Practical SAS Visual Analytics
- Basic background in statistics and charts
Practical SAS Visual Analytics is aimed to show examples of data visualization using SAS Visual Analytics, which is one of the leading software in the graphical analytics marketshare. To try this software requires no installation, no configuration and no previous experience. SAS Visual Analytics on SAS Viya is a cloud hosted proprietary software that requires a license to be used, since may 2019 you might be able to access it for free as an independent learner in SAS cloud servers. SAS Visual Analytics works on Linux Servers called LASR servers, it is compiled to handle massive amounts of data distributed across computer clusters. You can access to SAS VA through your web browser. The course will not have a theoretical approach to the statistical background of the charts exhibited along the videos but it it will be more a practical discussion on how-to carry out statistical charts.
Who this course is for:
- You should take this course if you have previous experience with data analysis
- This course can be enrolled by students wanting to learn SAS Visual Analytics
- This course is also made for people with prior data visualization experience
- 02:48Introduction to SAS Visual Analytics Environment
- 03:11Bar Charts in SAS Visual Analytics
- 04:01Pie Charts
- 02:56Step Plots
- 03:43Heat Maps
- 04:29Correlation Matrix
- 02:51Word Clouds
- 08:37Time Series in SAS Visual Analytics
- 02:36Gauge Charts
- 05:20Line Charts
- 03:17Box Plot
- 03:15Butterfly Chart
I am a machine learning engineer and a huge part of my career has been involved in commercial banking, insurance and retail industries. In recent years there has been a revolution in the way business are making their decisions. With the growth and the capability to store and process massive amounts of data and aided with the development of ML algorithms we have discover that strategies can be smarter if based on data.
Basically my job is to develop, assess and mantain ML models from countless source of data stored in computer clusters and then propose conclusions based on the output of the algorithm.
I hope the courses in this platform help you to find the motivation and the knowledge required to succeed in your work.