Today, we are all surrounded with full of data.
Data can be in the form of structured data(eg: Tables, worksheets), or unstructured data (free text fields or comments from social media).
An example of Data Usage is in AI model.
Data are the core of an AI model, which utilizes data input for the model to train, test, and learn from the data.
The usage of Machine Learning has allowed computers to perform predictions and provides suggestions to humans based on the data input that has been fed into the machine.
An example of Machine Learning is the Web Search Engine, which tries to understand what is the content we are searching for based on the data input that has been entered into the Search Engine.
Data is everywhere but if we do not transform the data into useful insight, we are not able to make the correct decision on a certain matter.
Imagine if we are recently appointed to be a director of an organization, are we able to:
1. React based on the latest or historical information that is happening around us?
2. Reduce or automate the manual works that have been done for decades?
3. Draw insights from a stack of printed reports?
If you do not have the answer, join this class to know more about Qlik Sense which can help solve your problems.
In the Intermediate session, you will be exposed to the load script where we load our data into Qlik Sense.
And also did some of the basic transformation.
Once we have all the tables transformed and saved into qvd formats, we will load all the qvds back into load script.
And finally, we will load the data model into our final application, which is the dashboard layer.
Next, we will go into the Sheets to expose ourselves with visualization expressions, how to create buttons and also the idea of having master dimension, master expressions and master visualizations in the dashboard.
For the Advanced session, we will guide you on how to build our load scripts with Master Calendar and Fiscal Calendar, YTD tables and so on.
Once the load script is completed, we will move on to the sheets to know how do we apply the flags created in the load script into the visualization expression.
A more simplified way to create MTD/YTD logic will be introduced at this session as well.
Functions such as Rank and AGGR are also introduced in the advanced session.
Together with all the knowledge we learned so far, we will create 100% chart which will be an interesting chart to showcase the distribution of the specific dimensions that we are interested of.
Thanks for enrolling and Happy Qliking!