Just because we have amassed a huge amount of data doesn’t mean that we really understand what it is telling us. To move from raw data to actionable information, we frequently must use algorithmic techniques. However, the ever-growing range of available algorithms and the confusing landscape of algorithmic technologies can make it hard select, apply, and integrate algorithmic intelligence into your data analysis pipeline.
In this course, we will develop a comprehensive framework with which to categorize and assess algorithms, look at a number of real-world case studies in which algorithms dramatically changed the ways in which visualization was leveraged, and review a few technical design patterns for how to insert algorithmic layers into traditional data visualization technology stacks. We’ll finish with a discussion of the human factors behind data-driven decision making and give attendees an opportunity to apply what they have learned through a team exercise on a hypothetical use-case.
You Will Learn
- A generalizable framework for categorizing algorithms and understanding their applicability to different datasets and use-cases
- How to improve data-visualizations by using algorithms for data preparation, hypothesis generation, and prediction
- The pros and cons of different technologies and how they can work together, including R, Python, Hadoop, Spark, graph databases, analytics databases, and third-party APIs
- How algorithms can be used for normalization and imputation of missing data
- How Fischer’s test can allow new ways to visualize consumer buying habits
- How clustering algorithms can lead to interactive hypothesis generation tools
- How social network analysis can be used on data that has nothing to do with being social
- How visualizing decision trees can allow you to collaborate with the computer to find trends in the data easily missed with visualization alone
- How unstructured text can be incorporated into predictive models
- A new way to think about communicating your data insights
- Data analysts, business analysts, business intelligence professionals, analytics professionals, data scientists, and data visualization practitioners; developers or architects responsible for integrating disparate technologies; anyone responsible for finding and communicating knowledge derived from data