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Big Data Analytics

Introduction to Cloud, AWS and Data Analytics

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  • Overview

    The purpose of this course is for a student to get a broad familiarity with the relevant concepts of data analytics and data science and how they are applied to a wide range of business problems. It also provides an overview of Enterprise Cloud Computing. It is aimed at a broad audience including technology managers. Cloud computing models are discussed, including public, private and hybrid clouds with a focus on Amazon AWS. Important issues such as Compliance, Security and migration are discussed.


    Date: 9/17/19 – 9/19/19
    Time: 9:00am – 4:30pm
    Days: Tuesday – Thursday
    Location: Columbia
    Price: $1235.00


  • Who Should Take This Course


    This course is suitable for: a general audience including business people, managers, system
    architects/engineers, business/systems analysts, and data scientists.


    Desire to learn the current state of Cloud/AWS and Data Analytics technologies and strategies
    for your business.

  • Schedule
  • Course Outline

    Cloud and AWS

    • Define deployment pipelines with Cloud resources
    • Fit AWS solutions inside a big data ecosystem
    • Describe configuration management, deployment, and provisioning
    • Understand the variety of resources available in AWS
    • Choose appropriate AWS data storage options
    • Define deployment pipelines with Cloud resources

    Data Analytics


    • History of data analytics and data science
    • Defining key terms and understanding the jargon
    • Business applications of data science
    • Current and emerging trends

    Working with Data

    • The different types of data (structured & unstructured)
    • The sources of data (internal & external)
    • Data retention policies & strategies
    • Acquiring the data
    • Data quality
    • Data cleanup, formatting, processing
    • What is Big Data (3 Vs)

    Traditional tools for data analytics

    • Excel
    • Reporting & Query tools
    • CSV Extracts & Feeds
    • SQL Databases
    • Statistical and Mathematical Methods

    New tools for Data Analytics

    • Tableau
    • PowerBI
    • Programming languages (Python and R)
    • NoSQL Databases
    • Open Source tools
    • Machine Learning
    • Cloud Tools & Services

    Larger considerations of Data Analytics

    • Errors and Bias
    • Ethics
    • Security, Privacy & Compliance
    • Interpreting, reporting, and visualizing the results of data analytics
    • Verification and validation of data analytics projects and systems
    • Transitions from project concept to deployed data analytic system
    • Computational and data storage considerations for data analytics
    • Agile approach to Data Analytics
    • Developing a Data Analytics culture

    Case Studies

    Discussion of Your Organization’s Data Analytics Opportunities & Challenges

  • FAQs
    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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