FI2BCPA05 Programmatic Data Analysis
FI2BCPA05 Programmatic Data Analysis
- Course description
- Course codeFI2BCPA05
- Level of study5.2
- Program of studyData Analyst 2
- Credits5
- Course coordinatorBertram Haskins, Alec Du Plessis
This course delves into the use of software libraries to support the process of data analysis. Candidates will further expand their understanding of programming concepts and integrate it with their knowledge of data analysis fundamentals by using data libraries, such as NumPy, SciPy, and pandas, to perform programmatic data analysis. Candidates will learn how to apply APIs to these libraries' built-in data row-column-based data structures. Candidates will learn how to programmatically create visualisations, using tools such as seaborn and matplotlib to create graphs programmatically directly into documentation.
This course extends the knowledge learned in Programming Fundamentals with Python into the use of data tools through the use of data scripting. Candidates will learn to use industry-desired data tools within the Python 3 programming language. Candidates can import data from multiple sources using pandas, perform data cleaning and correction using NumPy, and correlation analysis using SciPy. Additionally, candidates will apply their knowledge of data visualisation through Python-specific visualisation libraries. This course intends to teach all the programmatic data techniques found in similar suites, such as R but in an environment known to candidates.
The candidate:
- has knowledge of industry-relevant data tools that are available for use within the selected programming language
- has knowledge of theories, techniques and methods that used in external data manipulation libraries
- has knowledge of concepts, techniques and methods that are used in external data visualisation libraries such as Matplotlib, and Seaborn
- is familiar with data preparation and how to pre-emptively identify and remove data errors using the appropriate technique
- has insight into own opportunities for further development in programming
The candidate:
- can explain vocational choices of external data tools with data analytical methods to create robust data reports
- can explain vocational choices of external visualisation tools to programmatically create graphs and other visual indicators
- can explain vocational choices of corresponding tools given any data project outline
- can reflect on own use of key external data analytical tools to perform data analysis in Python and adjust it under supervision
- can find and refer to information about additional external Python data libraries and assess them to keep up to date with the latest analytical solutions
The candidate:
- can plan and carry out effective work methods to streamline data analysis pipelines using programmatic problem-solving techniques alone or as part of a group and in accordance with ethical requirements and principles
- can exchange points of view with others with a background in the data analysis field and participate in discussions about programmatic problem-solving questions and the newest industry-relevant external Python tools
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) |