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what variables and relationships would you be interested in discovering by conducting statistical analyses with the data?

what variables and relationships would you be interested in discovering by conducting statistical analyses with the data?

what variables and relationships would you be interested in discovering by conducting statistical analyses with the data?

Topic: Dream Data

examples below. on what variables and relationships would you be interested in discovering by conducting statistical analyses with the data? What would you search for?

  • Example 1:  You are given the full search records from Google for one day. You have Geo-IP info and can identify unique IPs. 
  • Example 2:  Snapshot of profile fields for all Facebook users.
  • Example 3:  All phone records from the main drug cartels for a one month period. Who called whom, duration of call. 
  • Follow the APA style formatting
  • Double spaced 

  • Length: two-page plus works cited page
  • Properly cite in-text sources 

Some notes to help below: 

We may not realize it but today as marketers, advertisers, researchers, communicators, companies, etc., Big Data plays a key role in almost every aspect of our daily activities, and is a great tool when needing to determine trends based on predictive and behavior analytics. Big data helps track consumer behavior, which in turn affords many opportunities to strategize and set objectives, in order to reach desired goals. Our mobile devices, for example, play a key role when it comes to data gathering because of their information-sensing internet of things (IoT) capabilities. Scientifically speaking, big data refers to a field that extracts and analyzes information that might normally be too large or complex in nature for typical software programs.

As with most research, there are pros and cons. 

Big Data Pros 

  • Better decision-making
  • Increased productivity
  • Reduce costs
  • Improved customer service
  • Fraud detection
  • Increased revenue
  • Increased agility
  • Greater innovation
  • Faster speed to market

Big Data Cons 

  • Need for talent
  • Data quality
  • Need for cultural change
  • Compliance
  • Cybersecurity risks
  • Rapid change
  • Hardware needs
  • Costs
  • Difficulty integrating legacy system

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