Understanding Bayesian statistics is really important for modern data scientists for a few key reasons:

1. What is Bayesian Inference?
   Bayesian statistics helps us update our beliefs based on new information. This is crucial in situations where what we already know can affect our decisions. For example, Bayes' theorem shows how to update probabilities. It can be written as:

   $$P(H|E) = \frac{P(E|H) \cdot P(H)}{P(E)}$$

   Here, $P(H|E)$ means the updated probability after seeing new evidence, $P(E|H)$ shows how likely we would see that evidence if our hypothesis is true, $P(H)$ is what we believed before seeing the evidence, and $P(E)$ helps keep everything balanced.

2. Using What We Already Know:
   Unlike other methods that focus only on long-term results, Bayesian methods let us use previous knowledge. This means data scientists can add their understanding from past experiences, which can really improve their models, especially when they don't have much data.

3. Making Decisions When Things Are Uncertain:
   Bayesian statistics gives tools to understand uncertainty and make better choices. One example is using credible intervals. For instance, if we talk about a 95% credible interval, it means we are 95% sure that a certain value falls within a specific range.

4. Comparing Different Models:
   With Bayesian methods, data scientists can easily compare different models. They use Bayes Factors, which help measure how much stronger one model is compared to another.

5. Wide Use in Many Fields:
   Bayesian statistics is useful in many areas like healthcare, finance, and machine learning. This makes it a powerful tool for data scientists.

In short, knowing Bayesian statistics allows data scientists to handle tricky problems using a strong method that adapts as they get new information.