Working with Big Data

Washington D.C. campuses

GA Washington D.C. (1776 8th Floor)
1133 15th Street NW, 8th Floor
Washington DC 20005

Past Locations for this Class

Working with Big Data

Washington D.C.

Washington D.C. campuses

GA Washington D.C. (1776 8th Floor)
1133 15th Street NW, 8th Floor
Washington DC 20005

Past Locations for this Class

About this class

Big data is ever-growing in importance, but operationalizing giant sets of data into repositories of easily-accessed insight seems confusing and daunting to most. This brief deep dive into big data seeks to provide you with a better understanding of tools and techniques which help anyone summarize and analyze very large sets of data with elegance and ease.

We’ll begin by setting the stage for Big Data, describing how data sources have proliferated in the past decade, and how analytical tools have changed and evolved to handle these ever-growing data sets. We’ll then dive into a few specific examples of big data analysis from Familian&1’s consulting work, with a focus on ways Splunk and other tools have been used to define and automate a set of big data dashboarding processes. We’ll then quickly cover some advanced data-scoring and segmentation techniques before concluding with a look at a few data visualization platforms that can help accelerate the process of bringing your big data summaries to life without breaking the time bank.

This class is ideal for anyone:

  • Who works with large data sets daily or weekly
  • Responsible for (and frustrated with) recurring reporting on large data sets
  • Responsible for driving standards around data + communication in their organization

Takeaways

  • Learn where to start with analysis of a big data set
  • Understand what analytical techniques to use when working with a large set of data
  • Explore what services to leverage when automating big data analysis + dashboarding

Prereqs & Preparation

  • Basic familiarity with Excel or Google Sheets
  • Basic understanding of CSV and other “flat” file formats
  • Bonus but not required: familiarity with data analysis flows and ETL processes

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