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.
The power of analytics grows when we move from batch analytics to real-time analytics and then to predictive analytics, but as always, the problem you are trying to resolve will always dictate the best method.
Batch analytics processes historical data to a job and after a period of time presents the data results. It's commonly used when we're doing reporting or some kind of BI analysis. So we have years of data in your data warehouse or in logs, in spreadsheets and we want to get reporting to correlate this data to try to find interesting patterns like potential sales, potential profits we can get, or potential insights from research data.
This is quite different from real-time analytics where we need to get answers now. You and your application might not be able to wait hours or days to get an answer because if you lose time there can be serious consequences, like for example, fast reaction over security alerts from Intrusion detection systems, or reactions to an ad campaign.
And we also have the last type which is predictive analytics, which takes historical data as an input, it then learns from the history, and then leaves us predictions for future behaviors. This is a common case for machine learning like spam detection where based on past behavior we identify malicious messages, predicting and avoiding spam messages.
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.