Big Data Analytics
Introduction to Cloud, AWS and Data Analytics
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.
COURSE DETAILS:
Date: 9/17/19 – 9/19/19
Time: 9:00am – 4:30pm
Days: Tuesday – Thursday
Location: Columbia
Price: $1235.00
AUDIENCE
This course is suitable for: a general audience including business people, managers, system
architects/engineers, business/systems analysts, and data scientists.
PREREQUISITES
Desire to learn the current state of Cloud/AWS and Data Analytics technologies and strategies
for your business.
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
Background
• 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
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.