Data Analytic Concepts
The course is part of this learning path
This course explores the different concepts behind data analytics. It will provide you with a clearer understanding of what data analytics actually is and how it allows you to collate, store, review, and analyze data to help drive business decisions through insights that have been identified.
If you have any feedback relating to this course, please contact us at firstname.lastname@example.org.
By the end of this course, you will have an understanding of:
- Different analytic concepts
- Data types, including structured, semi-structured, and unstructured data
- When you should use data analytics within your business
- The process behind running analytics against data
This course is ideal for those looking to become data scientists or solutions architects. Also, if you are studying for the AWS Data Analytics - Specialty certification, then this course would act as a great introduction to the topic itself.
As this is a beginner's course, all concepts will be explained throughout the course. Any knowledge of AWS data analytic services would be advantageous, but not essential.
To help us define if we need to use data analytics services to find the answers to our questions or problems that are locked within our data we can look at the following factors
Volume: The first is the volume, which refers to the size of the data set, or as we usually call it the data set size. And size matters in order to decide the right tool to analyze it. Usually, a big data problem will go into scale from gigabytes to petabytes of data.
Velocity: We have also what we call the velocity of the data which means how quick you need to get answers for your data and is also related to the age of your data. Like for example, historical records from past years, or real-time alerting and information. This has a great influence in the toolset used to analyze it, as depending on the need for answers, is real-time or a waiting time acceptable for you? Knowing this, we can decide which tool and which technique is the best for the problem.
Variety: This refers to the variety of the source data classification; if it’s structured or not structured. As often analytic problems will have sources from several types, like Business intelligence platform data, blogs, CSV data, text, and any sort of structured or unstructured data.
Stuart has been working within the IT industry for two decades covering a huge range of topic areas and technologies, from data center and network infrastructure design, to cloud architecture and implementation.
To date, Stuart has created 150+ courses relating to Cloud reaching over 180,000 students, mostly within the AWS category and with a heavy focus on security and compliance.
Stuart is a member of the AWS Community Builders Program for his contributions towards AWS.
He is AWS certified and accredited in addition to being a published author covering topics across the AWS landscape.
In January 2016 Stuart was awarded ‘Expert of the Year Award 2015’ from Experts Exchange for his knowledge share within cloud services to the community.
Stuart enjoys writing about cloud technologies and you will find many of his articles within our blog pages.