An Introduction to Notebooks

When you hear the word 'notebook' maybe you think of a notepad or a laptop. Increasingly, the word brings to mind a web application that contains all of your code, text, and visualizations for a particular data intensive project — all within one interface. Notebooks — particularly open-source iPython notebooks — are becoming a power tool of choice for data scientists doing analytics.

You already know that data exploration and analysis is a repetitive, iterative process, but in order to meet business demands, data scientists don't always have the luxury of long development cycles. Notebooks are the key to speeding up the process of trying out data models and frameworks and testing hypotheses, enabling data science teams and their business counterparts to work quickly, iteratively, and collaboratively. Some of the chief use cases for notebooks are data diagnosis, simulation, statistical modeling, and machine learning.

If you’re new to notebooks, this whitepaper offers a great introduction — and if you're interested in getting started with notebooks, check out the IBM Data Science Experience.

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