FI2BCIT75 Industry Tools
FI2BCIT75 Industry Tools
- Course description
- Course codeFI2BCIT75
- Level of study5.1
- Program of studyData Analyst 1
- Credits7.5
- Course coordinatorBertram Haskins, Alec Du Plessis
This course introduces candidates to bespoke data tools relevant to the data analysis industry. Candidates will be taught two mutually exclusive tools over two weeks each, and candidates will be allocated additional time to self-study similar tools within those fields. This course supplements the candidate’s technical aptitude for performing data analysis by exposing them to real-world software that traditionally gets trained in the first phase of employment. Tool-specific interests from industry partners include exposure to SPSS, PowerBi, Qlik, Grafana, and Tableau. However, not every tool will be covered since they share similar functionality. Additionally, open-sourced tools and product integration concepts will be taught to candidates.
This course serves as a platform to teach two additional industry tools to candidates. These tools greatly improve candidates’ ability to grasp work environments within the data ecology. This course is designed to be modular in nature, allowing for flexibility in the current desired tool market and leaving enough time to introduce candidates to the most relevant and up-to-date bespoke data tool at the time.
The candidate:
- has knowledge of industry-desired commercial and open-sourced data tools that are used to solve or contribute to a data analysis project
- has knowledge of processing gaps that fall outside the ability of standard tools, such as cluster modelling and kernel density predictors
- has insight into own opportunities of product integration techniques to merge the output of one tool into the input of other tools
- has insights into own opportunities to investigate new industry-relevant specialist tools used within the field of data analysis
The candidate:
- can explain vocational choices for industry-desired data tools that play key roles within the data analysis lifecycle
- can reflect on own approach to solving data problems using the appropriate industry tool for the job and adjust it under supervision
- can find and refer to information about the latest commercial and open-source data analysis tools
The candidate:
- can exchange points of view with their peers and participate in discussions about industry-relevant and efficient tools used in the field of data analysis
- can contribute to organisational development by choosing appropriate industry-relevant tools to solve a specific task
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.
Teaching materials, reading lists, and essential resources will be shared in the learning platform and software user manuals where applicable.
| Form of assessment | Grading scale | Grouping | Duration of assessment |
|---|---|---|---|
Course Assignment | Pass / Fail | Group/Individual | 1 Week(s) |