Welcome to the Agile Data Science 2.0 Class! During this course we will walk you through how to iteratively create big data applications using agile methods.
The hero's journey is from Joseph Campbell's Hero of a Thousand Faces. It expresses the monomyth, a story that spans all human civilizations in which a hero undergoes a cycle of change and emerges a hero with a boon to bestow unto his society. Agile methods fit with the cycle of the monomyth, and indeed with the nature of reality itself. That is why they work!
Now that we've covered the material on the theory of agile methods for data science, lets get acquainted with our tools! We'll go over the major components of the agile data science stack and introduce you to each one. We'll get started executing code right away!
Check out Chapter 2: Agile Tools
Now check out Part II - Climbing the Pyramid, your introduction to the process of iterative data refinement!
Now we will collect our data and setup plumbing through our software stack onto the web. Check out Chapter 4: Collecting and Displaying Records now!
Now we will start to process our data to extract higher level information through aggregation, visualization and mathematics. Check out Chapter 5: Visualizing Data with Tables and Charts.
Once we have multiple charts on a single web page, when we have multiple entity pages linking together to enable drilldown, when we add search and exploration of data... we arrive at the level of the interactive report. Check out Chapter 6: Exploring Data with Reports.
In the next level of the data value pyramid, we take what we've learned at the lower levels and apply it to creating predictions with our data. Check out Chapter 7: Making Predictions.
Most predictions die in the lab. To ensure this doesn't happen to your predictions, we next deploy a prediction as a full-blown predictive system on the web using Apache Kafka and Spark Streaming. Check out Chapter 8: Deploying Predictive Systems.
When a prediction proves useful, it then becomes your job to improve it. Check out Chapter 9: Improving Predictions.
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