Introduction to Data Visualization
The course is part of this learning path
This course explores data sources and formatting, and how to present data in a way that provides meaningful information. You'll look at data access patterns, and how different interfaces allow you to access the underlying information. This course also provides a practical, real-world example of how all this theory plays out in a business scenario. By the end of this course, you will have a good foundational understanding of how to wrangle and visualize data.
If you have any feedback relating to this course, feel free to reach out to us at firstname.lastname@example.org.
- Understand the difference between data and information
- Learn how to make data useful in order to gain insights from it
- Learn how to store data correctly
- Understand how these techniques can be applied in the business world
This course is ideal for anyone who is required to interpret or understand data for reporting purposes or for use in machine learning initiatives.
To get the most out of this course, you should be familiar with relational databases such as SQL or NoSQL and some common data formats such as CSV and JSON.
Welcome to visualizing your data, a class in which we discuss how you could format your information, data sources, and frankly data in order to best dashboard it and drive reporting. Hopefully by the end of this course, you'll have a practical understanding of what it takes to organize your data source, be it a database or a file in order to dashboard it and make easy to understand visualizations and insights.
Additionally, you'll develop the ability to communicate data structures for an end application. This means understanding data access patterns, and how different interfaces hope to access the underlying information. And finally, like all classes in the data engineering learning path, we will be providing practical hands-on examples of what it takes to wrangle and visualize data sources. So you can see how the theory applies in a very direct sense. And before we get started, there are a few prerequisites.
We recommend that you have a familiarity with relational databases such as SQL or NoSQL and some common data formats such as CSV and JSON. Furthermore, understanding how the SQL query language works and a high level understanding of what a business intelligent dashboard looks like and how it operates. If you're unfamiliar with these, check out the other courses in the data engineering learning path, where we cover them or check out Cloud Academy's broader content library, where they cover these topics at depth.
And finally, before we get started, if you're still on the fence about who should attend, this class is ideal for people who are required to interpret or understand data, especially if you're making reports made for use in the broader company or class project, or if you're responsible for ongoing machine learning support and other data initiatives.
Calculated Systems was founded by experts in Hadoop, Google Cloud and AWS. Calculated Systems enables code-free capture, mapping and transformation of data in the cloud based on Apache NiFi, an open source project originally developed within the NSA. Calculated Systems accelerates time to market for new innovations while maintaining data integrity. With cloud automation tools, deep industry expertise, and experience productionalizing workloads development cycles are cut down to a fraction of their normal time. The ability to quickly develop large scale data ingestion and processing decreases the risk companies face in long development cycles. Calculated Systems is one of the industry leaders in Big Data transformation and education of these complex technologies.