Data Analytics Fundamentals

Course Modality

Instructor-led (classroom)

Course Level

Fundamental

Course Time

3.5 Hours

Course Language

English

Course Overview

In this self-paced course, you learn about the process for planning data analysis solutions and the various data analytic processes that are involved. This course takes you through five key factors that indicate the need for specific AWS services in collecting, processing, analyzing, and presenting your data. This includes learning basic architectures, value propositions, and potential use cases. The course introduces you to the AWS services and solutions to help you build and enhance data

Prerequisites

We recommend that attendees of this course have the following prerequisites:

  • Working knowledge of database concepts
  • Basic understanding of data storage, processing, and analytics
  • Experience with enterprise IT systems

Intended Audience

This course is intended for:

  • Data architects
  • Data scientists
  • Data analysts

Why The DataTech Labs ?

Self-Paced Online Video

A 360-degree learning approach that you can adapt to your learning style

A 360-degree learning approach that you can adapt to your learning style

Engage and learn more with these live and highly-interactive classes alongside your peers

24/7 Teaching Assistance

24/7 Teaching Assistance Keep engaged with integrated teaching

Online Practice Labs

Projects provide you with sample work to show prospective employers.

Applied Projects

Real-world projects relevant to what you’re learning throughout the program

Learner Social Forums

A support team focused on helping you succeed alongside a peer community

Skill Covered

  • Identify the characteristics of data analysis solutions and the characteristics that indicate such a solution may be required
  • Define types of data including structured, semistructured, and unstructured data
  • Define data storage types such as data lakes, AWS Lake Formation, data warehouses, and the Amazon Simple Storage Service (Amazon S3)
  • Analyze the characteristics of and differences in batch and stream processing
  • Define how Amazon Kinesis is used to process streaming data
  • Analyze the characteristics of different storage systems for source data
  • Analyze the characteristics of online transaction processing (OLTP) and online     analytical processing (OLAP) systems and their impact on the organization of data within   these systems
  • Analyze the differences of row-based and columnar data storage methods
  • Define how Amazon EMR, AWS Glue, and Amazon Redshift each work to process, cleanse, and transform data within a data analysis solution
  • Analyze the concept of atomicity, consistency, isolation, and durability (ACID) compliance as well as basic availability, soft state, eventual consistency (BASE) compliance and how an extract,
  • Transform, load (ETL) process can help to ensure compliance
  • Explore the concept of data schemas and understand how they define data and how this information is stored in metastores
  • Analyze the concept of data versus information
  • Recognize the ways to analyze data to produce information for reports using tools such as Amazon QuickSight and Amazon Athena
  • 9 Define how AWS services work together to visualize data

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Course Curriculum

Introduction to data analysis solutions

  • Data analytics and data analysis concepts
  • Introduction to the challenges of data analytics

Volume – data storage

  • Introduction to Amazon S3
  • Introduction to data lakes
  • Introduction to data storage methods

Velocity – data processing

  • Introduction to data processing methods
  • Introduction to batch data processing
  • Introduction to stream data processing

Variety – data structure and types

  • Introduction to source data storage
  • Introduction to structured data stores
  • Introduction to semi structured and unstructured data stores

Veracity – cleansing and transformation

  • Understanding data integrity
  • Understanding database consistency
  • Introduction to the ETL process

Value – reporting and business intelligence

  • Introduction to analyzing data
  • Introduction to visualizing data

Key Takeaways

  • Putting the pieces together

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