Data Visualization
Learn the fundamentals and get hands-on experience with data visualization, including how to make useful visualizations and dashboards of data, how to help yourself and others to understand and analyse that data, and how to use state-of-the-art tools and techniques
Practical information:
Want to register?
- Register until: 15 Sep 2023
- Prerequisites: proficiency with programming in Python
- Price: €1.800
Georganiseerd door:
Learning goals
- you understand and know the fundamentals of data visualisation
- you get a first-hand experience with data visualisation
- you learn to make useful visualisations and dashboards of data
- you learn to help yourself and others understand and analyse that data
- you learn to use state-of the art tools and techniques
Course format
The course consists of three modules of two sessions each. Namely,
- Data preparation (sessions 1 & 2)
- Data visualization & interactive graphics (sessions 3 & 4)
- Dashboards & structured data (sessions 5 & 6)
Programme
Session 1 (22 September): Data preparation
by Quinten Danneels, dotdash.ai
The basics of data preparation with Python will be covered:
- Common operations & libraries
- Challenges in batch data preprocessing
- Missing or incomplete records
- Outliers or anomalies
- Improperly formatted / structured data
- Inconsistent values and non-standardized categorical variables
- Limited or sparse features / attributes
- Functions of Pandas (or Pyspark)
- Visualisation of data
- Best practices for data cleaning & software engineering
- Readable & documented code
- Logging
- Handling null values
- Data consistency, standardization & documentation
Session 2 (29 September): Advanced data preparation theory
by Quinten Danneels, dotdash.ai
In this session, you will cover more advanced topics.
- Best Practices for Feature Engineering
- Unstructured data
- Common operations
- Data Observability & data quality
- Data profiling
- Unit testing
- Moving to production
- Docker
- Workflow orchestrators (e.g., Dagster, Airflow)
Session 3 (13 October) and session 4 (20 October): Data visualization, tools & libraries
by Katrien Verbert & Aditya Bhattacharya - KU Leuven
In these workshop sessions, you will get an introduction to data visualization for data science, analysis, and storytelling. You will get hands-on experience working with popular Python libraries and standard tools on multiple datasets. The module is divided into the following five segments:
- Introduction to fundamentals of Data Visualization
- Introduction to Data Visualization using Matplotlib, Pandas, and NumPy in Python
- Advanced Data Visualization using Seaborn in Python
- Interactive Data Visualization using Plotly in Python
- Introduction to Data Reporting using Tableau
As a key takeaway from these sessions, along with the theoretical knowledge of data visualization, you will develop hands-on skills working with practical datasets. The code and the datasets will be provided to you and we will use Google Colab to practice the exercises from the first three segments. For the last segment, we recommend you to install Tableau Public.
Session 5 (27 October): Dashboards & linked visualizations
by Jefrey Lijffijt, UGent & VAIA
This session gives a hands-on introduction to dashboards and linked graphics, using Plotly Dash:
- Page layout, dash components, custom components,
- Recap interactive plots,
- Input, output, callbacks,
- Make components respond to each other.
- Examples,
- Integration of server-side computations and algorithms,
- Deployment
Session 6 (10 November): Interactive graphics to explore complex data
by Jefrey Lijffijt, UGent & VAIA
This session covers creating visualizations of structured and complex data, and the integration of these into dashboards:
- Visualization of (large) time series and collections of time series,
- (Choropleth) Maps and their complexities, terrain visualization, Sankey diagram/flow map,
- Visualization using constructed axes: dimensionality reduction/representation learning, visualization of graphs/networks (layout algorithms, chord diagram), image databases.
Seeing is believing: data visualization is key for successful AI implementation
Data may be the new gold, but what do you do with all those insights? A good dashboard, a punchy graph or a great story are essential to ensure that people actually understand, use and follow data insights.
Teachers / speakers
Quinten Danneels
Quinten Danneels is the technical founder at dotdash.ai (a knowledge graph startup), and has data science & engineering experience across various organizations (Bingli, Unilin, Volvo Group, Tomorrowland, Atlas Copco).
Aditya Bhattacharya
Aditya Bhattacharya is a doctoral researcher on Explainable AI at the Augment research group of the Department of Computer Sciences, KU Leuven. He has obtained his Master of Science degree in Computer Science with a specialization in Machine Learning from Georgia Institute of Technology, USA. Before joining KU Leuven, Aditya has worked in multiple roles in organizations like Microsoft and Intel and has worked on multiple AI projects in domains related to Computer Vision, Natural Language Processing, Time Series Analysis, Classical Machine Learning, and Data Engineering.
Jefrey Lijffijt
Jefrey Lijffijt is a professor of Data Science at Ghent University - IDLab and, together with Tijl De Bie , leads the AI & Data Analytics research group. The AI & Data Analytics research group designs, implements, and analyzes algorithms and systems to extract knowledge and insights from data. Nearly all of our tools and articles are open source and open access. Jefrey Lijffijt chairs the AI working group at the Faculty of Engineering and Architecture and is a member of the AI core group at Ghent University. He actively contributes to (Gen)AI education at Ghent University and previously at VAIA.
His expertise includes artificial intelligence, machine learning, knowledge discovery, data visualization, data mining, data exploration, visual analytics, computational complexity analysis, algorithm design, information theory, statistical hypothesis testing, interactivity, and tools and applications. For an overview of recent research, see https://aida.ugent.be/
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