Big data and visualization (5 cr)

Objective

You know the meaning of big data and how to use it in data driven decision-making.
You understand how to manage large data sets.
You learn the principles of data visualization.

Content

What is big data?
What is data analytics?
How to visualize essential findings in effective ways?

Course material

The theoretical lectures bring all the necessary learning material to prompt students to actively work on their data assignments and projects. Course materials (slides, readings, sample datasets, and instructions) are made available in the designated learning platform (e.g., Learn) and covered in detail during the lectures.

Study forms and methods

Students should expect a blend of lectures, hands-on exercises, and projects. This course is designed to provide opportunities for students to apply their learning to practical, real-world data visualization challenges. By the end of the course, students will have developed a comprehensive understanding of data visualization principles and the ability to visualize and interpret large datasets effectively.

Throughout this course, students will:

- Gain basic understanding of data analysis and visualization concepts.
- Explore various data collection and processing techniques.
- Learn the importance of data quality through data cleaning and data validation.
- Understand the basics of exploratory data analysis and how to summarize data.
- Learn the principles of effective data visualization and the main visualization tools.
- Address the challenges of visualizing large and real-time datasets.
- Apply their knowledge through hands-on projects that mimic real-world data challenges.

Timing of exams and assignments

Assignments are given throughout the course, covering both individual tasks and group project work. To pass the course, students must:

- Complete all mandatory assignments.
- Participate in group projects and project presentations.
- Submit a final piece of work or exam (if required) that demonstrates their understanding of the concepts.

For detailed information about deadlines and how each task contributes to the final grade, check the “Assessment methods and criteria” section below.

Student workload

The course has 5 ECT (5 credits), i.e. approximately 135 hours of student work. From these, 42 hours are dedicated to contact lectures (14 sessions x 3 hours). The remaining 93 hours are for independent study and project development. Students are advised to allocate time as follows:

- About 30 minutes per lecture for reading, reviewing lecture content, or completing quizzes.
- Approximately 6 hours per lecture block for coding exercises, data analysis, and project work.
- Students should utilize this time to deepen their data analysis skills, prepare visualizations, and complete individual and group assignments.

Course part description

Lecture Topics (These might be subject to changes when needed, this is more for you to have an idea of the contents of the theoretical lectures):

1) Seeing the Unseen: Introduction to Data Analysis
2) The Bread and Butter Tools
3) Data Wrangling
4) Data Visualization & Data Storytelling
5) Theory and Grammar: Basic Plots and Graphs
6) Goal-Based Visualization Choices
7) Controlling Graph Parameters
8) Prettifying Techniques
9) Data Storytelling Workshop
10) Dashboards Workshop
11) Projects Review
12) Machine Learning
13) Big Data Technologies
14) Closing the Course and Exams

Further information

Prior know-how and skills:
No formal prerequisites are required, but familiarity with basic programming principles, spreadsheets, handling data, or a general interest in data analysis will be helpful.

Evaluation scale

1-5

Assessment methods and criteria

Assessment criteria, satisfactory (1-2)

Information-based know-how
- Minimal theoretical knowledge, lacking connections between different topics, and with frequent inaccuracies or misunderstandings.
- Limited ability to find and use information without significant guidance.
- Limited context awareness, often overlooking the target audience or stakeholder needs.

Skills-based know-how
- Partial completion of assigned tasks, with notable inaccuracies or omissions.
- Inconsistent/limited application of basic tools or methods.

Teamwork/know-how/readiness to take responsibility
- Infrequent participation in tasks, requiring prompting to contribute.
- Shows a passive attitude and minimal engagement during the course.
- Takes little responsibility for team outcomes, relying heavily on peers.

Assessment criteria, good (3-4)

Information-based know-how
- Competent use of terminology and core data concepts, with occasional minor errors.
- Demonstrates effective information-search and problem-solving skills with some instructor support.
- Solid theoretical knowledge, applied reasonably well to tasks and projects.

Skills-based know-how
- Completes all assignments, most accurately, with few issues.
- Consistently applies basic tools or methods.

Teamwork/know-how/readiness to take responsibility
- Actively participates in tasks, contributing to discussions and deliverables.
- Maintains a positive and cooperative attitude throughout the course.
- Shares responsibility for outcomes and shows initiative when needed.

Assessment criteria, excellent (5)

Information-based know-how
- Mastery of professional vocabulary, with precise application in various contexts.
- Independently and creatively uses advanced information-search techniques and problem-solving approaches.
- Demonstrates deep theoretical understanding, providing insights beyond the standard course material.

Skills-based know-how
- Completes all assignments with exceptional accuracy and detail.
- Produces and delivers materials with an excellent professional footprint.
- Shows advanced proficiency in the use of tools and methods.

Teamwork/know-how/readiness to take responsibility
- Demonstrates great work in group settings, often taking the lead and fostering collaboration.
- Maintains a highly proactive, inquisitive, and enthusiastic attitude throughout the course.
- Assumes full responsibility for personal and team tasks, consistently exceeding expected outcomes.