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Machine Learning Engineer
In this course you will learn how to visualize data with Python in an impressive and understandable way.
From the basics with Pandas and Matplotlib to elegant statistical plots with Seaborn and interactive dashboards with Plotly - step by step you will learn the most important tools of modern data visualization.
Discover best practices, create your own visualizations and take your analyses to a new level - in a practical way so that what you learn can be applied directly in everyday life.
Refresh 1: Data analysis with NumPy and Pandas
- Overview: Aim and procedure of the course
- Data analysis with NumPy
- Arrays, data types, indexing
- Relevance for numerical data visualization
- Level-up with pandas
- Series and DataFrames
- Import data (CSV, Excel)
- Applied data exploration (head, describe, info, isnull, value_counts)
- Simple corrections
- Mini-exercise: First analysis of a real data set (e.g. Titanic, Iris)
2. basics of visualization with Matplotlib
- Philosophy of Matplotlib
- Plot types:
- Line, scatter, bar, histogram, boxplot
- Axes, titles, legends, colors & styles
- Subplots and layouts
- Saving plots (PNG, PDF, SVG)
- Mini-exercise: Create different plots with real data
3. data analysis and visualization with Seaborn
- Why Seaborn? High-level vs. low-level APIs
- Plot types:
- countplot, boxplot, violinplot
- histplot, scatterplot
- pairplot & heatmap
- Context analysis:
- Visualize correlations (heatmap)
- Display relational & categorical data
- Design & style customization
- Mini-exercise: Explorative data analysis with Seaborn
4. visual storytelling and best practices
- Difference between explorative & explanatory visualization
- Visual principles:
- Colors - Axes - Scaling - Annotations
- Common pitfalls: What makes a "bad" graphic?
- Introduction to design guidelines (data ink ratio & Tufte principles)
- Mini exercise : Improve bad graphics
5. interactive visualization with Plotly
- Why interactive visualization?
- Introduction to Plotly Express
- px.scatter, px.bar, px.line, px.histogram, px.box
- Interactive features: Zoom, hover, tooltips
- Colors, facets, animations
- Mini exercise: Attractive and interactive dashboards with Plotly Express
6 Plotly Graph Objects & Dash Introduction
- Plotly Graph Objects (GO) vs. Express
- Flexibility and customization
- Introduction to Dash (conceptual)
- Building a simple dashboard
- Layouts and callbacks (demo)
- Mini-exercise: Visualize a small interactive report
7. further tools & special visualizations
- Altair: declarative visualization
- Bokeh: interactive webplots
- Geopandas & Folium: Maps and geographical data
- Wordclouds & Networks
- When to use which tool? Overview & comparison table
8th mini project
- Mini project (individually or in small groups):
- Analyze, visualize and present data
- Use at least two libraries
- Goal: Communicate insights from data visually
- Short presentations & feedback round
- Conclusion: Q&A and tips for further learning
Requirement: Basic knowledge of programming in Python is an advantage
Additional info:
- The following must be installed by the participants in advance:
- Python (at least version 3.10+)
- A Python IDE (e.g. PyCharm or VSCode)
- Sufficient host rights to be able to install packages with pip
- Preparing and analyzing data
- Selecting the right visualization for the respective use case
- Getting to know the range of Python visualizations
- Create professional and comprehensible diagrams
- Developing interactive visualizations
- Best practices for good visual storytelling
This course features a mixture of guided exercises, theory and illustrative examples as well as hands-on scenarios that are adapted, extended or created by the participants themselves. Each chapter concludes with a mini-exercise.
The course ends with an explorative and interactive project work.
This course is aimed at anyone who not only wants to analyze data, but also wants to visualize it convincingly:
- Data analysts and data scientists
- Students and researchers from all disciplines
- analysts analysts and controllers
- developers with an interest in data visualization
- Anyone who works with data and wants to communicate their results in an understandable way
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