From Chaos to Clarity: Data Wrangling for Analysis and Visualisation (Classroom and Asynchronous e-learning)
About This Course
This module equips participants with the essential skills to clean, transform, and integrate data, extract meaningful insights, and create compelling data visualisations. It builds on foundational Python knowledge, improving participants’ coding proficiency for more efficient and effective programming. Participants will also learn to leverage on various Python packages to bring data to life and confidently make data-driven decisions.
What You'll Learn
1. Be comfortable using the command line to perform basic tasks.
2. Set up separate Python environments, updating packages.
3. Distinguish between the different types of data such as qualitative and quantitative and map it to the appropriate Python data type.
4. Define and identify possible biasness in any given data.
5. Accurately interpret and use any official Python packages technical documentations.
6. Identify possible data quality issues and its remediation.
7. Apply good Python coding practices to clean data.
8. Demonstrate the use of Regular Expression to handle various text patterns.
9. Apply appropriate techniques to clean sequential data.
10. Implement Python functions and classes to enhance code quality making it maintainable and reuseable.
11. Generate numerical summaries of the data and provide basic interpretations.
12. Construct various graphical displays of the data and provide basic interpretations.
Entry Requirements
At least a Higher Nitec qualification. Basic English Proficiency, foundational numeracy skills and familiarity with using digital tools such as Excel. Prior experience in programming or data analysis is advantageous but not mandatory. Participants without the necessary qualification can be considered on a case-by-case basis.