Students earning their BS in Computer Science with an Emphasis in Big Data Analytics explore the creative applications of software development, utilizing critical thinking and practical project experience. Big data refers to large amounts of data that can inform analysts of trends and patterns related to human behavior and interactions. There are four major components of big data.

Table of Contents:

• Volume
• Veracity
• Velocity
• Variety

Veracity relates to how reliable data is. An analyst wants to ensure that the data they look at is valid and comes from a trusted source. This is determined by where the data comes from and how it is collected. Data collected from native sites rather than third-parties is necessary for reliable results. Additionally, testing measures must be properly designed to ensure that data results in the desired information and is not extraneous.

Variety refers to how many points of reference are used to collect data. If data is collected from a single source, that information may be skewed in some ways. It will not represent a broad population or wide trend. In some cases, like with velocity, that is fine. A pet microchipping service, for example, may only want to target data from a neighborhood social networking site. A movie company, on the other hand, may want to target several social media sites and people of various age groups. So they would need more points of reference to decide on the best places to do business.

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Velocity in big data refers to how fast data can be generated, gathered and analyzed. Big data does not always have to be used imminently, but in some fields, there is a great advantage to receiving up to the second information about rates and being able to act accordingly. In other businesses, the data trend over time is more important to help make predictions or solve lingering problems.

Volume refers to how much data is actually collected. An analyst must determine what data and how much of it needs to be collected for a given purpose. To imagine the possibilities, consider a social media site where people write updates, like photos, review business, watch videos, search for new items and interact in some way with just about everything they see on their screens. Each of these interactions generates data about that person that can be fed into algorithms.