This is part of a series on my preparation for the DP-900 exam. This is the Microsoft Azure Data Fundamentals, part of a number of certification paths. You can read various posts I’ve created as part of this learning experience.
I didn’t think much of this bullet on the DP-900 skills document: describe analytics techniques.
Learn these concepts. They are definitely part of the knowledge needed. This is a part of some MS Learn courses, like this one.
Descriptive analytics have to do with describing what the data shows. In a report or visualization, we are reporting data to the user. An example of this is showing the top 10 products according to sales.
These are explanatory analytics and think about that in the test. Are we explaining something.
A diagnosis is an explanation of why something happened. Diagnostic analytics help explain the reason that something occurred. We might look at the top 10 sales against advertising for products and determine that more advertising shows higher sales.
In the link above from MS Learn, what we typically do here is identify some anomaly or thing we want to explain, get data related to this, and then use some statistics to find a relationship.
As you might guess, this type of analytics relate to the future. We are looking to predict what will happen. An example might be forecasting future demand for services.
When you see a request to predict, forecast or extrapolate, think predictive analytics.
This type of analysis was a little harder for me to understand or grasp. I get the general idea, but in prep I missed this one a few times. Prescriptive analytics are developing a prescription for how to change something.
An example might be if I want to find where to cut costs to impact profit, I am looking for a set of things to change. I need a prescription on what will help me achieve my goal.
In relation to the exam, this has to do with aiming for a goal (10% more or 12 less) and how to reach that goal. If you see specific numbers, usually that relates to a goal and prescriptive analytics.
This type of analysis has to do with thinking. In data analysis and computing, usually I think ML, AI, or some other type of data mining. This can be deriving conclusions or inferences from data, but the blend between this and the descriptive/diagnostic stuff becomes blurry.
If you need to do speech to text or video transcription or image recognition, think cognitive.
This is how I viewed this techniques on the exam when answering questions:
– What will happen
– What do I do
- Cognitive – Learning about data in new ways