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Big Data Analytics

Data Analysis for IT & Cyber Professionals

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  • Overview

    This course is offered in a number of variants, each of which focuses on data within a specific industry or domain (e.g. finance, health care, marketing, and IT/Cyber). The IT/Cyber course focuses on the analysis of data within an enterprise IT infrastructure, to be analyzed for the purposes of monitoring the health and security of operational systems and networks; to detect threats or breaches of systems and networks; penetration testing; forensic analysis; and incident response.

    The target audience for the IT/Cyber data analysis course includes system and network administrators, IT support engineers, systems engineers, systems analysts, information / network security staff, penetration testers, CND / Cyber Operations staff, forensic analysts, incident responders, and security auditors.

    This course focuses on the processes, methodologies, and concepts used in data analysis. It is not a course on the use of specific products or toolsets. The purpose of this course is for the student to understand the analysis of data in its raw form as collected from the original sources. Real world data sets are utilized in lab exercises. Various operating system utilities and open source tool are utilized to extract, manipulate, and analyze data. By understanding data analysis at this level, students will better understanding how to properly utilize enterprise grade systems and network management, penetration testing, and forensic tools.

    Upon completion of this course, students should be able to:

    • Define the goals of an analysis of data
    • Develop a plan for analyzing data
    • Identify data sources of interest
    • Access and collect raw data of interest
    • Process and structure raw data into formats that enable effective analysis.
    • Query, aggregate, and manipulate data to gain insights and/or make conclusions.
    • To refine data collection, manipulation, and analysis processes.
    • To communicate the results of data analysis to co-workers and decision makers.
  • Who Should Take This Course


    • Students must have an understanding of the common devices (PCs, servers, switches, routers and firewalls) and protocols (TCP/IP, ssh, http(s), ftp(s), smtp, SMB) used in modern enterprise IT environments
    • CompTIA Network+ and Security+ certification, or equivalent skills and experience
    • Working knowledge of the Linux shell, command line utilities, and file system fundamentals
    • Linux shell (bash), Perl, or Python programming experience
    • Training and/or work experience in Windows and Linux system administration is recommended
  • Schedule
  • Course Outline

    1. Core Concepts and Methodology

    • What do I want to know?
    • What useful data is available?
    • Where do I look for data?
    • How do I get the data?
    • What do I do with the data?
    • How do I analyze the data?
    • How do I interpret the data?
    • How do I communicate the results?

    2. Planning

    • Develop an analysis game plan
    • It will be refined as you understand the data better
    • Collecting the data –
    • where is it?
    • How and when can I get it?
    • What is the format?
    • What fields are available
    • What is the granularity
    • What is the time frequency
    • Can I tune the data collection to better support my analysis

    3. IT / Cyber Focus

    • What systems, servers, devices, tools produce data of interest? A lot!
    • Let’s ask some questions:
    • Is that server healthy?
    • Is that user’s laptop compromised?
    • Who is doing what on my network?

    4. Parsing, Processing, and Formatting Structured Data

    • Tools and techniques
    • Where should I put my processed data
    • Example data sources and sets:
    • GNU utilities
    • Regular expressions
    • Text files (data exports, structured log files)
    • SQL database (application databases)
    • XML configuration files
    • Device (e.g. router) configuration files
    • Windows Registry
    • Linux /proc
    • Toolset (e.g. nmap

    5. Parsing, Processing, and Formatting Unstructured Data

    • Tools and techniques
    • Where should I put my processed data
    • Example data sources and sets
    • Staging, rolling up
    • GNU utilities – e.g. grep, find, cut, sort
    • Regular expressions
    • OS utility output (e.g. top, tcpdump, netstat)
    • Linux OS log files
    • Services (e.g httpd, samba, mysql) log files
    • Device log files
    • User Files (e.g. office documents)
    • Speciality data – binary files, images, etc.

    6. Working With a Single Source

    • Working with test data to see what I can learn for a given source
    • How can I massage it
    • Fine tune collection
    • Timing and automation of collection and processing
    • Making inferences and conclusions from 1 source

    7. Working with Different Sources and Formats, Levels of Detail

    • Multiple sources
    • Integrating the sources
    • Common keys
    • Common database
    • Staging, rolling up

    8. Data Quality, Integrity and Reliability

    • Assessing quality
    • Assessing integrity
    • Assessing reliability
    • Correcting data

    9. Queries, Reports, Aggregations

    • Making decisions
    • Testing validity
    • Basic statistics concepts

    10. Tools

    • Using Excel to massage and analyze data
    • Using SQL to manage and analyze data
    • Formatting data
    • Summarizing and drawing inference
    • Automating
    • Basic statistical concepts
    • Reporting and presentation tools
    • Data Visualization

    11. Labs / Case Studies

    • Packet Capture
    • Firewall logs & reports
    • Router logs & reports
    • Port scans
    • Linux log files (system, ssh, httpd)
    • Windows event logs
    • Fielsystem scanning
    • Access logs (system, sql, servers)
    • Audit

    12. Capstone

  • FAQs
    Is there a discount available for current students?

    UMBC students and alumni, as well as students who have previously taken a public training course with UMBC Training Centers are eligible for a 10% discount, capped at $250. Please provide a copy of your UMBC student ID or an unofficial transcript or the name of the UMBC Training Centers course you have completed. Online courses are excluded from this offer.

    What is the cancellation and refund policy?

    Student will receive a refund of paid registration fees only if UMBC Training Centers receives a notice of cancellation at least 10 business days prior to the class start date for classes or the exam date for exams.

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