CFI 1071 - Introduction to Big Data Analytics and Security

Credits: 3
Hours/Week: Lecture 2 Lab 2
Internship hours per week 0
Course Description: This course introduces the fundamental concepts in the field of Big Data. The focus of the course is on an overview of the field and related security topics to develop the skills needed to participate effectively in Big Data and other analytics projects as a practitioner. Course activities provide students with opportunities to search, navigate, tag, build alerts, and create simple reports and dashboards with Splunk. The course begins with an introduction to Big Data, which includes grounding in basic analytic methods and Big Data analytics technology and tools, and builds to an exploration of the data analytics lifecycle to address business challenges that leverage Big Data. Course materials include both “open-source technology” and “commercial technology” tools. This course is for those new to the Big Data field and the security threat landscape. No prior programming experience or statistics background is required.
MnTC Goals
None

Prerequisite(s): Course placement into college-level English and Reading OR completion of ENGL 0950 with a grade of C or higher OR completion of RDNG 0940 with a grade of C or higher and qualifying English Placement Exam OR completion of RDNG 0950 with a grade of C or higher and ENGL 0090 with a grade of C or higher OR completion of ESOL 0051 with a grade of C or higher and ESOL 0052 with a grade of C or higher.
Corequisite(s): None
Recommendation: None

Major Content

Introduction to Big Data Analytics
Big Data Overview
State of the Practice in Analytics
The Data Scientist
Data Analytics Lifecycle
Analyzing and exploring public cloud data
Working with Time
Statistical Processing
Correlation Analysis
Introduction to knowledge Objects
Creating Knowledge Objects
Creating Fields Extractions
Data Models
Using Fields
Tags and Event Types
Creating Reports
The Hadoop Ecosystem

Learning Outcomes
At the end of this course, students will be able to:

employ the Data Analytics Lifecycle to address Big Data analytics projects.
explain how to structure data analysis and get values out of Big Data.
describe the landscape of Big Data Analytics by exploring several examples of real-world problems.
explain the impact of Big Data on data collection, data analysis, data reporting, data monitoring, and data storage.
apply appropriate Splunk’s analytic techniques and tools to analyze Big Data.
identify the possible problems that are associated with Big Data.
reorganize the possible problems that are associated with Big Data as data science questions.
install and run programs using current, relevant tools.
build alerts and create simple reports and dashboards with Splunk.
identify the threats affecting Big Data.

Minnesota Transfer Curriculum (MnTC): Goals and Competencies
Competency Goals (MnTC Goals 1-6)
None
Theme Goals (MnTC Goals 7-10)
None

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