Course Description

This course covers the fundamental concepts of data visualization, focusing on performance dashboards. Students will learn to design dashboards, explore the technologies involved, and understand the requirements to build effective dashboards. By the end of the course, students will have gained essential knowledge in performance dashboard design and the skills to develop and implement dashboards that meet specific needs.

Topics

  1. Introduction to driving change through insight
  2. Why tell stories with data?
  3. The Psychology of Data Storytelling
  4. The Anatomy of a Data Story
  5. Data: The Foundation of Your Data Story
  6. Narrative: The Structure of Your Data Story
  7. Visuals (Part 1): Setting the Scenes of Your Data Story
  8. Visuals (Part 2): Polishing the Scenes of Your Data Story
  9. Crafting Your Own Data Story

Introduction to Tableau: Learning Outline

  1. Getting Started with Tableau
  • Introduction to Data Visualization: Importance and benefits, Common use cases.
  • Overview of Tableau Software: Tableau product suite (Tableau Desktop, Tableau Server, Tableau Online, Tableau Public), Installation and setup
  1. Understanding the Tableau Interface
  • Navigating the Tableau Workspace: Data pane, Sheets (Worksheets, Dashboards, Stories), Toolbar and menu options.
  • Key Terms and Concepts: Dimensions and Measures, Discrete vs. Continuous fields, Aggregation and granularity
  1. Connecting to Data
  • Data Sources and Connections: Types of data connections (Excel, CSV, databases, cloud services), Live vs. Extract data connections
  • Data Preparation and Transformation: Data Interpreter, Data types and field roles, Joining and blending data, Pivoting and splitting data
  1. Creating Basic Visualizations**
  • Types of Charts and Graphs: Bar charts, Line charts, Pie charts, Scatter plots, Maps
  • Building Basic Views: Drag-and-drop functionality, Adding dimensions and measures, Sorting and filtering data
  • Customizing Visualizations: Formatting options (color, size, labels), Using marks card (color, size, label, detail, tooltip)
  1. Working with Data
  • Sorting and Filtering: Basic and advanced sorting, Filters on dimensions and measures, Context filters
  • Grouping and Hierarchies: Creating groups and sets, Building and using hierarchies
  • Calculated Fields: Basic calculations, String and date calculations, Logical calculations (IF, CASE)
  1. Advanced Visualization Techniques
  • Dual-Axis and Combined Charts: Creating dual-axis charts, Synchronizing axes
  • Heat Maps and Highlight Tables
  • Using Parameters: Creating and using parameters, Parameter actions
  • Reference Lines and Bands: Adding reference lines, Reference bands and distributions
  1. Building Dashboards and Stories
  • Creating Dashboards: Adding and arranging sheets, Dashboard objects (text, images, web, filters), Interactive elements (actions, filters)
  • Design Best Practices: Layout and formatting, Ensuring interactivity and usability
  • Developing Stories: Creating story points, Navigating through stories
  1. Sharing and Publishing Work
  • Exporting Views and Data: Exporting images and data, Printing options
  • Publishing to Tableau Server and Tableau Public: Steps to publish, Managing permissions and access
  • Sharing Workbooks and Dashboards: Creating packaged workbooks, Embedding visualizations in web pages
  1. Tips and Best Practices
  • Performance Optimization: Extracts vs. live connections, Reducing dashboard load times
  • Maintaining Data Accuracy: Avoiding common pitfalls, Validating data sources
  • Staying Updated: Tableau community and resources, Regular updates and new features
  1. Resources for Further Learning
  • Official Tableau Resources: Tableau training videos, Tableau forums and community
  • Books and Online Courses: Recommended books, MOOCs and online courses
  • Practice Datasets and Projects: Public datasets for practice, Participating in Tableau Public challenges

Textbooks and Other Resources

Supporting Software

Data

Students may use the following dummy data to practice and develop their dashboards. Kaggle provides a wide range of datasets that you can use for your project of building a dashboard. Some notable datasets are the following.

  • Titanic: Machine Learning from Disaster. The Titanic dataset provides information on the passengers who were on board the Titanic when it sank. This dataset is commonly used for exploratory data analysis and data visualization, as it includes various features like passenger class, age, sex, and survival status.
  • COVID-19 Dataset. This dataset contains detailed information about the COVID-19 pandemic, including daily reports of cases, deaths, and recoveries across various countries. It's great for visualizing trends and patterns in the spread of the virus.
  • Iris Dataset. The Iris dataset is a classic dataset for data visualization. It contains measurements of various attributes of iris flowers from three different species. It's often used to illustrate basic concepts in data visualization and machine learning.
  • World Happiness Report. This dataset includes the results of a global survey measuring how people evaluate their own lives in different countries. It provides a wealth of information for visualizing global happiness and its determinants.
  • Netflix Movies and TV Shows. This dataset contains information about Netflix movies and TV shows, including titles, genres, release dates, and more. It's great for visualizing trends in entertainment content.
  • New York City Airbnb Open Data. This dataset includes detailed information about Airbnb listings in New York City, such as location, price, and availability. It's useful for visualizing trends in the short-term rental market.
  • Global Terrorism Database. The Global Terrorism Database (GTD) includes information on terrorist events around the world from 1970 through 2017. It's valuable for visualizing trends and patterns in global terrorism.
  • Zomato Restaurants Data. This dataset contains information about restaurants listed on Zomato, including names, ratings, locations, and cuisines. It's excellent for visualizing trends in the restaurant industry.
  • Global Superstore Dataset. This dataset contains sales data from a global superstore, including details on orders, products, and customers. It's perfect for visualizing sales performance and customer behavior.
  • Olympic History Dataset. This dataset covers the history of the modern Olympic Games, including details on athletes, events, and results. It's ideal for visualizing trends and achievements in the Olympics.

Projects

These videos are produced by Business-Engineering’s Students of Binusian 2026.

Winston Khogres (2602242460)

[Last updated 15 August 2024 by Prof. Dr. Eng. Fergyanto E. Gunawan]