1. Monday, January 11, 2016

2.  INSTRUCTORS  STUDENTS:  Name?  Class?  Hometown?  Major?  Background: Math? Computers? Statistics?  Why did you take this class?

3.  Course web site:  http://twig.lssu.edu http://twig.lssu.edu  Prerequisite  Office hours  Textbook  Grades:  Homework/quizzes  Class participation  Final project / presentation  Final Exam

4.  Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate  Three Vs of Big Data (Gartner, 2012) :  Volume: big data doesn't sample; it just observes and tracks what happens  Velocity: big data is often available in real-time  Variety: big data draws from text, images, audio, video; plus it completes missing pieces through data fusion  Next week in HONR101: What does “big” mean? We’ll talk about bits & bytes, data growth, etc.

5.  Correlation – the strength and type of how two data sets are related / depend on each other  What are some examples?  Related to LSSU students?  Clustering – the task of grouping a set of objects into clusters so that objects in the same cluster are more similar to each other than to those in other clusters  What is an example related to LSSU students?

6.  Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events  Applications exist in numerous areas (retail, travel, health care, actuarial science, credit scoring, movies, sports, marketing, financial services, pharmaceuticals, telecommunications, etc.)

7.  Read Chapter 1 of your textbook  Perhaps there will be a surprise quiz next week  Start thinking about term project ideas and possible format