Bachelor
2020/2021
Applied Plotting, Charting and Data Representation in Python
Type:
Elective course (Marketing and Market Analytics)
Area of studies:
Management
Delivered by:
Department of Marketing
Where:
Graduate School of Business
When:
3 year, 4 module
Mode of studies:
distance learning
Instructors:
Gleb Karpushkin
Language:
English
ECTS credits:
3
Contact hours:
2
Course Syllabus
Abstract
This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will be a tutorial of functionality available in matplotlib and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data.
Learning Objectives
- Explain the specific of each method of data representation.
- Explain how to create Visualization with Python programming language.
- Develop and understand different types of data visualization.
- Develop the fundamental skills of swift data representation.
- Learn to use standard formats, techniques, and documentation to gain credibility in data science setting.
Expected Learning Outcomes
- Describe what makes a good or bad visualization
- Identify the functions that are best for particular problems
- Understand best practices for creating basic charts
- Create a visualization using matplotlb.
Course Contents
- Principles of Information VisualizationIn this module, you will get an introduction to principles of information visualization. We will be introduced to tools for thinking about design and graphical heuristics for thinking about creating effective visualizations. All of the course information on grading, prerequisites, and expectations are on the course syllabus, which is included in this module."
- Basic ChartingIn this module, you will delve into basic charting. For this week’s assignment, you will work with real world CSV weather data. You will manipulate the data to display the minimum and maximum temperature for a range of dates and demonstrate that you know how to create a line graph using matplotlib. Additionally, you will demonstrate the procedure of composite charts, by overlaying a scatter plot of record breaking data for a given year.
- Charting FundamentalsIn this module you will explore charting fundamentals. For this week’s assignment you will work to implement a new visualization technique based on academic research. This assignment is flexible and you can address it using a variety of difficulties - from an easy static image to an interactive chart where users can set ranges of values to be used.
- Applied VisualizationsIn this module, then everything starts to come together. Your final assignment is entitled “Becoming a Data Scientist.” This assignment requires that you identify at least two publicly accessible datasets from the same region that are consistent across a meaningful dimension. You will state a research question that can be answered using these data sets and then create a visual using matplotlib that addresses your stated research question. You will then be asked to justify how your visual addresses your research question.
Assessment Elements
- Group projectIndividual or in a group of 2 students max Project
- Course assessmentsProvided by Coursera course, embedded surveys, tests, quizes
- Exam
Interim Assessment
- Interim assessment (4 module)0.3 * Course assessments + 0.4 * Exam + 0.3 * Group project
Bibliography
Recommended Core Bibliography
- Python 3, Прохоренок, Н. А., 2016
- Легкий способ выучить Python 3, Шоу, З. А., 2019
- Основы Data Science и Big data : Python и наука о данных, Силен, Д., 2017
Recommended Additional Bibliography
- Python и анализ данных, Маккинли, У., 2015
- Элегантный SciPy : искусство научного программирования на Python, Нуньес-Иглесиас, Х., 2018