Data visualization using the Phyton

2024/2025

Academic Year

ENG

Instruction in English

3

ECTS credits

Delivered at:

Department of Management

Course type:

Compulsory course

When:

1 year, 2 module

Course Syllabus

Abstract

"A picture is worth a thousand words". We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the analysis of increasingly large datasets. Data visualization plays an essential role in the representation of both small and large-scale data. One of the key skills of a data scientist is the ability to tell a compelling story, visualizing data and findings in an approachable and stimulating way. Learning how to leverage a software tool to visualize data will also enable you to extract information, better understand the data, and make more effective decisions. The main goal of this Data Visualization with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually but in this course, we will be using several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium.

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

• able to apply advanced instrumental methods of economic analysis in applied and (or) fundamental research
• 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

• 1. Data types and storytelling
• 2. Data extraction and wrangling
• 3. Data exploration and engineering
• 4. Data visualization and integration

Assessment Elements

• In-class assignments
• Final Project

Interim Assessment

• 2024/2025 2nd module
  0.11 * Assignments + 0.11 * Assignments + 0.11 * Assignments + 0.11 * Assignments + 0.11 * Assignments + 0.11 * Assignments + 0.11 * Assignments + 0.23 * Final Project

Bibliography

Recommended Core Bibliography

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

Recommended Additional Bibliography

• Brent Dykes. (2020). Effective Data Storytelling : How to Drive Change with Data, Narrative and Visuals. Wiley.
• Sharan B. Merriam, & Robin S. Grenier. (2019). Qualitative Research in Practice : Examples for Discussion and Analysis: Vol. Second edition. Jossey-Bass.

Authors

• BRODSKAYA NATALYA NIKOLAEVNA