COURSE INFORMATION
Big Data Analytics – An Introduction
Neil Whitehead
Course Description:
What is “Big Data”? In the simplest of terms, it refers to the tools, processes and procedures allowing an organization to create, manipulate, and manage very large data sets and storage facilities. As of the beginning of 2013, the world is creating more data each day, than for the past four decades combined. The process of shifting through such sheer quantities of data proves to be a demanding process for any person to do. Big Data is a new course that encompasses information technology, science and mathematics. This course will focus on the conceptual understanding and the application theory behind Big Data Analytics rather than explicit formulas and technical jargons. The main objective for this course is to create “awareness” and to be exposed to the realm of big data and the hidden dangers it might bring. This course will include some hands on experience utilizing big data analytics to solve some practical real life projects. Upon completion, you will be more aware about this big data phenomenon.
Duration: 1 semester Credit: 0.5 Prerequisite: N/A
Introduction:
Welcome to Big Data Analytics! This is a course that will broaden your horizon and gives you an edge over many high school students. “Facts without Data is Just an Opinion”. I hope you have had a wonderful summer break and hope that you are ready for a unique semester. My goal is to increase your awareness on what Big Data is all about. This course will cover the following topics:
- Introduction & Overview
- Basic Ruby
- The Volume of Data
- The Messiness of Data
- Correlation
- Datafication
- Value in Data
- Implications of Data
- Risks and Control of Data
- Beyond Big Data
Drivers for Big Data
Big Data Analytics Applications
Architecture Components
Advanced Analytics Platform
Implementations of Big Data Analytics
The two textbooks for this course are: Big Data: A revolution that will transform how we live, work
and think by Viktor Mayer-Schönberger and Big Data Analytics: Disruptive Technologies for
Changing the Game by Arvind Sathil.
Expectations
Big Data Analytics – Course Info 2016-2017- 1.docx
Grading Policy approximate:
Quizzes and Badges 25%
Homework and Class Participation/discussion 20%
Projects 30%
Final Project 25%
Homework is due the next day unless informed otherwise. Late work penalty is 10% per day regardless of whether we meet as a class or not, to maximum lateness of 4 days including weekends.
Homework past the 4 th day will receive a ZERO.
Cheating and Plagiarism:
Students caught cheating on an assessment/assignment or have an unfair advantage or in possession of unauthorized assessment material will: be handled in accordance with the student handbook
Absences and Tardies:
It is essential to come to class every day on time. Tardies are very disruptive. Missing more than two days per quarter will put you significantly behind. Please note the complete policy, which is published on school’s handbook.
Mr. Whitehead