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.
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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.
So let's talk a little bit about the input. We have two basic data types where the data is organized. The quantitative and qualitative. Quantitative refers to numbers, the amounts of certain values, like the number of citizens in a given geographical area. Qualitative data refers to attributes of the population not expressed in direct numbers like eye color, satisfaction levels and so on which qualifies the attribute.
We also have three main classifications for the data format:
- Unstructured data
Structured data refers to data with a defined data model like SQL databases, where tables have a fixed DB model and schema. On AWS for example, the RDS or Relational Database Services is a very complete example of a structured store.
In semi-structured data, we basically have a flexible data model or tagging mechanism that allows a semantic organization and some kind of hierarchy discovery from the data without having the fixed and rigid rules from a SQL database. XML, JSON, and CSV files are good examples of semi-structured data.
Non-SQL databases can also be structured, but usually, they are used in a flexible way to complement the limitations from Amazon S3 to SQL databases. Amazon DynamoDB allows each record to have a different number of columns but gives fixed indexes for searching. This provides a very flexible schema.
Lastly, we have the unstructured data where all kinds of text information without a data model is classified. Here we have all kinds of documents without a proper data model or a schema, like books, natural language processing, and all sorts of text processing.
Data generation has exploded exponentially in the past decade. We generate data from the moment we wake up to the moment we go to bed, and even sleeping, sensors can be collecting data from our body and environment to improve a series of apps and services. It could be suggested that we are generating too much data, much more than we can probably analyze.
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.