Data Science Fundamentals
Duration
- One semester or equivalent
Contact hours
- 32 Hours
On-campus unit delivery combines face-to-face and digital learning.
Aims and objectives
This unit will give students a solid foundation in contemporary data science best practices using Python. It will cover a hands-on introduction to programming paradigms and fundamental data analysis techniques. Through examples involving real-world data, students will learn data cleaning and validation techniques, data transformation procedures, algorithm design, text analytics, and data visualisation techniques. Students will become familiar with important Python software modules such as Pandas, Matplotlib, and the Natural Language Toolkit (NLTK).
Courses with unit
Graduate Certificate of Research and Innovation ManagementUnit information in detail
- Teaching methods, assessment and content.
Teaching methods
| Activity Type | Activity | Total Hours | Number of Weeks | Hours Per Week | Optional - Activity Details |
| Face to Face Contact | Computer Laboratory | 32 | 2 weeks | 16 | |
| Unspecified Learning Activities | Independent Learning | 118 | 12 weeks | 9.83 | |
| Total Hours: | 150 | Total Hours (per week): | 25.83 | ||
Please note that due to current situation unit will be scheduled as follows:
Online teaching
2 x 3-hour sessions each week
Assessment
1. Assignment / Workbook (Individual) 10-20%
2. Assignment / Workbook 10-20%
3. Assignment / Workbook 10-20%
4. Assignment / Workbook 10-20%
5. Assignment / Workbook 10-20%
Assignment and Presentation 1 (Individual/Group) 30-50%
Content
- Basic programming theory
- Data science best practices
- Data structures, access and usage
- Data cleaning and validation
- Data Visualisation
- How to validate results
- Working with Text data (Text Analysis)
- Data science tools
Study resources
- Reading materials.
Reading materials
Students are advised to check the unit outline in the relevant teaching period for appropriate textbooks and further reading.