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In this course an overview is given of different phases of the data analysis pipeline using Python and its data analysis ecosystem. What is typically done in data analysis? We assume that data is already available, so we only need to download it. After downloading the data it needs to be cleaned to enable further analysis. In the cleaning phase the data is converted to some uniform and consistent format. After which the data can, for instance, be
The point is to manipulate the data into a form that enables discovery of relationships and regularities among the elements of data. Visualization of data often helps to get a better understanding of the data. Another useful tool for data analysis is machine learning, where a mathematical or statistical model is fitted to the data. These models can then be used to make predictions of new data, or can be used to explain or describe the current data.
- combined or divided into smaller chunks
- grouped or sorted,
- condensed into small number of summary statistics
- numerical or string operations can be performed on the data
The point is to manipulate the data into a form that enables discovery of relationships and regularities among the elements of data. Visualization of data often helps to get a better understanding of the data. Another useful tool for data analysis is machine learning, where a mathematical or statistical model is fitted to the data. These models can then be used to make predictions of new data, or can be used to explain or describe the current data.
