Data Processing and Manipulation

The "Data Processing and Manipulation" course provides students with a comprehensive understanding of various data processing and manipulation concepts and tools. Participants will learn how to handle missing values, detect outliers, perform sampling and dimension reduction, apply scaling and discretization techniques, and explore data cube and pivot table operations. This course equips students with essential skills for efficiently preparing and transforming data for analysis and decision-making.

• Understand the importance of data processing and manipulation in the data analysis pipeline.

• Learn techniques to handle missing values in datasets, including imputation and exclusion strategies.

• Identify and detect outliers to assess their impact on data analysis and decision-making.

• Explore sampling methods and dimension reduction techniques for large datasets and high-dimensional data.

• Apply data scaling techniques to normalize and standardize variables for meaningful comparisons.

• Utilize discretization to transform continuous data into categorical representations, simplifying analysis.

• Understand the concept of data cube and perform multidimensional aggregation for exploratory analysis.

• DAC1 L1: You apply basic techniques to transform data into information for your audience.

• DAC1 L2: You apply a range of techniques to transform data into valid and purposeful information.

• DAC2 L1: You recognise basic issues of data quality and can take action with guidance to prevent or counteract them.

• DAC2 L2: You can identify a broad range of data quality issues and perform data cleansing and consistency checks.

• DAC2 L3: You understand and can communicate the limitations of the data and how it can be enriched to deliver more relevant information.