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Regular version of the site

Data Visualization and Storytelling with Python

2023/2024
Academic Year
ENG
Instruction in English
3
ECTS credits
Course type:
Compulsory course
When:
1 year, 2 module

Instructor

Course Syllabus

Abstract

Often, there is not enough space for information to be displayed and time to be shared with third parties. Therefore, the data is presented graphically. A high-quality presentation allows you to: See trends in changes in company performance, growth or decrease in profit, conduct a comparative analysis when moving from one business development strategy to another, emphasize the dependence of some indicators on others Visualization is also used to obtain some results. For example, using visualization tools, you can calculate statistical indicators or aggregate data, for example, find the amount of monthly profit by daily indicators. Also, these tools allow you to build graphs and diagrams of the data itself or its aggregations. As part of the course, we will dwell in more detail on the latter - plotting and diagrams. We will visualize the data using Python libraries. The Python language is one of the most common for solving problems related to data analysis and machine learning. Thanks to Python, an analyst can use a single tool to obtain, process and analyze data.
Learning Objectives

Learning Objectives

  • During the course students are taught to see context for data, create data-based narrative, asses what data needs visual representation and what tools to use for most efficient visual data representation and data-storytelling.
  • To gain experience using visualization tools using Python and to practice organizing reports and dashboards with visualization results.
Expected Learning Outcomes

Expected Learning Outcomes

  • GPC-2.ECN. Able to apply advanced instrumental methods of economic analysis in applied and (or) fundamental research
  • PC-2. Able to identify the data necessary to solve the set research tasks in the field of management; to collect data both in the field and from the main sources of socio-economic information: reports of organizations of various forms of ownership, departments, etc., databases, journals, etc.
Course Contents

Course Contents

  • Data types and storytelling
  • Data extraction and wrangling
  • Data exploration and engineering
  • Data visualization and integration
Assessment Elements

Assessment Elements

  • non-blocking Final Project
  • non-blocking Assignments
  • non-blocking Assignments
  • non-blocking Assignments
  • non-blocking Assignments
  • non-blocking Assignments
  • non-blocking Assignments
  • non-blocking Assignments
Interim Assessment

Interim Assessment

  • 2023/2024 2nd module
    0.115 * Assignments + 0.115 * Assignments + 0.115 * Assignments + 0.115 * Assignments + 0.115 * Assignments + 0.115 * Assignments + 0.115 * Assignments + 0.195 * Final Project
Bibliography

Bibliography

Recommended Core Bibliography

  • Brent Dykes. (2020). Effective Data Storytelling : How to Drive Change with Data, Narrative and Visuals. Wiley.

Recommended Additional Bibliography

  • Nathalie Henry Riche, Christophe Hurter, Nicholas Diakopoulos, & Sheelagh Carpendale. (2018). Data-Driven Storytelling. A K Peters/CRC Press.
  • Sharan B. Merriam, & Robin S. Grenier. (2019). Qualitative Research in Practice : Examples for Discussion and Analysis: Vol. Second edition. Jossey-Bass.