Data Visualization in Python

Overview

This course is designed to teach you how to turn raw data into meaningful, insightful and visually compelling graphics using Python. Whether you’re a data analyst, student, researcher or aspiring data scientist, this course will give you the tools and techniques to effectively communicate data-driven insights.

Requirements

Basic Python programming knowledge is required.

Certificate

Participants receive a certificate of completion of the authorized Softinery course.

Materials and Tools

You will have access to all the course materials: course notes, exercise solutions and presentations. Moreover, you will learn the basics of most important tools used in modern Python applications in engineering and data science.

NumPy

Data Visualization in Python – Syllabus

Why visualize data?
Overview of visualization libraries: strengths and use cases

Line, bar, scatter, and histogram plots
Figure and axis objects
Styling: colors, markers, labels, grids, ticks
Subplots and multi-panel layouts
Saving and exporting plots (PNG, PDF, SVG)

Visualizing distributions, boxplots, violin plots
Categorical data: countplot, barplot
Heatmaps and correlation matrices
Using themes and color palettes

Intro to Plotly Express and graph objects
Interactive scatter, line, and bar charts
Hover labels, zooming, and sliders
Embedding charts in Dash apps

Time-series and sensor data
Dual axes, log scales, scientific units
Error bars, annotations, scientific notation

Create a visual dashboard or report based on participant or provided engineering dataset

Instructor

PhD Eng. Szymon Skoneczny is a former university professor specialized in mathematical modelling. He has also worked for international companies like Electricite de France, Siemens and ArcellorMittal.