FI1BBDF05 Data Analysis Fundamentals
FI1BBDF05 Data Analysis Fundamentals
- Course description
- Course codeFI1BBDF05
- Level of study5.1
- Program of studyData Analyst 2
- Credits5
- Course coordinatorBertram Haskins, Alec Du Plessis
This course delivers an introductory overview of Data Analysis. It provides the foundational material required to build a strong theoretical understanding of why data analysis is required in industry and how using analytics tools can shape decision making in the real world. Using case studies, candidates will investigate the history and importance of data analysis, allowing candidates the opportunity to explore where the techniques can be applied to their bespoke field of interest or expertise. Candidates will learn how to identify problems, where to apply data gathering techniques, how data will be tabulated and managed, and what results the data analysis evaluations intend to achieve.
This course creates a foundation understanding data, including the evolution and history of it. Students are introduced to the impact of how analysis has been used to shape the outcomes of decision making in the real world. It is important that key concepts such as data integrity, ethically sourced medium, and GDPR standards are explained early in the candidate’s career so that their actions going forward reflect these practices passively.
The candidate:
- has knowledge of the history of data and data sources
- has knowledge of the significance of data in the real world
- has introductory knowledge of business intelligence and big data
- has insights into data strategies, specifically exploration, visualization, trends and estimates
- understands the importance of data warehouses, data silos, and open data platforms
The candidate:
- can apply knowledge of problem division and solving into each stage in the data lifecycle
- can apply theoretical data analysis strategies into simulated and proxy real world scenarios
- can find information relevant to problem scenarios and suggest several applicable data analysis strategy solutions
- can identify where data can be collected first-hand and where to source from alternative medium
- masters online data collection tools such as Google Forms, or printed hand-outs
- can identify and source data ethically with GDPR standards in mind
The candidate:
- understands the ethical principles required for a successful data analysis project
- understands the ethical principles of collecting and maintaining ethically sourced data
- can carry out data strategies from proxy real world scenarios sourced from industry
- can develop their terminology used within data analysis industry
In this course, the following teaching and learning methods can be applied, but are not limited to:
- Lecture: Educator-led presentations or activities providing knowledge, skills, or general competencies in the subject area.
- Group work: Collaborative activities where students work together to solve problems or complete tasks.
- Tutoring: One-on-one or small group sessions with an instructor for personalized guidance and support.
- Student presentations: Opportunities for students to demonstrate their understanding of course material by presenting to peers.
- Online lessons: Digital content delivered via an online learning platform.
- Guidance: Individualized advice and direction from instructors to support students in their learning journey.
- Workshops: Practical sessions focused on hands-on application of theoretical concepts or skills.
- Self-study: Independent study where students engage with course material on their own without any teacher support.
| Form of assessment | Grading scale | Grouping | Duration of assessment |
|---|---|---|---|
Course Assignment | Pass / Fail | Individual | 1 Week(s) |
Teaching materials, reading lists, and essential resources will be shared in the learning platform and software user manuals where applicable.