Data Science Fundamentals
- One semester or equivalent
- 32 Hours
On-campus unit delivery combines face-to-face and digital learning.
2023 teaching periods
HB5 HE Block 5
Last self enrolment:
Last withdraw without fail:
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 unitGraduate Certificate of Research and Innovation Management
Unit information in detail
- Teaching methods, assessment and content.
Number of Weeks
Hours Per Week
Optional - Activity Details
Face to Face Contact
Unspecified Learning Activities
Total Hours (per week):
Please note that due to current situation unit will be scheduled as follows:
2 x 3-hour sessions each week
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%
- 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
- Reading materials.
Students are advised to check the unit outline in the relevant teaching period for appropriate textbooks and further reading.