Frequently Asked Questions
General Questions
What is Data Visualization?
Data visualization is the graphical representation of information and data using visual elements like charts, graphs, and maps. It helps communicate complex data insights clearly and effectively to various audiences.
Do I need programming experience?
Basic programming knowledge is helpful but not required. We’ll cover R and Python fundamentals in the first week. The course is designed to accommodate beginners while challenging advanced students.
Which software should I use - R or Python?
Both are excellent choices. R has powerful visualization packages (ggplot2, plotly), while Python offers great libraries like matplotlib, seaborn, and plotly. We’ll teach both and you can choose based on your preference.
Course Logistics
How much time should I dedicate to this course?
Expect to spend 8-10 hours per week: - 3 hours: Lectures and tutorials - 3-4 hours: Readings and videos - 2-3 hours: Assignments and practice
Can I audit the course?
Please contact the instructor to discuss auditing options. Priority is given to enrolled students.
Are lectures recorded?
Yes, all lectures are recorded and available on Moodle within 24 hours. However, live attendance is strongly encouraged for interactive elements.
Technical Questions
What computer specifications do I need?
- RAM: 8GB minimum (16GB recommended)
- Storage: 10GB free space
- OS: Windows 10+, macOS 10.14+, or Linux
- Internet connection for package downloads
How do I install the required software?
Detailed installation guides are provided in Week 1 materials. We also offer installation support sessions.
Can I use cloud-based tools instead?
Yes! Options include: - Google Colab (Python) - RStudio Cloud - Binder for Jupyter notebooks
Assessment
Can I work with others on assignments?
- Mid-term project: Yes (teams of 3-4)
- Final project: No (individual work)
- Weekly exercises: Discussion encouraged, but submit your own work
What if I submit late?
Late submissions receive a 10% penalty per day. Extensions may be granted for documented emergencies.
How are projects graded?
Projects are evaluated on: - Technical correctness (30%) - Analytical depth (30%) - Business relevance (30%) - Presentation quality (10%)
Career & Applications
How is data visualization used in industry?
Common applications include: - Business intelligence dashboards - Financial reporting - Marketing analytics - Customer insights - Performance monitoring - Strategic decision-making
What career paths benefit from data visualization skills?
- Data Scientists
- Business Analysts
- Marketing Analysts
- Data Analysts
- Management Consultants
- Product Managers
Will this help with my dissertation/thesis?
Absolutely! Data visualization is essential for presenting research findings effectively. We can discuss how to apply these methods to your research interests.
Getting Help
I’m struggling with the material. What should I do?
- Attend office hours
- Post questions on Moodle forums
- Form study groups with classmates
- Review recorded lectures
- Schedule one-on-one meeting with instructor
Where can I find additional practice problems?
- End of chapter exercises in textbooks
- GitHub repository practice sets
- Online courses (Coursera, DataCamp)
- Kaggle competitions
What if I have accessibility needs?
Please contact Student Services and the instructor as soon as possible. We’ll work together to ensure appropriate accommodations.