization
    Visualization of amounts (column/bar graph, stacked bar graph, heatmap), distributions (bar and line histograms, density plots), proportions (pie chart, polar area diagram, subburst charts, side-by-side bar graph, stacked graphs, mosaic plots, tree map, parallel sets), x-y relationships (line graph, area chart, scatter plot, connected scatterplot, bubble chart), geospatial data (maps, choropleth, cartogram), process charts (funnel).
  • Principles of Figure Design
    Human information processing. Tufte’s design rules. Using color. Data visualization design process. Best practices. Ten principles of good design. Effective visualizations.
  • Basic Graphs in Power BI
    Power BI interface design. Key concepts. Unit of analysis/aggregation. Creating visualizations in Power BI: Line charts, area charts, column/bar charts, pie/doughnut charts, gauge charts, funnel charts, bubble charts, slicer charts (use of filters), tree maps, tables, custom visualizations. Best practices.
  • Dashboard Design in Power BI
    Your audience. Dashboard goals. Information organization. Labeling and Formatting. Margin size. Key characteristics of great dashboards. Reducing complexity and providing clarity. Common data visualization mistakes. Differences between a report and a dashboard in Power BI.
  • Basic Graphs in Tableau
    Tableau interface design. Key concepts. Unit of analysis/aggregation. Creating visualizations in Tableau: Line charts, area charts, column/bar charts, pie/doughnut charts, gauge charts, funnel charts, bubble charts, slicer charts (use of filters), tree maps, tables, custom visualizations. Best practices.
  • Dashboard Design in Tableau
    Line charts, area charts, column/bar charts, pie/doughnut charts, gauge charts, funnel charts, bubble charts, slicer charts (use of filters), tree maps, tables, custom visualizations. Creating visualizations in Tableau. Best practices. Tableau versus Power BI.
  • Data Storytelling
    Data-driven storytelling
Assessment Elements

Assessment Elements

  • non-blocking Exam
  • non-blocking Home tasks
Interim Assessment

Interim Assessment

  • Interim assessment (1 module)
    0.4 * Exam + 0.6 * Home tasks
Bibliography

Bibliography

Recommended Core Bibliography

  • Brent Dykes. (2020). Effective Data Storytelling : How to Drive Change with Data, Narrative and Visuals. Wiley.
  • Walny, J., Frisson, C., West, M., Kosminsky, D., Knudsen, S., Carpendale, S., & Willett, W. (2019). Data Changes Everything: Challenges and Opportunities in Data Visualization Design Handoff.

Recommended Additional Bibliography

  • Nathalie Henry Riche, Christophe Hurter, Nicholas Diakopoulos, & Sheelagh Carpendale. (2018). Data-Driven Storytelling. A K Peters/CRC Press.