What are the differences between exploratory data analysis (EDA) and hypothesis testing in the realm of data mining, and what is the rationale behind analysts' adoption of EDA?

Consider the benefits and drawbacks of different data gathering techniques.

When classifying data, when should we use the Nave Bayes approach, and when should we avoid it?

How does data modeling play a crucial role in the development of big data applications?

Because of its greater efficiency, exploratory data analysis (EDA) is often favoured by analysts over hypothesis testing when it comes to data mining.

What role does statistics play in data management?

Please answer below questions based on this dimensional model and additional information provided     Q1: What is the maximum possible fact data volume in terms of record/row count? Answer:   Q3: The m:n ratio from attribute B3 to attribute B4 is changed from 1:3 to 1:1 due to some organizational changes. By how many times the maximum possible fact data volume (by record count) decreases? Answer: Q4: If there is an aggregate fact table at quarter dimension level with other attributes B4, C5 and A3. What is the maximum possible record count of this aggregate table as per the current state mentioned above? Answer:

What are the four advantages of using a data flow approach over narrative explanations of data movement?

Explain how the Data Science Methodology (DSM) is adaptive and its Advantages?

In what ways can data modelling aid in the analysis process? Can we estimate how much information will be required for our model?