How to do it...

The following are the five key steps for data analysis:

1. Acquisition: The first step in the pipeline is to acquire the data from a variety of sources, including relational databases, NoSQL and document stores, web scraping, and distributed databases such as HDFS on a Hadoop platform, RESTful APIs, flat files, and hopefully this is not the case, PDFs.
2. Exploration and understanding: The second step is to come to an understanding of the data that you will use and how it was collected; this often requires significant exploration.
3. Munging, wrangling, and manipulation: This step is often the single most time-consuming and important step in the pipeline. Data is almost never in the needed form for the desired analysis.
4. Analysis and modeling: This is the fun part where the data scientist gets to explore the statistical relationships between the variables in the data and pulls out his or her bag of machine learning tricks to cluster, categorize, or classify the data and create predictive models to see into the future.
5. Communicating and operationalizing: At the end of the pipeline, we need to give the data back in a compelling form and structure, sometimes to ourselves to inform the next iteration, and sometimes to a completely different audience. The data products produced can be a simple one-off report or a scalable web product that will be used interactively by millions.