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Practicing Data science indeed a long term effort than a learning handful of skills. We ought to be academically good enough to take up this challenge. However, if you think you came a long way from your academic rebuilding, but you still have that zeal & passion to take the oil from the data and fill the skill gap of data science then here is the warm-up tips. Below points must exercised before jumping into any data science & data mining problems:
Not all datasets are in the form of a data matrix. For instance, more complex datasets can be in the form of sequences, text, time-series, images, audio, video, and so on, which may need special techniques for analysis. However, in many cases even if the raw data is not a data matrix it can usually be transformed into that form via feature extraction. A practical example of feature example is explained in my last post on scikit-learn library.
" In fact, data mining is part of a larger knowledge discovery process, which includes pre-processing tasks like data extraction, data cleaning, data fusion, data reduction and feature construction. As well as post-processing steps like pattern and model interpretation, hypothesis confirmation and generation, and so on. This knowledge discovery and data mining process tends to be highly iterative and interactive. "
CRUX: The algebraic, geometric & probabilistic viewpoints of data play a key role in data mining. You should exercise them beforehand. So you can easily sail though your boat in Data Science !
Original post: http://datumengineering.wordpress.com/2013/10/18/warm-up-exercise-b...
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